From f08db4ab2460b5942121af8bb77dc91ca44e0f67 Mon Sep 17 00:00:00 2001
From: MiXaiLL76 <mike.milos@yandex.ru>
Date: Wed, 20 Mar 2024 16:12:45 +0300
Subject: [PATCH 01/15] move draw function

---
 examples/curve_example.ipynb      |  28 +--
 examples/eval_example.ipynb       |   2 +-
 faster_coco_eval/extra/curves.py  |  48 +----
 faster_coco_eval/extra/display.py | 248 +++---------------------
 faster_coco_eval/extra/draw.py    | 307 ++++++++++++++++++++++++++++++
 faster_coco_eval/version.py       |   2 +-
 6 files changed, 348 insertions(+), 287 deletions(-)
 create mode 100644 faster_coco_eval/extra/draw.py

diff --git a/examples/curve_example.ipynb b/examples/curve_example.ipynb
index 2ff820b..ef31e82 100644
--- a/examples/curve_example.ipynb
+++ b/examples/curve_example.ipynb
@@ -10,7 +10,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "faster_coco_eval.__version__='1.4.0'\n"
+      "faster_coco_eval.__version__='1.5.0'\n"
      ]
     }
    ],
@@ -1258,9 +1258,9 @@
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       },
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{\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"range\":[0,256]},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"range\":[256,0]},\"margin\":{\"t\":60},\"title\":{\"text\":\"image_id=3\\u003cbr\\u003eimage_fn=img_2.jpg\"},\"autosize\":true,\"height\":700,\"width\":900},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('d1a51e91-4084-41a0-8f30-2b4fd3942292');\n",
+       "var gd = document.getElementById('bbca250c-2a36-46dc-83a0-bf51640f93d0');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
diff --git a/examples/eval_example.ipynb b/examples/eval_example.ipynb
index 4d04941..b930b6a 100644
--- a/examples/eval_example.ipynb
+++ b/examples/eval_example.ipynb
@@ -10,7 +10,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "faster_coco_eval.__version__='1.4.2'\n"
+      "faster_coco_eval.__version__='1.5.0'\n"
      ]
     }
    ],
diff --git a/faster_coco_eval/extra/curves.py b/faster_coco_eval/extra/curves.py
index 4a32e21..9a97f04 100644
--- a/faster_coco_eval/extra/curves.py
+++ b/faster_coco_eval/extra/curves.py
@@ -1,7 +1,7 @@
 import logging
-import plotly.graph_objects as go
 
 from ..core import COCOeval_faster
+from .draw import plot_pre_rec
 from .extra import ExtraEval
 
 logger = logging.getLogger(__name__)
@@ -51,48 +51,4 @@ def plot_pre_rec(
         if curves is None:
             curves = self.build_curve(label)
 
-        fig = go.Figure()
-
-        for _curve in curves:
-            recall_list = _curve["recall_list"]
-            precision_list = _curve["precision_list"]
-            scores = _curve["scores"]
-            name = _curve["name"]
-
-            fig.add_trace(
-                go.Scatter(
-                    x=recall_list,
-                    y=precision_list,
-                    name=name,
-                    text=scores,
-                    hovertemplate="Pre: %{y:.3f}<br>"
-                    + "Rec: %{x:.3f}<br>"
-                    + "Score: %{text:.3f}<extra></extra>",
-                    showlegend=True,
-                    mode="lines",
-                )
-            )
-
-        margin = 0.01
-        fig.layout.yaxis.range = [0 - margin, 1 + margin]
-        fig.layout.xaxis.range = [0 - margin, 1 + margin]
-
-        fig.layout.yaxis.title = "Precision"
-        fig.layout.xaxis.title = "Recall"
-
-        fig.update_xaxes(showspikes=True)
-        fig.update_yaxes(showspikes=True)
-
-        layout = {
-            "title": "Precision-Recall",
-            "autosize": True,
-            "height": 600,
-            "width": 1200,
-        }
-
-        fig.update_layout(layout)
-
-        if return_fig:
-            return fig
-
-        fig.show()
+        return plot_pre_rec(curves, return_fig=return_fig)
diff --git a/faster_coco_eval/extra/display.py b/faster_coco_eval/extra/display.py
index ccd27bb..cfdf9e2 100644
--- a/faster_coco_eval/extra/display.py
+++ b/faster_coco_eval/extra/display.py
@@ -1,190 +1,39 @@
 import logging
 import numpy as np
-import os.path as osp
-import plotly.express as px
-import plotly.graph_objs as go
-from PIL import Image
+from typing import Optional
 
+from .draw import display_image, display_matrix
 from .extra import ExtraEval
 
 logger = logging.getLogger(__name__)
 
 
 class PreviewResults(ExtraEval):
-    A = 0.1
-    DT_COLOR = (238, 130, 238, A)
-
-    GT_COLOR = (0, 255, 0, A)
-    FN_COLOR = (0, 0, 255, A)
-    FP_COLOR = (255, 0, 0, A)
-
-    def get_ann_poly(self, ann, color, text=None, legendgroup=None):
-        all_x = []
-        all_y = []
-
-        if self.iouType == "bbox":
-            x1, y1, w, h = ann["bbox"]
-            all_x = [x1, x1 + w, x1 + w, x1, x1, None]
-            all_y = [y1, y1, y1 + h, y1 + h, y1, None]
-        else:
-            for poly in ann["segmentation"]:
-                if len(poly) > 3:
-                    poly += poly[:2]
-                    poly = np.array(poly).reshape(-1, 2)
-                    all_x += poly[:, 0].tolist() + [None]
-                    all_y += poly[:, 1].tolist() + [None]
-
-        return go.Scatter(
-            x=all_x,
-            y=all_y,
-            name="",
-            text=text,
-            hovertemplate="{text}<extra></extra>",
-            mode="lines",
-            legendgroup=legendgroup,
-            legendgrouptitle_text=legendgroup,
-            showlegend=False,
-            fill="toself",
-            fillcolor="rgba{}".format(color),
-            line=dict(color="rgb{}".format(color[:3])),
-        )
-
     def display_image(
         self,
-        image_id=1,
-        display_fp=True,
-        display_fn=True,
-        display_tp=True,
-        display_gt=True,
-        data_folder=None,
-        categories=None,
+        image_id: int = 1,
+        display_fp: bool = True,
+        display_fn: bool = True,
+        display_tp: bool = True,
+        display_gt: bool = True,
+        data_folder: Optional[str] = None,
+        categories: Optional[list] = None,
         return_fig: bool = False,
     ):
-        polygons = []
-
-        image = self.cocoGt.imgs[image_id]
-        gt_anns = {ann["id"]: ann for ann in self.cocoGt.imgToAnns[image_id]}
-
-        if self.cocoDt is not None:
-            dt_anns = {
-                ann["id"]: ann for ann in self.cocoDt.imgToAnns[image_id]
-            }
-        else:
-            dt_anns = {}
-
-        image_fn = image["file_name"]
-        if data_folder is not None:
-            image_load_path = osp.join(data_folder, image["file_name"])
-        else:
-            image_load_path = image["file_name"]
-
-        if osp.exists(image_load_path):
-            im = Image.open(image_load_path).convert("RGB")
-        else:
-            logger.warning(
-                "[{}] not found!\nLoading default empty image".format(
-                    image_load_path
-                )
-            )
-
-            im = Image.new("RGB", (image["width"], image["height"]))
-
-        categories_labels = {
-            category["id"]: category["name"]
-            for _, category in self.cocoGt.cats.items()
-        }
-
-        if len(gt_anns) > 0:
-            for ann in gt_anns.values():
-                if categories is None or ann["category_id"] in categories:
-                    if ann.get("fn", False):
-                        if display_fn:
-                            poly = self.get_ann_poly(
-                                ann,
-                                color=self.FN_COLOR,
-                                text="<b>FN</b><br>id={}<br>category={}".format(
-                                    ann["id"],
-                                    categories_labels[ann["category_id"]],
-                                ),
-                                legendgroup="fn",
-                            )
-                            polygons.append(poly)
-                    else:
-                        if display_gt:
-                            poly = self.get_ann_poly(
-                                ann,
-                                color=self.GT_COLOR,
-                                text="<b>GT</b><br>id={}<br>category={}".format(
-                                    ann["id"],
-                                    categories_labels[ann["category_id"]],
-                                ),
-                                legendgroup="gt",
-                            )
-                            polygons.append(poly)
-
-        if len(dt_anns) > 0:
-            for ann in dt_anns.values():
-                if categories is None or ann["category_id"] in categories:
-                    if ann.get("tp", False):
-                        if display_tp:
-                            poly = self.get_ann_poly(
-                                ann,
-                                color=self.DT_COLOR,
-                                text="<b>DT</b><br>id={}<br>category={}<br>score={:.2f}<br>IoU={:.2f}".format(
-                                    ann["id"],
-                                    categories_labels[ann["category_id"]],
-                                    ann["score"],
-                                    ann["iou"],
-                                ),
-                                legendgroup="tp",
-                            )
-                            polygons.append(poly)
-                    else:
-                        if display_fp:
-                            poly = self.get_ann_poly(
-                                ann,
-                                color=self.FP_COLOR,
-                                text="<b>FP</b><br>id={}<br>category={}<br>score={:.2f}".format(
-                                    ann["id"],
-                                    categories_labels[ann["category_id"]],
-                                    ann["score"],
-                                ),
-                                legendgroup="fp",
-                            )
-                            polygons.append(poly)
-
-        fig = px.imshow(
-            im,
-            binary_compression_level=5,
-            binary_format="jpg",
-            aspect="auto",
-            labels=dict(animation_frame="shown picture"),
+        return display_image(
+            self.cocoGt,
+            self.cocoDt,
+            image_id=image_id,
+            iouType=self.iouType,
+            display_fp=display_fp,
+            display_fn=display_fn,
+            display_tp=display_tp,
+            display_gt=display_gt,
+            data_folder=data_folder,
+            categories=categories,
+            return_fig=return_fig,
         )
 
-        legends = {}
-        for poly in polygons:
-            if legends.get(poly.legendgroup) is None:
-                poly.showlegend = True
-                legends[poly.legendgroup] = True
-
-            fig.add_trace(poly)
-
-        layout = {
-            "title": "image_id={}<br>image_fn={}".format(image_id, image_fn),
-            "autosize": True,
-            "height": 700,
-            "width": 900,
-        }
-
-        fig.update_layout(layout)
-        fig.update_xaxes(range=[0, image["width"]])
-        fig.update_yaxes(range=[image["height"], 0])
-
-        if return_fig:
-            return fig
-
-        fig.show()
-
     def display_tp_fp_fn(
         self,
         image_ids=["all"],
@@ -273,58 +122,7 @@ def display_matrix(
             conf_matrix = self.compute_confusion_matrix()
 
         labels = [category["name"] for _, category in self.cocoGt.cats.items()]
-        labels += ["fp", "fn"]
-
-        if in_percent:
-            conf_matrix /= conf_matrix.sum(axis=1).reshape(-1, 1)
-            conf_matrix *= 100
 
-        hovertemplate = "Real: %{y}<br>" "Predict: %{x}<br>"
-
-        if in_percent:
-            hovertemplate += "Percent: %{z:.0f}<extra></extra>"
-        else:
-            hovertemplate += "Count: %{z:.0f}<extra></extra>"
-
-        heatmap = go.Heatmap(
-            z=conf_matrix,
-            x=labels,
-            y=labels[:-2],
-            colorscale="Blues",
-            hovertemplate=hovertemplate,
+        return display_matrix(
+            conf_matrix, labels, in_percent=in_percent, return_fig=return_fig
         )
-
-        annotations = []
-        for j, row in enumerate(conf_matrix):
-            for i, value in enumerate(row):
-                text_value = "{:.0f}".format(value)
-                if in_percent:
-                    text_value += "%"
-
-                annotations.append(
-                    {
-                        "x": labels[i],
-                        "y": labels[j],
-                        "font": {"color": "white"},
-                        "text": text_value,
-                        "xref": "x1",
-                        "yref": "y1",
-                        "showarrow": False,
-                    }
-                )
-
-        layout = {
-            "title": "Confusion Matrix",
-            "xaxis": {"title": "Predicted value"},
-            "yaxis": {"title": "Real value"},
-            "annotations": annotations,
-        }
-
-        fig = go.Figure(data=[heatmap], layout=layout)
-        fig.update_traces(showscale=False)
-        fig.update_layout(height=700, width=900)
-
-        if return_fig:
-            return fig
-
-        fig.show()
diff --git a/faster_coco_eval/extra/draw.py b/faster_coco_eval/extra/draw.py
new file mode 100644
index 0000000..e27b190
--- /dev/null
+++ b/faster_coco_eval/extra/draw.py
@@ -0,0 +1,307 @@
+import logging
+import numpy as np
+import os.path as osp
+import plotly.express as px
+import plotly.graph_objs as go
+from PIL import Image
+from typing import Optional
+
+from ..core import COCO
+
+logger = logging.getLogger(__name__)
+
+A = 0.1
+DT_COLOR = (238, 130, 238, A)
+
+GT_COLOR = (0, 255, 0, A)
+FN_COLOR = (0, 0, 255, A)
+FP_COLOR = (255, 0, 0, A)
+
+
+def generate_ann_polygon(
+    ann: dict,
+    color: tuple,
+    iouType: str = "bbox",
+    text: Optional[str] = None,
+    legendgroup: Optional[str] = None,
+):
+    all_x = []
+    all_y = []
+
+    if iouType == "bbox":
+        x1, y1, w, h = ann["bbox"]
+        all_x = [x1, x1 + w, x1 + w, x1, x1, None]
+        all_y = [y1, y1, y1 + h, y1 + h, y1, None]
+    else:
+        for poly in ann["segmentation"]:
+            if len(poly) > 3:
+                poly += poly[:2]
+                poly = np.array(poly).reshape(-1, 2)
+                all_x += poly[:, 0].tolist() + [None]
+                all_y += poly[:, 1].tolist() + [None]
+
+    return go.Scatter(
+        x=all_x,
+        y=all_y,
+        name="",
+        text=text,
+        hovertemplate="{text}<extra></extra>",
+        mode="lines",
+        legendgroup=legendgroup,
+        legendgrouptitle_text=legendgroup,
+        showlegend=False,
+        fill="toself",
+        fillcolor="rgba{}".format(color),
+        line=dict(color="rgb{}".format(color[:3])),
+    )
+
+
+def display_image(
+    cocoGt: COCO,
+    cocoDt: Optional[COCO] = None,
+    image_id: int = 1,
+    iouType: Optional[str] = "bbox",
+    display_fp: bool = True,
+    display_fn: bool = True,
+    display_tp: bool = True,
+    display_gt: bool = True,
+    data_folder: Optional[str] = None,
+    categories: Optional[list] = None,
+    return_fig: bool = False,
+):
+    polygons = []
+
+    image = cocoGt.imgs[image_id]
+    gt_anns = {ann["id"]: ann for ann in cocoGt.imgToAnns[image_id]}
+
+    if cocoDt is not None:
+        dt_anns = {ann["id"]: ann for ann in cocoDt.imgToAnns[image_id]}
+    else:
+        dt_anns = {}
+
+    image_fn = image["file_name"]
+    if data_folder is not None:
+        image_load_path = osp.join(data_folder, image["file_name"])
+    else:
+        image_load_path = image["file_name"]
+
+    if osp.exists(image_load_path):
+        im = Image.open(image_load_path).convert("RGB")
+    else:
+        logger.warning(
+            "[{}] not found!\nLoading default empty image".format(
+                image_load_path
+            )
+        )
+
+        im = Image.new("RGB", (image["width"], image["height"]))
+
+    categories_labels = {
+        category["id"]: category["name"] for _, category in cocoGt.cats.items()
+    }
+
+    if len(gt_anns) > 0:
+        for ann in gt_anns.values():
+            if categories is None or ann["category_id"] in categories:
+                if ann.get("fn", False):
+                    if display_fn:
+                        poly = generate_ann_polygon(
+                            ann,
+                            color=FN_COLOR,
+                            iouType=iouType,
+                            text="<b>FN</b><br>id={}<br>category={}".format(
+                                ann["id"],
+                                categories_labels[ann["category_id"]],
+                            ),
+                            legendgroup="fn",
+                        )
+                        polygons.append(poly)
+                else:
+                    if display_gt:
+                        poly = generate_ann_polygon(
+                            ann,
+                            color=GT_COLOR,
+                            iouType=iouType,
+                            text="<b>GT</b><br>id={}<br>category={}".format(
+                                ann["id"],
+                                categories_labels[ann["category_id"]],
+                            ),
+                            legendgroup="gt",
+                        )
+                        polygons.append(poly)
+
+    if len(dt_anns) > 0:
+        for ann in dt_anns.values():
+            if categories is None or ann["category_id"] in categories:
+                if ann.get("tp", False):
+                    if display_tp:
+                        poly = generate_ann_polygon(
+                            ann,
+                            color=DT_COLOR,
+                            iouType=iouType,
+                            text="<b>DT</b><br>id={}<br>category={}<br>score={:.2f}<br>IoU={:.2f}".format(
+                                ann["id"],
+                                categories_labels[ann["category_id"]],
+                                ann["score"],
+                                ann["iou"],
+                            ),
+                            legendgroup="tp",
+                        )
+                        polygons.append(poly)
+                else:
+                    if display_fp:
+                        poly = generate_ann_polygon(
+                            ann,
+                            color=FP_COLOR,
+                            iouType=iouType,
+                            text="<b>FP</b><br>id={}<br>category={}<br>score={:.2f}".format(
+                                ann["id"],
+                                categories_labels[ann["category_id"]],
+                                ann["score"],
+                            ),
+                            legendgroup="fp",
+                        )
+                        polygons.append(poly)
+
+    fig = px.imshow(
+        im,
+        binary_compression_level=5,
+        binary_format="jpg",
+        aspect="auto",
+        labels=dict(animation_frame="shown picture"),
+    )
+
+    legends = {}
+    for poly in polygons:
+        if legends.get(poly.legendgroup) is None:
+            poly.showlegend = True
+            legends[poly.legendgroup] = True
+
+        fig.add_trace(poly)
+
+    layout = {
+        "title": "image_id={}<br>image_fn={}".format(image_id, image_fn),
+        "autosize": True,
+        "height": 700,
+        "width": 900,
+    }
+
+    fig.update_layout(layout)
+    fig.update_xaxes(range=[0, image["width"]])
+    fig.update_yaxes(range=[image["height"], 0])
+
+    if return_fig:
+        return fig
+
+    fig.show()
+
+
+def display_matrix(
+    conf_matrix: np.ndarray,
+    labels: list,
+    in_percent: bool = False,
+    return_fig: bool = False,
+):
+    _labels = labels + ["fp", "fn"]
+
+    if in_percent:
+        conf_matrix /= conf_matrix.sum(axis=1).reshape(-1, 1)
+        conf_matrix *= 100
+
+    hovertemplate = "Real: %{y}<br>" "Predict: %{x}<br>"
+
+    if in_percent:
+        hovertemplate += "Percent: %{z:.0f}<extra></extra>"
+    else:
+        hovertemplate += "Count: %{z:.0f}<extra></extra>"
+
+    heatmap = go.Heatmap(
+        z=conf_matrix,
+        x=_labels,
+        y=_labels[:-2],
+        colorscale="Blues",
+        hovertemplate=hovertemplate,
+    )
+
+    annotations = []
+    for j, row in enumerate(conf_matrix):
+        for i, value in enumerate(row):
+            text_value = "{:.0f}".format(value)
+            if in_percent:
+                text_value += "%"
+
+            annotations.append(
+                {
+                    "x": _labels[i],
+                    "y": _labels[j],
+                    "font": {"color": "white"},
+                    "text": text_value,
+                    "xref": "x1",
+                    "yref": "y1",
+                    "showarrow": False,
+                }
+            )
+
+    layout = {
+        "title": "Confusion Matrix",
+        "xaxis": {"title": "Predicted value"},
+        "yaxis": {"title": "Real value"},
+        "annotations": annotations,
+    }
+
+    fig = go.Figure(data=[heatmap], layout=layout)
+    fig.update_traces(showscale=False)
+    fig.update_layout(height=700, width=900)
+
+    if return_fig:
+        return fig
+
+    fig.show()
+
+
+def plot_pre_rec(curves, return_fig: bool = False):
+    fig = go.Figure()
+
+    for _curve in curves:
+        recall_list = _curve["recall_list"]
+        precision_list = _curve["precision_list"]
+        scores = _curve["scores"]
+        name = _curve["name"]
+
+        fig.add_trace(
+            go.Scatter(
+                x=recall_list,
+                y=precision_list,
+                name=name,
+                text=scores,
+                hovertemplate="Pre: %{y:.3f}<br>"
+                + "Rec: %{x:.3f}<br>"
+                + "Score: %{text:.3f}<extra></extra>",
+                showlegend=True,
+                mode="lines",
+            )
+        )
+
+    margin = 0.01
+    fig.layout.yaxis.range = [0 - margin, 1 + margin]
+    fig.layout.xaxis.range = [0 - margin, 1 + margin]
+
+    fig.layout.yaxis.title = "Precision"
+    fig.layout.xaxis.title = "Recall"
+
+    fig.update_xaxes(showspikes=True)
+    fig.update_yaxes(showspikes=True)
+
+    layout = {
+        "title": "Precision-Recall",
+        "autosize": True,
+        "height": 600,
+        "width": 1200,
+    }
+
+    fig.update_layout(layout)
+
+    if return_fig:
+        return fig
+
+    fig.show()
diff --git a/faster_coco_eval/version.py b/faster_coco_eval/version.py
index f59b411..35fc040 100644
--- a/faster_coco_eval/version.py
+++ b/faster_coco_eval/version.py
@@ -1,2 +1,2 @@
-__version__ = "1.4.3"
+__version__ = "1.5.0"
 __author__ = "MiXaiLL76"

From 53a17a0952ae9c44b6b89b843e9104dd3cdf621d Mon Sep 17 00:00:00 2001
From: MiXaiLL76 <mike.milos@yandex.ru>
Date: Fri, 12 Apr 2024 12:40:44 +0300
Subject: [PATCH 02/15] append f1

---
 examples/curve_example.ipynb     | 1119 +++++++++++++++++++++++++++++-
 faster_coco_eval/extra/curves.py |   11 +-
 faster_coco_eval/version.py      |    2 +-
 3 files changed, 1119 insertions(+), 13 deletions(-)

diff --git a/examples/curve_example.ipynb b/examples/curve_example.ipynb
index ef31e82..256c2ad 100644
--- a/examples/curve_example.ipynb
+++ b/examples/curve_example.ipynb
@@ -1258,9 +1258,1105 @@
        }
       },
       "text/html": [
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@@ -1297,7 +2393,8 @@
     "cocoDt = cocoGt.loadRes(prepared_anns)\n",
     "\n",
     "cur = Curves(cocoGt, cocoDt, iou_tresh=threshold_iou, iouType=iouType)\n",
-    "cur.plot_pre_rec()"
+    "cur.plot_pre_rec()\n",
+    "cur.plot_f1_confidence()"
    ]
   },
   {
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                       {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('bbca250c-2a36-46dc-83a0-bf51640f93d0');\n",
+       "var gd = document.getElementById('6f23faf8-fb85-4ef2-be79-dac098a98f50');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
diff --git a/faster_coco_eval/extra/curves.py b/faster_coco_eval/extra/curves.py
index 9a97f04..d0ef323 100644
--- a/faster_coco_eval/extra/curves.py
+++ b/faster_coco_eval/extra/curves.py
@@ -1,7 +1,7 @@
 import logging
 
 from ..core import COCOeval_faster
-from .draw import plot_pre_rec
+from .draw import plot_f1_confidence, plot_pre_rec
 from .extra import ExtraEval
 
 logger = logging.getLogger(__name__)
@@ -37,6 +37,7 @@ def build_curve(self, label):
                     recall_list=recall_list,
                     precision_list=precision_list,
                     name="{}auc: {:.3f}".format(_label, auc),
+                    label=_label,
                     scores=scores,
                     auc=auc,
                     category_id=category_id,
@@ -52,3 +53,11 @@ def plot_pre_rec(
             curves = self.build_curve(label)
 
         return plot_pre_rec(curves, return_fig=return_fig)
+
+    def plot_f1_confidence(
+        self, curves=None, label: str = "category_id", return_fig: bool = False
+    ):
+        if curves is None:
+            curves = self.build_curve(label)
+
+        return plot_f1_confidence(curves, return_fig=return_fig)
diff --git a/faster_coco_eval/version.py b/faster_coco_eval/version.py
index 35fc040..45ba11b 100644
--- a/faster_coco_eval/version.py
+++ b/faster_coco_eval/version.py
@@ -1,2 +1,2 @@
-__version__ = "1.5.0"
+__version__ = "1.5.1"
 __author__ = "MiXaiLL76"

From 980e5ce474e88feb44cc3058056edb907a311fb5 Mon Sep 17 00:00:00 2001
From: MiXaiLL76 <mike.milos@yandex.ru>
Date: Fri, 12 Apr 2024 12:41:30 +0300
Subject: [PATCH 03/15] plot_f1_confidence

---
 faster_coco_eval/extra/draw.py | 53 ++++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)

diff --git a/faster_coco_eval/extra/draw.py b/faster_coco_eval/extra/draw.py
index e27b190..a584dba 100644
--- a/faster_coco_eval/extra/draw.py
+++ b/faster_coco_eval/extra/draw.py
@@ -305,3 +305,56 @@ def plot_pre_rec(curves, return_fig: bool = False):
         return fig
 
     fig.show()
+
+
+def plot_f1_confidence(curves, return_fig: bool = False):
+    fig = go.Figure()
+    eps = 1e-16
+    for _curve in curves:
+        recall_list = _curve["recall_list"]
+        precision_list = _curve["precision_list"]
+        scores = _curve["scores"]
+        f1_curve = (
+            2
+            * precision_list
+            * recall_list
+            / (precision_list + recall_list + eps)
+        )
+
+        name = _curve["label"] if len(_curve["label"]) > 0 else "F1-Confidence"
+
+        fig.add_trace(
+            go.Scatter(
+                x=scores,
+                y=f1_curve,
+                name=name,
+                hovertemplate="F1: %{y:.3f}<br>"
+                + "Confidence: %{x:.3f}<br><extra></extra>",
+                showlegend=True,
+                mode="lines",
+            )
+        )
+
+    margin = 0.01
+    fig.layout.yaxis.range = [0 - margin, 1 + margin]
+    fig.layout.xaxis.range = [0 - margin, 1 + margin]
+
+    fig.layout.yaxis.title = "F1"
+    fig.layout.xaxis.title = "Confidence"
+
+    fig.update_xaxes(showspikes=True)
+    fig.update_yaxes(showspikes=True)
+
+    layout = {
+        "title": "F1-Confidence",
+        "autosize": True,
+        "height": 600,
+        "width": 1200,
+    }
+
+    fig.update_layout(layout)
+
+    if return_fig:
+        return fig
+
+    fig.show()

From 06935f860a1db041265a369ff892763906b8c5d4 Mon Sep 17 00:00:00 2001
From: MiXaiLL76 <mike.milos@yandex.ru>
Date: Sat, 13 Apr 2024 23:32:06 +0300
Subject: [PATCH 04/15] remove not used files

---
 .flake8                                      |   3 -
 examples/comparison/comparison.ipynb         | 288 ------------
 examples/comparison/faster_coco_detection.py | 455 -------------------
 examples/comparison/faster_coco_wrapper.py   | 198 --------
 examples/comparison/mmdet_mmeval.py          |  73 ---
 setup.cfg                                    |   8 -
 6 files changed, 1025 deletions(-)
 delete mode 100644 .flake8
 delete mode 100644 examples/comparison/comparison.ipynb
 delete mode 100644 examples/comparison/faster_coco_detection.py
 delete mode 100644 examples/comparison/faster_coco_wrapper.py
 delete mode 100644 examples/comparison/mmdet_mmeval.py
 delete mode 100644 setup.cfg

diff --git a/.flake8 b/.flake8
deleted file mode 100644
index 73646ac..0000000
--- a/.flake8
+++ /dev/null
@@ -1,3 +0,0 @@
-[flake8]
-max-line-length = 88
-extend-ignore = E203, E501
\ No newline at end of file
diff --git a/examples/comparison/comparison.ipynb b/examples/comparison/comparison.ipynb
deleted file mode 100644
index 9baeebc..0000000
--- a/examples/comparison/comparison.ipynb
+++ /dev/null
@@ -1,288 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Install MMDetection"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<https://mmdetection.readthedocs.io/en/latest/get_started.html>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Download COCO VAL"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget -P COCO/DIR/ http://images.cocodataset.org/annotations/annotations_trainval2017.zip\n",
-    "!wget -P COCO/DIR/ http://images.cocodataset.org/zips/val2017.zip"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Unzip COCO VAL"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!unzip -qq COCO/DIR/annotations_trainval2017.zip -d COCO/DIR/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!unzip -qq COCO/DIR/val2017.zip -d COCO/DIR/"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Install MMEVAL"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip3 install --quiet --upgrade mmeval==0.2.1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Download model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "config_file='/home/mixaill76/.local/lib/python3.10/site-packages/mmdet/.mim/configs/rtmdet/rtmdet-ins_tiny_8xb32-300e_coco.py'\n",
-      "model_file='https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet-ins_tiny_8xb32-300e_coco/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth'\n",
-      "--2023-12-05 15:22:50--  https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet-ins_tiny_8xb32-300e_coco/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth\n",
-      "Resolving download.openmmlab.com (download.openmmlab.com)... 47.246.2.173, 47.246.2.213, 47.246.2.179, ...\n",
-      "Connecting to download.openmmlab.com (download.openmmlab.com)|47.246.2.173|:443... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 22757492 (22M) [application/octet-stream]\n",
-      "Saving to: ‘model/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth’\n",
-      "\n",
-      "rtmdet-ins_tiny_8xb 100%[===================>]  21.70M  39.3MB/s    in 0.6s    \n",
-      "\n",
-      "2023-12-05 15:22:51 (39.3 MB/s) - ‘model/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth’ saved [22757492/22757492]\n",
-      "\n",
-      "total 22M\n",
-      "drwxrwxrwx 2 mixaill76 mixaill76 4.0K Dec  5 15:22 .\n",
-      "drwxr-xr-x 5 mixaill76 mixaill76 4.0K Dec  5 15:22 ..\n",
-      "-rw-r--r-- 1 mixaill76 mixaill76  16K Dec  5 15:22 rtmdet-ins_tiny_8xb32-300e_coco.py\n",
-      "-rw-r--r-- 1 mixaill76 mixaill76  22M Dec 19  2022 rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth\n"
-     ]
-    }
-   ],
-   "source": [
-    "import mmdet\n",
-    "import mmengine\n",
-    "import os\n",
-    "import os.path as osp\n",
-    "\n",
-    "config_dir = osp.dirname(mmdet.__file__)\n",
-    "sub_config = \"configs/rtmdet/rtmdet-ins_tiny_8xb32-300e_coco.py\"\n",
-    "config_file = osp.join(config_dir, \".mim\", sub_config)\n",
-    "cfg = mmengine.Config.fromfile(config_file)\n",
-    "\n",
-    "model_file = \"https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet-ins_tiny_8xb32-300e_coco/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth\"\n",
-    "\n",
-    "print(f\"{config_file=}\")\n",
-    "print(f\"{model_file=}\")\n",
-    "\n",
-    "!mkdir -p -m 777 model\n",
-    "\n",
-    "cfg.dump(osp.join(\"model\", osp.basename(config_file)))\n",
-    "!wget -P model/ {model_file}\n",
-    "\n",
-    "!ls -lah model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Validate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loads checkpoint by local backend from path: ./model/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth\n",
-      "12/05 15:22:56 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
-      "/home/mixaill76/.local/lib/python3.10/site-packages/mmengine/visualization/visualizer.py:196: UserWarning: Failed to add <class 'mmengine.visualization.vis_backend.LocalVisBackend'>, please provide the `save_dir` argument.\n",
-      "  warnings.warn(f'Failed to add {vis_backend.__class__}, '\n",
-      "loading annotations into memory...\n",
-      "Done (t=0.32s)\n",
-      "creating index...\n",
-      "index created!\n",
-      "loading annotations into memory...\n",
-      "Done (t=0.38s)\n",
-      "creating index...\n",
-      "index created!\n",
-      "  0%|                                                  | 0/5000 [00:00<?, ?it/s]/home/mixaill76/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:456: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)\n",
-      "  return F.conv2d(input, weight, bias, self.stride,\n",
-      "/home/mixaill76/.local/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3526.)\n",
-      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
-      "100%|███████████████████████████████████████| 5000/5000 [03:39<00:00, 22.74it/s]\n",
-      "Evaluating bbox...\n",
-      "Loading and preparing results...\n",
-      "DONE (t=0.80s)\n",
-      "creating index...\n",
-      "index created!\n",
-      "Running per image evaluation...\n",
-      "Evaluate annotation type *bbox*\n",
-      "DONE (t=15.93s).\n",
-      "Accumulating evaluation results...\n",
-      "DONE (t=3.68s).\n",
-      "\n",
-      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.405\n",
-      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.576\n",
-      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.440\n",
-      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.207\n",
-      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.446\n",
-      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.578\n",
-      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.332\n",
-      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.540\n",
-      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.574\n",
-      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.338\n",
-      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.638\n",
-      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.765\n",
-      "\n",
-      "Evaluating segm...\n",
-      "Loading and preparing results...\n",
-      "DONE (t=1.57s)\n",
-      "creating index...\n",
-      "index created!\n",
-      "Running per image evaluation...\n",
-      "Evaluate annotation type *segm*\n",
-      "DONE (t=18.35s).\n",
-      "Accumulating evaluation results...\n",
-      "DONE (t=3.67s).\n",
-      "\n",
-      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.354\n",
-      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.551\n",
-      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.376\n",
-      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.131\n",
-      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.383\n",
-      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.566\n",
-      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.302\n",
-      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.473\n",
-      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.500\n",
-      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.252\n",
-      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.567\n",
-      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.716\n",
-      "\n",
-      "\n",
-      "\u001b[3m                                bbox Results (%)                               \u001b[0m\n",
-      "┏━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n",
-      "┃\u001b[1m \u001b[0m\u001b[1mbbox_mAP\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mbbox_mAP_50\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mbbox_mAP_75\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mbbox_mAP_s\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mbbox_mAP_m\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mbbox_mAP_l\u001b[0m\u001b[1m \u001b[0m┃\n",
-      "┡━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n",
-      "│ 40.49    │ 57.60       │ 43.96       │ 20.66      │ 44.64      │ 57.83      │\n",
-      "└──────────┴─────────────┴─────────────┴────────────┴────────────┴────────────┘\n",
-      "\n",
-      "\n",
-      "\u001b[3m                                segm Results (%)                               \u001b[0m\n",
-      "┏━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n",
-      "┃\u001b[1m \u001b[0m\u001b[1msegm_mAP\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msegm_mAP_50\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msegm_mAP_75\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msegm_mAP_s\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msegm_mAP_m\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msegm_mAP_l\u001b[0m\u001b[1m \u001b[0m┃\n",
-      "┡━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n",
-      "│ 35.39    │ 55.10       │ 37.65       │ 13.06      │ 38.34      │ 56.64      │\n",
-      "└──────────┴─────────────┴─────────────┴────────────┴────────────┴────────────┘\n",
-      "\n",
-      "coco_metric.compute() : 55.512\n",
-      "Evaluating bbox...\n",
-      "\n",
-      "\n",
-      "Evaluating segm...\n",
-      "\n",
-      "\n",
-      "\n",
-      "\u001b[3m                                bbox Results (%)                               \u001b[0m\n",
-      "┏━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n",
-      "┃\u001b[1m \u001b[0m\u001b[1mbbox_mAP\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mbbox_mAP_50\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mbbox_mAP_75\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mbbox_mAP_s\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mbbox_mAP_m\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mbbox_mAP_l\u001b[0m\u001b[1m \u001b[0m┃\n",
-      "┡━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n",
-      "│ 40.49    │ 57.60       │ 43.96       │ 20.66      │ 44.64      │ 57.83      │\n",
-      "└──────────┴─────────────┴─────────────┴────────────┴────────────┴────────────┘\n",
-      "\n",
-      "\n",
-      "\u001b[3m                                segm Results (%)                               \u001b[0m\n",
-      "┏━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n",
-      "┃\u001b[1m \u001b[0m\u001b[1msegm_mAP\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msegm_mAP_50\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msegm_mAP_75\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msegm_mAP_s\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msegm_mAP_m\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msegm_mAP_l\u001b[0m\u001b[1m \u001b[0m┃\n",
-      "┡━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n",
-      "│ 35.39    │ 55.10       │ 37.65       │ 13.06      │ 38.34      │ 56.64      │\n",
-      "└──────────┴─────────────┴─────────────┴────────────┴────────────┴────────────┘\n",
-      "\n",
-      "faster_coco_metric.compute() : 27.748\n",
-      "0.4998470015873722\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "!python3 mmdet_mmeval.py --load \"./model/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth\" --config \"./model/rtmdet-ins_tiny_8xb32-300e_coco.py\""
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/comparison/faster_coco_detection.py b/examples/comparison/faster_coco_detection.py
deleted file mode 100644
index 85e556d..0000000
--- a/examples/comparison/faster_coco_detection.py
+++ /dev/null
@@ -1,455 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import contextlib
-import datetime
-import io
-import itertools
-import numpy as np
-import os.path as osp
-import tempfile
-import warnings
-from collections import OrderedDict
-from json import dump
-from mmeval.fileio import get_local_path, load
-from mmeval.metrics import COCODetection
-from mmeval.utils import is_list_of
-from rich.console import Console
-from rich.table import Table
-from typing import List, Optional, Sequence, Union
-
-try:
-    from faster_coco_wrapper import COCO, COCOeval
-
-    HAS_FASTER_COCOAPI = True
-except ImportError:
-    HAS_FASTER_COCOAPI = False
-
-
-class FasterCOCODetection(COCODetection):
-    def __init__(
-        self,
-        ann_file: Optional[str] = None,
-        metric: Union[str, List[str]] = "bbox",
-        iou_thrs: Union[float, Sequence[float], None] = None,
-        classwise: bool = False,
-        proposal_nums: Sequence[int] = (1, 10, 100),
-        metric_items: Optional[Sequence[str]] = None,
-        format_only: bool = False,
-        outfile_prefix: Optional[str] = None,
-        gt_mask_area: bool = True,
-        backend_args: Optional[dict] = None,
-        print_results: bool = True,
-        extra_calc: bool = False,
-        **kwargs,
-    ) -> None:
-        if not HAS_FASTER_COCOAPI:
-            raise RuntimeError(
-                "Failed to import `COCO` and `COCOeval` from "
-                "`mmeval.utils.coco_wrapper`. "
-                "Please try to install official pycocotools by "
-                '"pip install pycocotools"'
-            )
-        super().__init__(**kwargs)
-        # coco evaluation metrics
-        self.metrics = metric if isinstance(metric, list) else [metric]
-        allowed_metrics = ["bbox", "segm"]
-        for metric in self.metrics:
-            if metric not in allowed_metrics:
-                raise KeyError(
-                    "metric should be one of 'bbox' and 'segm'"
-                    f"but got {metric}."
-                )
-
-        # do class wise evaluation, default False
-        self.classwise = classwise
-
-        # proposal_nums used to compute recall or precision.
-        self.proposal_nums = list(proposal_nums)
-
-        # iou_thrs used to compute recall or precision.
-        if iou_thrs is None:
-            iou_thrs = np.linspace(
-                0.5,
-                0.95,
-                int(np.round((0.95 - 0.5) / 0.05)) + 1,
-                endpoint=True,
-            )
-        elif isinstance(iou_thrs, float):
-            iou_thrs = np.array([iou_thrs])
-        elif is_list_of(iou_thrs, float):
-            iou_thrs = np.array(iou_thrs)
-        else:
-            raise TypeError(
-                "`iou_thrs` should be None, float, or a list of float"
-            )
-
-        self.iou_thrs = iou_thrs
-        self.metric_items = metric_items
-        self.print_results = print_results
-        self.extra_calc = extra_calc
-        self.format_only = format_only
-        if self.format_only:
-            assert outfile_prefix is not None, "outfile_prefix must be not"
-            "None when format_only is True, otherwise the result files will"
-            "be saved to a temp directory which will be cleaned up at the end."
-
-        self.outfile_prefix = outfile_prefix
-
-        # if ann_file is not specified,
-        # initialize coco api with the converted dataset
-        self._coco_api: Optional[COCO]  # type: ignore
-        if ann_file is not None:
-            with get_local_path(
-                filepath=ann_file, backend_args=backend_args
-            ) as local_path:
-                self._coco_api = COCO(annotation_file=local_path)
-        else:
-            self._coco_api = None
-
-        self.gt_mask_area = gt_mask_area
-        # handle dataset lazy init
-        self.cat_ids: list = []
-        self.img_ids: list = []
-
-    def gt_to_coco_json(
-        self, gt_dicts: Sequence[dict], outfile_prefix: str
-    ) -> str:
-        """Convert ground truth to coco format json file.
-
-        Args:
-            gt_dicts (Sequence[dict]): Ground truth of the dataset.
-            outfile_prefix (str): The filename prefix of the json files. If the
-                prefix is "somepath/xxx", the json file will be named
-                "somepath/xxx.gt.json".
-
-        Returns:
-            str: The filename of the json file.
-        """
-        try:
-            from faster_coco_wrapper import mask_util
-        except ImportError:
-            mask_util = None
-
-        warnings.warn(
-            "The area of the instance is default to use bbox area. "
-            "Compared to load annotation file evaluate way, this will "
-            "not affect the overall AP, but leads to different "
-            "small/medium/large AP results."
-        )
-
-        classes = self.classes
-        categories = [dict(id=id, name=name) for id, name in enumerate(classes)]
-        image_infos: list = []
-        annotations: list = []
-
-        for idx, gt_dict in enumerate(gt_dicts):
-            img_id = gt_dict.get("img_id", idx)
-            image_info = dict(
-                id=img_id,
-                width=gt_dict["width"],
-                height=gt_dict["height"],
-                file_name="",
-            )
-            image_infos.append(image_info)
-            gt_bboxes = gt_dict["bboxes"]
-            gt_labels = gt_dict["labels"]
-            assert len(gt_bboxes) == len(gt_labels)
-            if "ignore_flags" in gt_dict:
-                ignore_flags = gt_dict["ignore_flags"]
-                assert len(gt_bboxes) == len(ignore_flags)
-            else:
-                ignore_flags = np.zeros(len(gt_bboxes))
-            if "masks" in gt_dict:
-                gt_masks = gt_dict["masks"]
-                assert len(gt_masks) == len(gt_bboxes)
-            else:
-                gt_masks = [None for _ in range(len(gt_bboxes))]
-
-            for i in range(len(gt_bboxes)):
-                label = gt_labels[i]
-                coco_bbox = self.xyxy2xywh(gt_bboxes[i])
-                ignore_flag = ignore_flags[i]
-                mask = gt_masks[i]
-                annotation = dict(
-                    id=len(annotations)
-                    + 1,  # coco api requires id starts with 1
-                    image_id=img_id,
-                    bbox=coco_bbox,
-                    iscrowd=int(ignore_flag),
-                    category_id=int(label),
-                    area=coco_bbox[2] * coco_bbox[3],
-                )
-                if mask is not None:
-                    if mask_util and self.gt_mask_area:
-                        # Using mask area can reduce the gap of
-                        # small/medium/large AP results.
-                        area = mask_util.area(mask)
-                        annotation["area"] = float(area)
-                    if isinstance(mask, dict) and isinstance(
-                        mask["counts"], bytes
-                    ):
-                        mask["counts"] = mask["counts"].decode()
-                    annotation["segmentation"] = mask
-                annotations.append(annotation)
-
-        info = dict(
-            date_created=str(datetime.datetime.now()),
-            description="Coco json file converted by mmeval CocoMetric.",
-        )
-        coco_json = dict(
-            info=info,
-            images=image_infos,
-            categories=categories,
-            licenses=None,
-        )
-        if len(annotations) > 0:
-            coco_json["annotations"] = annotations
-        converted_json_path = f"{outfile_prefix}.gt.json"
-        with open(converted_json_path, "w") as f:
-            dump(coco_json, f)
-        return converted_json_path
-
-    def compute_metric(self, results: list) -> dict:
-        """Compute the COCO metrics.
-
-        Args:
-            results (List[tuple]): A list of tuple. Each tuple is the
-                prediction and ground truth of an image. This list has already
-                been synced across all ranks.
-
-        Returns:
-            dict: The computed metric.
-            The keys are the names of the metrics, and the values are
-            corresponding results.
-        """
-        tmp_dir = None
-        if self.outfile_prefix is None:
-            tmp_dir = tempfile.TemporaryDirectory()
-            outfile_prefix = osp.join(tmp_dir.name, "results")
-        else:
-            outfile_prefix = self.outfile_prefix
-
-        classes = self.classes
-        # split gt and prediction list
-        preds, gts = zip(*results)
-
-        if self._coco_api is None:
-            # use converted gt json file to initialize coco api
-            self.logger.info("Converting ground truth to coco format...")
-            coco_json_path = self.gt_to_coco_json(
-                gt_dicts=gts, outfile_prefix=outfile_prefix
-            )
-            self._coco_api = COCO(coco_json_path)
-
-        # handle lazy init
-        if len(self.cat_ids) == 0:
-            self.cat_ids = self._coco_api.get_cat_ids(cat_names=classes)  # type: ignore
-        if len(self.img_ids) == 0:
-            self.img_ids = self._coco_api.get_img_ids()
-
-        # convert predictions to coco format and dump to json file
-        result_files = self.results2json(preds, outfile_prefix)
-
-        eval_results: OrderedDict = OrderedDict()
-        table_results: OrderedDict = OrderedDict()
-        if self.format_only:
-            self.logger.info(
-                f"Results are saved in {osp.dirname(outfile_prefix)}"
-            )
-            return eval_results
-
-        for metric in self.metrics:
-            self.logger.info(f"Evaluating {metric}...")
-
-            # evaluate proposal, bbox and segm
-            iou_type = "bbox" if metric == "proposal" else metric
-            if metric not in result_files:
-                raise KeyError(f"{metric} is not in results")
-            try:
-                predictions = load(result_files[metric])
-                if iou_type == "segm":
-                    # Refer to https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/coco.py#L331  # noqa
-                    # When evaluating mask AP, if the results contain bbox,
-                    # cocoapi will use the box area instead of the mask area
-                    # for calculating the instance area. Though the overall AP
-                    # is not affected, this leads to different
-                    # small/medium/large mask AP results.
-                    for x in predictions:
-                        x.pop("bbox")
-                coco_dt = self._coco_api.loadRes(predictions)
-
-            except IndexError:
-                self.logger.warning(
-                    "The testing results of the " "whole dataset is empty."
-                )
-                break
-
-            coco_eval = COCOeval(
-                self._coco_api, coco_dt, iou_type, extra_calc=self.extra_calc
-            )
-
-            coco_eval.params.catIds = self.cat_ids
-            coco_eval.params.imgIds = self.img_ids
-            coco_eval.params.maxDets = self.proposal_nums
-            coco_eval.params.iouThrs = self.iou_thrs
-
-            # mapping of cocoEval.stats
-            coco_metric_names = {
-                "mAP": 0,
-                "mAP_50": 1,
-                "mAP_75": 2,
-                "mAP_s": 3,
-                "mAP_m": 4,
-                "mAP_l": 5,
-                f"AR@{self.proposal_nums[0]}": 6,
-                f"AR@{self.proposal_nums[1]}": 7,
-                f"AR@{self.proposal_nums[2]}": 8,
-                f"AR_s@{self.proposal_nums[2]}": 9,
-                f"AR_m@{self.proposal_nums[2]}": 10,
-                f"AR_l@{self.proposal_nums[2]}": 11,
-            }
-            metric_items = self.metric_items
-            if metric_items is not None:
-                for metric_item in metric_items:
-                    if metric_item not in coco_metric_names:
-                        raise KeyError(
-                            f'metric item "{metric_item}" is not supported'
-                        )
-
-            coco_eval.evaluate()
-            coco_eval.accumulate()
-            # Save coco summarize print information to logger
-            redirect_string = io.StringIO()
-            with contextlib.redirect_stdout(redirect_string):
-                coco_eval.summarize()
-            self.logger.info("\n" + redirect_string.getvalue())
-            if metric_items is None:
-                metric_items = [
-                    "mAP",
-                    "mAP_50",
-                    "mAP_75",
-                    "mAP_s",
-                    "mAP_m",
-                    "mAP_l",
-                ]
-
-            results_list = []
-            for metric_item in metric_items:
-                key = f"{metric}_{metric_item}"
-                val = coco_eval.stats[coco_metric_names[metric_item]]
-                results_list.append(f"{round(val * 100, 2):0.2f}")
-                eval_results[key] = float(val)
-            table_results[f"{metric}_result"] = results_list
-
-            if self.classwise:  # Compute per-category AP
-                # Compute per-category AP
-                # from https://github.com/facebookresearch/detectron2/
-                precisions = coco_eval.eval["precision"]
-                # precision: (iou, recall, cls, area range, max dets)
-                assert len(self.cat_ids) == precisions.shape[2]
-
-                results_per_category = []
-                for idx, cat_id in enumerate(self.cat_ids):
-                    # area range index 0: all area ranges
-                    # max dets index -1: typically 100 per image
-                    nm = self._coco_api.loadCats(cat_id)[0]
-                    precision = precisions[:, :, idx, 0, -1]
-                    precision = precision[precision > -1]
-                    if precision.size:
-                        ap = np.mean(precision)
-                    else:
-                        ap = float("nan")
-                    results_per_category.append(
-                        (f'{nm["name"]}', f"{round(ap * 100, 2):0.2f}")
-                    )
-                    eval_results[f'{metric}_{nm["name"]}_precision'] = ap
-
-                table_results[f"{metric}_classwise_result"] = (
-                    results_per_category
-                )
-        if tmp_dir is not None:
-            tmp_dir.cleanup()
-        # if the testing results of the whole dataset is empty,
-        # does not print tables.
-        if self.print_results and len(table_results) > 0:
-            self._print_results(table_results)
-        return eval_results
-
-    def _print_results(self, table_results: dict) -> None:
-        """Print the evaluation results table.
-
-        Args:
-            table_results (dict): The computed metric.
-        """
-        for metric in self.metrics:
-            result = table_results[f"{metric}_result"]
-
-            if metric == "proposal":
-                table_title = " Recall Results (%)"
-                if self.metric_items is None:
-                    assert len(result) == 6
-                    headers = [
-                        f"AR@{self.proposal_nums[0]}",
-                        f"AR@{self.proposal_nums[1]}",
-                        f"AR@{self.proposal_nums[2]}",
-                        f"AR_s@{self.proposal_nums[2]}",
-                        f"AR_m@{self.proposal_nums[2]}",
-                        f"AR_l@{self.proposal_nums[2]}",
-                    ]
-                else:
-                    assert len(result) == len(self.metric_items)  # type: ignore # yapf: disable # noqa: E501
-                    headers = self.metric_items  # type: ignore
-            else:
-                table_title = f" {metric} Results (%)"
-                if self.metric_items is None:
-                    assert len(result) == 6
-                    headers = [
-                        f"{metric}_mAP",
-                        f"{metric}_mAP_50",
-                        f"{metric}_mAP_75",
-                        f"{metric}_mAP_s",
-                        f"{metric}_mAP_m",
-                        f"{metric}_mAP_l",
-                    ]
-                else:
-                    assert len(result) == len(self.metric_items)
-                    headers = [f"{metric}_{item}" for item in self.metric_items]
-            table = Table(title=table_title)
-            console = Console()
-            for name in headers:
-                table.add_column(name, justify="left")
-            table.add_row(*result)
-            with console.capture() as capture:
-                console.print(table, end="")
-            self.logger.info("\n" + capture.get())
-
-            if self.classwise and metric != "proposal":
-                self.logger.info(
-                    f"Evaluating {metric} metric of each category..."
-                )
-                classwise_table_title = f" {metric} Classwise Results (%)"
-                classwise_result = table_results[f"{metric}_classwise_result"]
-
-                num_columns = min(6, len(classwise_result) * 2)
-                results_flatten = list(itertools.chain(*classwise_result))
-                headers = ["category", f"{metric}_AP"] * (num_columns // 2)
-                results_2d = itertools.zip_longest(
-                    *[
-                        results_flatten[i::num_columns]
-                        for i in range(num_columns)
-                    ]
-                )
-
-                table = Table(title=classwise_table_title)
-                console = Console()
-                for name in headers:
-                    table.add_column(name, justify="left")
-                for _result in results_2d:
-                    table.add_row(*_result)
-                with console.capture() as capture:
-                    console.print(table, end="")
-                self.logger.info("\n" + capture.get())
-
-
-# Keep the deprecated metric name as an alias.
-# The deprecated Metric names will be removed in 1.0.0!
-COCODetectionMetric = COCODetection
diff --git a/examples/comparison/faster_coco_wrapper.py b/examples/comparison/faster_coco_wrapper.py
deleted file mode 100644
index ca01424..0000000
--- a/examples/comparison/faster_coco_wrapper.py
+++ /dev/null
@@ -1,198 +0,0 @@
-# Copyright (c) MiXaill76.
-from collections import defaultdict
-from pathlib import Path
-from typing import Dict, Optional, Sequence, Union
-
-import faster_coco_eval.core.mask as _mask_util
-from faster_coco_eval import COCO as _COCO
-from faster_coco_eval import COCOeval_faster as _COCOeval
-
-
-class COCO(_COCO):
-    """This class is almost the same as official pycocotools package.
-
-    It implements some snake case function aliases. So that the COCO class has
-    the same interface as LVIS class.
-
-    Args:
-        annotation_file (str, optional): Path of annotation file.
-            Defaults to None.
-    """
-
-    def __init__(
-        self, annotation_file: Optional[Union[str, Path]] = None
-    ) -> None:
-        super().__init__(annotation_file=annotation_file)
-        self.img_ann_map = self.imgToAnns
-        self.cat_img_map = self.catToImgs
-
-    def get_ann_ids(
-        self,
-        img_ids: Union[list, int] = [],
-        cat_ids: Union[list, int] = [],
-        area_rng: Union[list, int] = [],
-        iscrowd: Optional[bool] = None,
-    ) -> list:
-        """Get annotation ids that satisfy given filter conditions.
-
-        Args:
-            img_ids (list | int): Get annotations for given images.
-            cat_ids (list | int): Get categories for given images.
-            area_rng (list | int): Get annotations for given area range.
-            iscrowd (bool, optional):  Get annotations for given crowd label.
-
-        Returns:
-            List: Integer array of annotation ids.
-        """
-        return self.getAnnIds(img_ids, cat_ids, area_rng, iscrowd)
-
-    def get_cat_ids(
-        self,
-        cat_names: Union[list, int] = [],
-        sup_names: Union[list, int] = [],
-        cat_ids: Union[list, int] = [],
-    ) -> list:
-        """Get category ids that satisfy given filter conditions.
-
-        Args:
-            cat_names (list | int): Get categories for given category names.
-            sup_names (list | int): Get categories for given supercategory
-                names.
-            cat_ids (list | int):  Get categories  for given category ids.
-
-        Returns:
-            List: Integer array of category ids.
-        """
-        return self.getCatIds(cat_names, sup_names, cat_ids)
-
-    def get_img_ids(
-        self, img_ids: Union[list, int] = [], cat_ids: Union[list, int] = []
-    ) -> list:
-        """Get image ids that satisfy given filter conditions.
-
-        Args:
-            img_ids (list | int): Get images for given ids
-            cat_ids (list | int): Get images with all given cats
-
-        Returns:
-            List: Integer array of image ids.
-        """
-        return self.getImgIds(img_ids, cat_ids)
-
-    def load_anns(self, ids: Union[list, int] = []) -> list:
-        """Load annotations with the specified ids.
-
-        Args:
-            ids (list | int): Integer ids specifying annotations.
-
-        Returns:
-            List[dict]: Loaded annotation objects.
-        """
-        return self.loadAnns(ids)
-
-    def load_cats(self, ids: Union[list, int] = []) -> list:
-        """Load categories with the specified ids.
-
-        Args:
-            ids (list | int): Integer ids specifying categories.
-
-        Returns:
-            List[dict]: loaded category objects.
-        """
-        return self.loadCats(ids)
-
-    def load_imgs(self, ids: Union[list, int] = []) -> list:
-        """Load annotations with the specified ids.
-
-        Args:
-            ids (list): integer ids specifying image.
-
-        Returns:
-            List[dict]: Loaded image objects.
-        """
-        return self.loadImgs(ids)
-
-
-class COCOPanoptic(COCO):
-    """This wrapper is for loading the panoptic style annotation file."""
-
-    def createIndex(self) -> None:
-        """Create index."""
-        # create index
-        print("creating index...")
-        # anns stores 'segment_id -> annotation'
-        anns: Dict[int, list] = {}
-        cats: Dict[int, dict] = {}
-        imgs: Dict[int, dict] = {}
-        img_to_anns, cat_to_imgs = defaultdict(list), defaultdict(list)
-        if "annotations" in self.dataset:
-            for ann in self.dataset["annotations"]:
-                for seg_ann in ann["segments_info"]:
-                    # to match with instance.json
-                    seg_ann["image_id"] = ann["image_id"]
-                    img_to_anns[ann["image_id"]].append(seg_ann)
-                    # segment_id is not unique in coco dataset orz...
-                    # annotations from different images but
-                    # may have same segment_id
-                    if seg_ann["id"] in anns.keys():
-                        anns[seg_ann["id"]].append(seg_ann)
-                    else:
-                        anns[seg_ann["id"]] = [seg_ann]
-
-            # filter out annotations from other images
-            img_to_anns_ = defaultdict(list)
-            for k, v in img_to_anns.items():
-                img_to_anns_[k] = [x for x in v if x["image_id"] == k]
-            img_to_anns = img_to_anns_
-
-        if "images" in self.dataset:
-            for img_info in self.dataset["images"]:
-                img_info["segm_file"] = img_info["file_name"].replace(
-                    "jpg", "png"
-                )
-                imgs[img_info["id"]] = img_info
-
-        if "categories" in self.dataset:
-            for cat in self.dataset["categories"]:
-                cats[cat["id"]] = cat
-
-        if "annotations" in self.dataset and "categories" in self.dataset:
-            for ann in self.dataset["annotations"]:
-                for seg_ann in ann["segments_info"]:
-                    cat_to_imgs[seg_ann["category_id"]].append(ann["image_id"])
-
-        print("index created!")
-
-        self.anns = anns
-        self.imgToAnns = img_to_anns
-        self.catToImgs = cat_to_imgs
-        self.imgs = imgs
-        self.cats = cats
-
-    def load_anns(self, ids: Union[list, int] = []) -> list:
-        """Load annotations with the specified ids.
-
-        ``self.anns`` is a list of annotation lists instead of a
-        list of annotations.
-
-        Args:
-            ids (Union[List[int], int]): Integer ids specifying annotations.
-
-        Returns:
-            List: Loaded annotation objects.
-        """
-        anns = []
-
-        if isinstance(ids, Sequence):
-            # self.anns is a list of annotation lists instead of
-            # a list of annotations
-            for id in ids:
-                anns += self.anns[id]
-            return anns
-        else:
-            return self.anns[ids]
-
-
-# just for the ease of import
-COCOeval = _COCOeval
-mask_util = _mask_util
diff --git a/examples/comparison/mmdet_mmeval.py b/examples/comparison/mmdet_mmeval.py
deleted file mode 100644
index 40881a8..0000000
--- a/examples/comparison/mmdet_mmeval.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import argparse
-import numpy as np
-import time
-import tqdm
-from faster_coco_detection import FasterCOCODetection
-from mmdet.apis import DetInferencer
-from mmdet.datasets import CocoDataset
-from mmeval.metrics import COCODetection  # type: ignore
-
-
-def do_mmeval_evaluate(config_file: str, checkpoint: str):
-    model = DetInferencer(config_file, checkpoint, show_progress=False)
-
-    coco_dataset = CocoDataset(
-        # ann_file='annotations/instances_val2017_short.json',
-        ann_file="annotations/instances_val2017.json",
-        data_prefix=dict(img="val2017/"),
-        data_root="./COCO/DIR",
-    )
-
-    faster_coco_metric = FasterCOCODetection(
-        ann_file=coco_dataset.ann_file,
-        metric=["bbox", "segm"],
-        proposal_nums=[1, 10, 100],
-    )
-    coco_metric = COCODetection(
-        ann_file=coco_dataset.ann_file,
-        metric=["bbox", "segm"],
-        proposal_nums=[1, 10, 100],
-    )
-
-    faster_coco_metric.dataset_meta = {
-        "CLASSES": coco_dataset.METAINFO["classes"]
-    }
-    coco_metric.dataset_meta = {"CLASSES": coco_dataset.METAINFO["classes"]}
-
-    for item in tqdm.tqdm(coco_dataset):
-        pred_results = model(item["img_path"])["predictions"][0]
-        pred_results["bboxes"] = np.array(pred_results["bboxes"])
-        pred_results["img_id"] = item["img_id"]
-        coco_metric.add_predictions([pred_results])
-        faster_coco_metric.add_predictions([pred_results])
-
-    ts1 = time.time()
-    coco_metric.compute()
-    te1 = time.time()
-
-    print(f"coco_metric.compute() : {te1-ts1:.3f}")
-
-    ts2 = time.time()
-    faster_coco_metric.compute()
-    te2 = time.time()
-
-    print(f"faster_coco_metric.compute() : {te2-ts2:.3f}")
-
-    print((te2 - ts2) / (te1 - ts1))
-    print()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--load", type=str, help="load a model for evaluation.", required=True
-    )
-    parser.add_argument(
-        "--config",
-        type=str,
-        help="load a config for evaluation.",
-        required=True,
-    )
-    args = parser.parse_args()
-
-    do_mmeval_evaluate(args.config, args.load)
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 28630e2..0000000
--- a/setup.cfg
+++ /dev/null
@@ -1,8 +0,0 @@
-[isort]
-line_length = 80
-multi_line_output = 0
-known_standard_library = setuptools
-known_first_party = faster-coco-eval
-known_third_party = yaml
-no_lines_before = STDLIB,LOCALFOLDER
-default_section = THIRDPARTY
\ No newline at end of file

From a198fac3176d5779d2d155c49b633538189628b6 Mon Sep 17 00:00:00 2001
From: MiXaiLL76 <mike.milos@yandex.ru>
Date: Sat, 13 Apr 2024 23:32:24 +0300
Subject: [PATCH 05/15] update make rules

---
 Makefile | 25 +++++++++++++++++--------
 1 file changed, 17 insertions(+), 8 deletions(-)

diff --git a/Makefile b/Makefile
index cf62e63..cced93a 100644
--- a/Makefile
+++ b/Makefile
@@ -1,21 +1,30 @@
 all: clean format sdist wheel
 	ls -lah dist
 
+wheel:
+	pipx run build --wheel .
+
 sdist:
-	python3 -m build . --sdist
+	pipx run build --sdist .
 
-wheel:
-	python3 -m build . --wheel
+install: clean wheel
+	pip3 install dist/*.whl
+
+FORMAT_DIRS = ./faster_coco_eval ./tests ./examples setup.py
+LINE_LENGTH = 80
+BLACK_CONFIG = --preview --enable-unstable-feature string_processing
 
 format:
-	python3 -m black --config pyproject.toml .
-	python3 -m isort --profile black .
+	python3 -m docformatter --in-place --recursive --blank $(FORMAT_DIRS)
+	python3 -m black $(BLACK_CONFIG) --line-length $(LINE_LENGTH) $(FORMAT_DIRS)
+	python3 -m isort --line-length $(LINE_LENGTH) --profile black $(FORMAT_DIRS)
 	$(MAKE) linter
 
 linter:
-	python3 -m black . --check --diff
-	python3 -m isort --profile black . --check --diff
-	flake8 .
+	python3 -m docformatter --check --recursive --blank $(FORMAT_DIRS)
+	python3 -m black $(BLACK_CONFIG) --line-length $(LINE_LENGTH) $(FORMAT_DIRS) --check --diff
+	python3 -m isort --line-length $(LINE_LENGTH) --profile black $(FORMAT_DIRS) --check --diff
+	flake8 --max-line-length $(LINE_LENGTH) $(FORMAT_DIRS) --ignore=E203,W503 --exclude "*/model/*"
 
 clean:
 	rm -rf build

From df207a7f983fc942f2299b489ada24938491ceb4 Mon Sep 17 00:00:00 2001
From: MiXaiLL76 <mike.milos@yandex.ru>
Date: Sat, 13 Apr 2024 23:32:44 +0300
Subject: [PATCH 06/15] remove tools from project

---
 pyproject.toml | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index cb18402..41af64b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,8 +7,4 @@ requires = [
     "cython",
     "pybind11==2.11.1",
 ]
-build-backend = "setuptools.build_meta"
-
-
-[tool.black]
-line-length = 80
\ No newline at end of file
+build-backend = "setuptools.build_meta"
\ No newline at end of file

From c519ff34972552f86d6ce2d08a8e346535d90252 Mon Sep 17 00:00:00 2001
From: MiXaiLL76 <mike.milos@yandex.ru>
Date: Sat, 13 Apr 2024 23:33:06 +0300
Subject: [PATCH 07/15] update format

---
 setup.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/setup.py b/setup.py
index bc4cf2b..95a49c8 100644
--- a/setup.py
+++ b/setup.py
@@ -1,12 +1,12 @@
 #!/usr/bin/python3
 
-import setuptools
-from setuptools import Extension, setup
-
 import glob
-import numpy as np
 import platform
+
+import numpy as np
+import setuptools
 from pybind11.setup_helpers import Pybind11Extension, build_ext
+from setuptools import Extension, setup
 
 
 def readme():
@@ -27,6 +27,7 @@ def get_version():
 def parse_requirements(fname="requirements/runtime.txt", with_version=True):
     """Parse the package dependencies listed in a requirements file but strips
     specific versioning information.
+
     Args:
         fname (str): path to requirements file
         with_version (bool, default=False): if True include version specs
@@ -34,6 +35,7 @@ def parse_requirements(fname="requirements/runtime.txt", with_version=True):
         List[str]: list of requirements items
     CommandLine:
         python -c "import setup; print(setup.parse_requirements())"
+
     """
     import re
     import sys
@@ -67,7 +69,7 @@ def parse_line(line):
                         version, platform_deps = map(str.strip, rest.split(";"))
                         info["platform_deps"] = platform_deps
                     else:
-                        version = rest  # NOQA
+                        version = rest
                     info["version"] = (op, version)
             yield info
 

From 54053fdad0cc3a72eead02db6f4c92b3de97861b Mon Sep 17 00:00:00 2001
From: MiXaiLL76 <mike.milos@yandex.ru>
Date: Sat, 13 Apr 2024 23:33:31 +0300
Subject: [PATCH 08/15] update comparison

---
 README.md                                     |  19 +-
 examples/comparison/mmdet/coco_metric.py      | 703 ++++++++++++++++++
 examples/comparison/mmdet/comparison.ipynb    | 589 +++++++++++++++
 .../comparison/mmdet/eval_det_model.ipynb     | 238 ++++++
 4 files changed, 1544 insertions(+), 5 deletions(-)
 create mode 100644 examples/comparison/mmdet/coco_metric.py
 create mode 100644 examples/comparison/mmdet/comparison.ipynb
 create mode 100644 examples/comparison/mmdet/eval_det_model.ipynb

diff --git a/README.md b/README.md
index 7ed1132..4e2217e 100644
--- a/README.md
+++ b/README.md
@@ -17,13 +17,22 @@ for coco's AP metrics, especially when dealing with a high number of instances i
 For our use case with a test dataset of 5000 images from the coco val dataset.
 Testing was carried out using the mmdetection framework and the eval_metric.py script. The indicators are presented below.
 
-Visualization of testing **comparison.ipynb** available in directory [examples/comparison](https://github.com/MiXaiLL76/faster_coco_eval/blob/main/examples/comparison/comparison.ipynb)
+Visualization of testing **comparison.ipynb** available in directory [examples/comparison](https://github.com/MiXaiLL76/faster_coco_eval/blob/main/examples/comparison/mmdet/comparison.ipynb)
 Tested with yolo3 model (bbox eval) and yoloact model (segm eval)
 
-| Type | COCOeval    | COCOeval_faster | Profit      |
-| ---- | ----------- | --------------- | ----------- |
-| bbox | 18.477 sec. | 7.345 sec.      | 2.5x faster |
-| segm | 29.819 sec. | 15.840 sec.     | 2x faster   |
+### Summary for 5000 imgs
+
+| Type | COCOeval    | COCOeval_faster | Profit       |
+| ---- | ----------- | --------------- | ------------ |
+| bbox | 18.477 sec. | 7.345 sec.      | ~2.5x faster |
+| segm | 29.819 sec. | 15.840 sec.     | ~1.7x faster |
+
+## Summary for 500 imgs
+
+| Type | COCOeval  | COCOeval_faster | Profit       |
+| ---- | --------- | --------------- | ------------ |
+| bbox | 3.57 sec. | 2.03 sec.       | ~1.7x faster |
+| segm | 4.16 sec. | 2.41 sec.       | ~1.7x faster |
 
 ## Feautures
 
diff --git a/examples/comparison/mmdet/coco_metric.py b/examples/comparison/mmdet/coco_metric.py
new file mode 100644
index 0000000..fbb5ac4
--- /dev/null
+++ b/examples/comparison/mmdet/coco_metric.py
@@ -0,0 +1,703 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import datetime
+import itertools
+import os.path as osp
+import tempfile
+from collections import OrderedDict
+from typing import Dict, List, Optional, Sequence, Union
+
+import numpy as np
+import torch
+from mmdet.datasets.api_wrappers import COCO, COCOeval, COCOevalMP
+from mmdet.evaluation.functional import eval_recalls
+from mmdet.registry import METRICS
+from mmdet.structures.mask import encode_mask_results
+from mmengine.evaluator import BaseMetric
+from mmengine.fileio import dump, get_local_path, load
+from mmengine.logging import MMLogger
+from terminaltables import AsciiTable
+
+from faster_coco_eval import COCO as COCO_faster
+from faster_coco_eval import COCOeval_faster
+
+if "CocoMetric" in METRICS.module_dict:
+    del METRICS.module_dict["CocoMetric"]
+
+
+@METRICS.register_module()
+class CocoMetric(BaseMetric):
+    """COCO evaluation metric.
+
+    Evaluate AR, AP, and mAP for detection tasks including proposal/box
+    detection and instance segmentation. Please refer to
+    https://cocodataset.org/#detection-eval for more details.
+
+    Args:
+        ann_file (str, optional): Path to the coco format annotation file.
+            If not specified, ground truth annotations from the dataset will
+            be converted to coco format. Defaults to None.
+        metric (str | List[str]): Metrics to be evaluated. Valid metrics
+            include 'bbox', 'segm', 'proposal', and 'proposal_fast'.
+            Defaults to 'bbox'.
+        classwise (bool): Whether to evaluate the metric class-wise.
+            Defaults to False.
+        proposal_nums (Sequence[int]): Numbers of proposals to be evaluated.
+            Defaults to (100, 300, 1000).
+        iou_thrs (float | List[float], optional): IoU threshold to compute AP
+            and AR. If not specified, IoUs from 0.5 to 0.95 will be used.
+            Defaults to None.
+        metric_items (List[str], optional): Metric result names to be
+            recorded in the evaluation result. Defaults to None.
+        format_only (bool): Format the output results without perform
+            evaluation. It is useful when you want to format the result
+            to a specific format and submit it to the test server.
+            Defaults to False.
+        outfile_prefix (str, optional): The prefix of json files. It includes
+            the file path and the prefix of filename, e.g., "a/b/prefix".
+            If not specified, a temp file will be created. Defaults to None.
+        file_client_args (dict, optional): Arguments to instantiate the
+            corresponding backend in mmdet <= 3.0.0rc6. Defaults to None.
+        backend_args (dict, optional): Arguments to instantiate the
+            corresponding backend. Defaults to None.
+        collect_device (str): Device name used for collecting results from
+            different ranks during distributed training. Must be 'cpu' or
+            'gpu'. Defaults to 'cpu'.
+        prefix (str, optional): The prefix that will be added in the metric
+            names to disambiguate homonymous metrics of different evaluators.
+            If prefix is not provided in the argument, self.default_prefix
+            will be used instead. Defaults to None.
+        sort_categories (bool): Whether sort categories in annotations. Only
+            used for `Objects365V1Dataset`. Defaults to False.
+        use_mp_eval (bool): Whether to use mul-processing evaluation
+
+    """
+
+    default_prefix: Optional[str] = "coco"
+
+    def __init__(
+        self,
+        ann_file: Optional[str] = None,
+        metric: Union[str, List[str]] = "bbox",
+        classwise: bool = False,
+        proposal_nums: Sequence[int] = (100, 300, 1000),
+        iou_thrs: Optional[Union[float, Sequence[float]]] = None,
+        metric_items: Optional[Sequence[str]] = None,
+        format_only: bool = False,
+        outfile_prefix: Optional[str] = None,
+        file_client_args: dict = None,
+        backend_args: dict = None,
+        collect_device: str = "cpu",
+        prefix: Optional[str] = None,
+        sort_categories: bool = False,
+        use_mp_eval: bool = False,
+        use_faster: bool = False,
+    ) -> None:
+        super().__init__(collect_device=collect_device, prefix=prefix)
+        # coco evaluation metrics
+        self.metrics = metric if isinstance(metric, list) else [metric]
+        allowed_metrics = ["bbox", "segm", "proposal", "proposal_fast"]
+        for metric in self.metrics:
+            if metric not in allowed_metrics:
+                raise KeyError(
+                    "metric should be one of 'bbox', 'segm', 'proposal', "
+                    f"'proposal_fast', but got {metric}."
+                )
+
+        # do class wise evaluation, default False
+        self.classwise = classwise
+        # whether to use multi processing evaluation, default False
+        self.use_mp_eval = use_mp_eval
+        # faster_coco_eval
+        self.use_faster = use_faster
+
+        # proposal_nums used to compute recall or precision.
+        self.proposal_nums = list(proposal_nums)
+
+        # iou_thrs used to compute recall or precision.
+        if iou_thrs is None:
+            iou_thrs = np.linspace(
+                0.5, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True
+            )
+        self.iou_thrs = iou_thrs
+        self.metric_items = metric_items
+        self.format_only = format_only
+        if self.format_only:
+            assert outfile_prefix is not None, "outfile_prefix must be not"
+            "None when format_only is True, otherwise the result files will"
+            "be saved to a temp directory which will be cleaned up at the end."
+
+        self.outfile_prefix = outfile_prefix
+
+        self.backend_args = backend_args
+        if file_client_args is not None:
+            raise RuntimeError(
+                "The `file_client_args` is deprecated, "
+                "please use `backend_args` instead, please refer to"
+                "https://github.com/open-mmlab/mmdetection/blob/main/configs/_base_/datasets/coco_detection.py"  # noqa: E501
+            )
+
+        # if ann_file is not specified,
+        # initialize coco api with the converted dataset
+        if ann_file is not None:
+            with get_local_path(
+                ann_file, backend_args=self.backend_args
+            ) as local_path:
+                if self.use_faster:
+                    self._coco_api = COCO_faster(local_path)
+                else:
+                    self._coco_api = COCO(local_path)
+                if sort_categories:
+                    # 'categories' list in objects365_train.json and
+                    # objects365_val.json is inconsistent, need sort
+                    # list(or dict) before get cat_ids.
+                    cats = self._coco_api.cats
+                    sorted_cats = {i: cats[i] for i in sorted(cats)}
+                    self._coco_api.cats = sorted_cats
+                    categories = self._coco_api.dataset["categories"]
+                    sorted_categories = sorted(
+                        categories, key=lambda i: i["id"]
+                    )
+                    self._coco_api.dataset["categories"] = sorted_categories
+        else:
+            self._coco_api = None
+
+        # handle dataset lazy init
+        self.cat_ids = None
+        self.img_ids = None
+
+    def fast_eval_recall(
+        self,
+        results: List[dict],
+        proposal_nums: Sequence[int],
+        iou_thrs: Sequence[float],
+        logger: Optional[MMLogger] = None,
+    ) -> np.ndarray:
+        """Evaluate proposal recall with COCO's fast_eval_recall.
+
+        Args:
+            results (List[dict]): Results of the dataset.
+            proposal_nums (Sequence[int]): Proposal numbers used for
+                evaluation.
+            iou_thrs (Sequence[float]): IoU thresholds used for evaluation.
+            logger (MMLogger, optional): Logger used for logging the recall
+                summary.
+        Returns:
+            np.ndarray: Averaged recall results.
+
+        """
+        gt_bboxes = []
+        pred_bboxes = [result["bboxes"] for result in results]
+        for i in range(len(self.img_ids)):
+            ann_ids = self._coco_api.get_ann_ids(img_ids=self.img_ids[i])
+            ann_info = self._coco_api.load_anns(ann_ids)
+            if len(ann_info) == 0:
+                gt_bboxes.append(np.zeros((0, 4)))
+                continue
+            bboxes = []
+            for ann in ann_info:
+                if ann.get("ignore", False) or ann["iscrowd"]:
+                    continue
+                x1, y1, w, h = ann["bbox"]
+                bboxes.append([x1, y1, x1 + w, y1 + h])
+            bboxes = np.array(bboxes, dtype=np.float32)
+            if bboxes.shape[0] == 0:
+                bboxes = np.zeros((0, 4))
+            gt_bboxes.append(bboxes)
+
+        recalls = eval_recalls(
+            gt_bboxes, pred_bboxes, proposal_nums, iou_thrs, logger=logger
+        )
+        ar = recalls.mean(axis=1)
+        return ar
+
+    def xyxy2xywh(self, bbox: np.ndarray) -> list:
+        """Convert ``xyxy`` style bounding boxes to ``xywh`` style for COCO
+        evaluation.
+
+        Args:
+            bbox (numpy.ndarray): The bounding boxes, shape (4, ), in
+                ``xyxy`` order.
+
+        Returns:
+            list[float]: The converted bounding boxes, in ``xywh`` order.
+
+        """
+
+        _bbox: List = bbox.tolist()
+        return [
+            _bbox[0],
+            _bbox[1],
+            _bbox[2] - _bbox[0],
+            _bbox[3] - _bbox[1],
+        ]
+
+    def results2json(
+        self, results: Sequence[dict], outfile_prefix: str
+    ) -> dict:
+        """Dump the detection results to a COCO style json file.
+
+        There are 3 types of results: proposals, bbox predictions, mask
+        predictions, and they have different data types. This method will
+        automatically recognize the type, and dump them to json files.
+
+        Args:
+            results (Sequence[dict]): Testing results of the
+                dataset.
+            outfile_prefix (str): The filename prefix of the json files. If the
+                prefix is "somepath/xxx", the json files will be named
+                "somepath/xxx.bbox.json", "somepath/xxx.segm.json",
+                "somepath/xxx.proposal.json".
+
+        Returns:
+            dict: Possible keys are "bbox", "segm", "proposal", and
+            values are corresponding filenames.
+
+        """
+        bbox_json_results = []
+        segm_json_results = [] if "masks" in results[0] else None
+        for idx, result in enumerate(results):
+            image_id = result.get("img_id", idx)
+            labels = result["labels"]
+            bboxes = result["bboxes"]
+            scores = result["scores"]
+            # bbox results
+            for i, label in enumerate(labels):
+                data = dict()
+                data["image_id"] = image_id
+                data["bbox"] = self.xyxy2xywh(bboxes[i])
+                data["score"] = float(scores[i])
+                data["category_id"] = self.cat_ids[label]
+                bbox_json_results.append(data)
+
+            if segm_json_results is None:
+                continue
+
+            # segm results
+            masks = result["masks"]
+            mask_scores = result.get("mask_scores", scores)
+            for i, label in enumerate(labels):
+                data = dict()
+                data["image_id"] = image_id
+                data["bbox"] = self.xyxy2xywh(bboxes[i])
+                data["score"] = float(mask_scores[i])
+                data["category_id"] = self.cat_ids[label]
+                if isinstance(masks[i]["counts"], bytes):
+                    masks[i]["counts"] = masks[i]["counts"].decode()
+                data["segmentation"] = masks[i]
+                segm_json_results.append(data)
+
+        result_files = dict()
+        result_files["bbox"] = f"{outfile_prefix}.bbox.json"
+        result_files["proposal"] = f"{outfile_prefix}.bbox.json"
+        dump(bbox_json_results, result_files["bbox"])
+
+        if segm_json_results is not None:
+            result_files["segm"] = f"{outfile_prefix}.segm.json"
+            dump(segm_json_results, result_files["segm"])
+
+        return result_files
+
+    def gt_to_coco_json(
+        self, gt_dicts: Sequence[dict], outfile_prefix: str
+    ) -> str:
+        """Convert ground truth to coco format json file.
+
+        Args:
+            gt_dicts (Sequence[dict]): Ground truth of the dataset.
+            outfile_prefix (str): The filename prefix of the json files. If the
+                prefix is "somepath/xxx", the json file will be named
+                "somepath/xxx.gt.json".
+        Returns:
+            str: The filename of the json file.
+
+        """
+        categories = [
+            dict(id=id, name=name)
+            for id, name in enumerate(self.dataset_meta["classes"])
+        ]
+        image_infos = []
+        annotations = []
+
+        for idx, gt_dict in enumerate(gt_dicts):
+            img_id = gt_dict.get("img_id", idx)
+            image_info = dict(
+                id=img_id,
+                width=gt_dict["width"],
+                height=gt_dict["height"],
+                file_name="",
+            )
+            image_infos.append(image_info)
+            for ann in gt_dict["anns"]:
+                label = ann["bbox_label"]
+                bbox = ann["bbox"]
+                coco_bbox = [
+                    bbox[0],
+                    bbox[1],
+                    bbox[2] - bbox[0],
+                    bbox[3] - bbox[1],
+                ]
+
+                annotation = dict(
+                    id=len(annotations)
+                    + 1,  # coco api requires id starts with 1
+                    image_id=img_id,
+                    bbox=coco_bbox,
+                    iscrowd=ann.get("ignore_flag", 0),
+                    category_id=int(label),
+                    area=coco_bbox[2] * coco_bbox[3],
+                )
+                if ann.get("mask", None):
+                    mask = ann["mask"]
+                    # area = mask_util.area(mask)
+                    if isinstance(mask, dict) and isinstance(
+                        mask["counts"], bytes
+                    ):
+                        mask["counts"] = mask["counts"].decode()
+                    annotation["segmentation"] = mask
+                    # annotation['area'] = float(area)
+                annotations.append(annotation)
+
+        info = dict(
+            date_created=str(datetime.datetime.now()),
+            description="Coco json file converted by mmdet CocoMetric.",
+        )
+        coco_json = dict(
+            info=info,
+            images=image_infos,
+            categories=categories,
+            licenses=None,
+        )
+        if len(annotations) > 0:
+            coco_json["annotations"] = annotations
+        converted_json_path = f"{outfile_prefix}.gt.json"
+        dump(coco_json, converted_json_path)
+        return converted_json_path
+
+    # TODO: data_batch is no longer needed, consider adjusting the
+    #  parameter position
+    def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None:
+        """Process one batch of data samples and predictions. The processed
+        results should be stored in ``self.results``, which will be used to
+        compute the metrics when all batches have been processed.
+
+        Args:
+            data_batch (dict): A batch of data from the dataloader.
+            data_samples (Sequence[dict]): A batch of data samples that
+                contain annotations and predictions.
+
+        """
+        for data_sample in data_samples:
+            result = dict()
+            pred = data_sample["pred_instances"]
+            result["img_id"] = data_sample["img_id"]
+            result["bboxes"] = pred["bboxes"].cpu().numpy()
+            result["scores"] = pred["scores"].cpu().numpy()
+            result["labels"] = pred["labels"].cpu().numpy()
+            # encode mask to RLE
+            if "masks" in pred:
+                result["masks"] = (
+                    encode_mask_results(pred["masks"].detach().cpu().numpy())
+                    if isinstance(pred["masks"], torch.Tensor)
+                    else pred["masks"]
+                )
+            # some detectors use different scores for bbox and mask
+            if "mask_scores" in pred:
+                result["mask_scores"] = pred["mask_scores"].cpu().numpy()
+
+            # parse gt
+            gt = dict()
+            gt["width"] = data_sample["ori_shape"][1]
+            gt["height"] = data_sample["ori_shape"][0]
+            gt["img_id"] = data_sample["img_id"]
+            if self._coco_api is None:
+                # TODO: Need to refactor to support LoadAnnotations
+                assert "instances" in data_sample, (
+                    "ground truth is required for evaluation when "
+                    "`ann_file` is not provided"
+                )
+                gt["anns"] = data_sample["instances"]
+            # add converted result to the results list
+            self.results.append((gt, result))
+
+    def compute_metrics(self, results: list) -> Dict[str, float]:
+        """Compute the metrics from processed results.
+
+        Args:
+            results (list): The processed results of each batch.
+
+        Returns:
+            Dict[str, float]: The computed metrics. The keys are the names of
+            the metrics, and the values are corresponding results.
+
+        """
+        logger: MMLogger = MMLogger.get_current_instance()
+
+        # split gt and prediction list
+        gts, preds = zip(*results)
+
+        tmp_dir = None
+        if self.outfile_prefix is None:
+            tmp_dir = tempfile.TemporaryDirectory()
+            outfile_prefix = osp.join(tmp_dir.name, "results")
+        else:
+            outfile_prefix = self.outfile_prefix
+
+        if self._coco_api is None:
+            # use converted gt json file to initialize coco api
+            logger.info("Converting ground truth to coco format...")
+            coco_json_path = self.gt_to_coco_json(
+                gt_dicts=gts, outfile_prefix=outfile_prefix
+            )
+
+            if self.use_faster:
+                self._coco_api = COCO_faster(coco_json_path)
+            else:
+                self._coco_api = COCO(coco_json_path)
+
+        # handle lazy init
+        if self.cat_ids is None:
+            self.cat_ids = self._coco_api.get_cat_ids(
+                cat_names=self.dataset_meta["classes"]
+            )
+        if self.img_ids is None:
+            self.img_ids = self._coco_api.get_img_ids()
+
+        # convert predictions to coco format and dump to json file
+        result_files = self.results2json(preds, outfile_prefix)
+
+        eval_results = OrderedDict()
+        if self.format_only:
+            logger.info(f"results are saved in {osp.dirname(outfile_prefix)}")
+            return eval_results
+
+        for metric in self.metrics:
+            logger.info(f"Evaluating {metric}...")
+
+            # TODO: May refactor fast_eval_recall to an independent metric?
+            # fast eval recall
+            if metric == "proposal_fast":
+                ar = self.fast_eval_recall(
+                    preds, self.proposal_nums, self.iou_thrs, logger=logger
+                )
+                log_msg = []
+                for i, num in enumerate(self.proposal_nums):
+                    eval_results[f"AR@{num}"] = ar[i]
+                    log_msg.append(f"\nAR@{num}\t{ar[i]:.4f}")
+                log_msg = "".join(log_msg)
+                logger.info(log_msg)
+                continue
+
+            # evaluate proposal, bbox and segm
+            iou_type = "bbox" if metric == "proposal" else metric
+            if metric not in result_files:
+                raise KeyError(f"{metric} is not in results")
+            try:
+                predictions = load(result_files[metric])
+                if iou_type == "segm":
+                    # Refer to https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/coco.py#L331  # noqa
+                    # When evaluating mask AP, if the results contain bbox,
+                    # cocoapi will use the box area instead of the mask area
+                    # for calculating the instance area. Though the overall AP
+                    # is not affected, this leads to different
+                    # small/medium/large mask AP results.
+                    for x in predictions:
+                        x.pop("bbox")
+                coco_dt = self._coco_api.loadRes(predictions)
+
+            except IndexError:
+                logger.error(
+                    "The testing results of the whole dataset is empty."
+                )
+                break
+
+            if self.use_mp_eval:
+                coco_eval = COCOevalMP(self._coco_api, coco_dt, iou_type)
+            else:
+                if self.use_faster:
+                    coco_eval = COCOeval_faster(
+                        self._coco_api, coco_dt, iou_type, print_function=print
+                    )
+                else:
+                    coco_eval = COCOeval(self._coco_api, coco_dt, iou_type)
+
+            coco_eval.params.catIds = self.cat_ids
+            coco_eval.params.imgIds = self.img_ids
+            coco_eval.params.maxDets = list(self.proposal_nums)
+            coco_eval.params.iouThrs = self.iou_thrs
+
+            # mapping of cocoEval.stats
+            coco_metric_names = {
+                "mAP": 0,
+                "mAP_50": 1,
+                "mAP_75": 2,
+                "mAP_s": 3,
+                "mAP_m": 4,
+                "mAP_l": 5,
+                "AR@100": 6,
+                "AR@300": 7,
+                "AR@1000": 8,
+                "AR_s@1000": 9,
+                "AR_m@1000": 10,
+                "AR_l@1000": 11,
+            }
+            metric_items = self.metric_items
+            if metric_items is not None:
+                for metric_item in metric_items:
+                    if metric_item not in coco_metric_names:
+                        raise KeyError(
+                            f'metric item "{metric_item}" is not supported'
+                        )
+
+            if metric == "proposal":
+                coco_eval.params.useCats = 0
+                coco_eval.evaluate()
+                coco_eval.accumulate()
+                coco_eval.summarize()
+                if metric_items is None:
+                    metric_items = [
+                        "AR@100",
+                        "AR@300",
+                        "AR@1000",
+                        "AR_s@1000",
+                        "AR_m@1000",
+                        "AR_l@1000",
+                    ]
+
+                for item in metric_items:
+                    val = float(
+                        f"{coco_eval.stats[coco_metric_names[item]]:.3f}"
+                    )
+                    eval_results[item] = val
+            else:
+                coco_eval.evaluate()
+                coco_eval.accumulate()
+                coco_eval.summarize()
+                if self.classwise:  # Compute per-category AP
+                    # Compute per-category AP
+                    # from https://github.com/facebookresearch/detectron2/
+                    precisions = coco_eval.eval["precision"]
+                    # precision: (iou, recall, cls, area range, max dets)
+                    assert len(self.cat_ids) == precisions.shape[2]
+
+                    results_per_category = []
+                    for idx, cat_id in enumerate(self.cat_ids):
+                        t = []
+                        # area range index 0: all area ranges
+                        # max dets index -1: typically 100 per image
+                        nm = self._coco_api.loadCats(cat_id)[0]
+                        precision = precisions[:, :, idx, 0, -1]
+                        precision = precision[precision > -1]
+                        if precision.size:
+                            ap = np.mean(precision)
+                        else:
+                            ap = float("nan")
+                        t.append(f'{nm["name"]}')
+                        t.append(f"{round(ap, 3)}")
+                        eval_results[f'{nm["name"]}_precision'] = round(ap, 3)
+
+                        # indexes of IoU  @50 and @75
+                        for iou in [0, 5]:
+                            precision = precisions[iou, :, idx, 0, -1]
+                            precision = precision[precision > -1]
+                            if precision.size:
+                                ap = np.mean(precision)
+                            else:
+                                ap = float("nan")
+                            t.append(f"{round(ap, 3)}")
+
+                        # indexes of area of small, median and large
+                        for area in [1, 2, 3]:
+                            precision = precisions[:, :, idx, area, -1]
+                            precision = precision[precision > -1]
+                            if precision.size:
+                                ap = np.mean(precision)
+                            else:
+                                ap = float("nan")
+                            t.append(f"{round(ap, 3)}")
+                        results_per_category.append(tuple(t))
+
+                    num_columns = len(results_per_category[0])
+                    results_flatten = list(
+                        itertools.chain(*results_per_category)
+                    )
+                    headers = [
+                        "category",
+                        "mAP",
+                        "mAP_50",
+                        "mAP_75",
+                        "mAP_s",
+                        "mAP_m",
+                        "mAP_l",
+                    ]
+                    results_2d = itertools.zip_longest(
+                        *[
+                            results_flatten[i::num_columns]
+                            for i in range(num_columns)
+                        ]
+                    )
+                    table_data = [headers]
+                    table_data += [result for result in results_2d]
+                    table = AsciiTable(table_data)
+                    logger.info("\n" + table.table)
+
+                if metric_items is None:
+                    metric_items = [
+                        "mAP",
+                        "mAP_50",
+                        "mAP_75",
+                        "mAP_s",
+                        "mAP_m",
+                        "mAP_l",
+                    ]
+
+                for metric_item in metric_items:
+                    key = f"{metric}_{metric_item}"
+                    val = coco_eval.stats[coco_metric_names[metric_item]]
+                    eval_results[key] = float(f"{round(val, 3)}")
+
+                ap = coco_eval.stats[:6]
+                logger.info(
+                    f"{metric}_mAP_copypaste: {ap[0]:.3f} "
+                    f"{ap[1]:.3f} {ap[2]:.3f} {ap[3]:.3f} "
+                    f"{ap[4]:.3f} {ap[5]:.3f}"
+                )
+
+        if tmp_dir is not None:
+            tmp_dir.cleanup()
+        return eval_results
+
+
+if __name__ == "__main__":
+    import argparse
+    import pickle
+    import time
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("tmp_results", type=str, help="path to tmp_results.pkl")
+    parser.add_argument(
+        "dataset_meta", type=str, help="path to dataset_meta.pkl"
+    )
+    parser.add_argument("--metric", type=str, help="metric", default="bbox")
+    parser.add_argument(
+        "--use_faster", action=argparse.BooleanOptionalAction, default=False
+    )
+
+    args = parser.parse_args()
+
+    with open(args.tmp_results, "rb") as fd:
+        tmp_results = pickle.load(fd)
+
+    with open(args.dataset_meta, "rb") as fd:
+        dataset_meta = pickle.load(fd)
+
+    print(args)
+
+    metric = CocoMetric(metric=args.metric, use_faster=args.use_faster)
+    metric.dataset_meta = dataset_meta
+    metric.results = tmp_results
+
+    ts = time.time()
+    metric.evaluate(len(tmp_results))
+    te = time.time()
+
+    print(f"Total evaluate: {te - ts:.2f}s")
diff --git a/examples/comparison/mmdet/comparison.ipynb b/examples/comparison/mmdet/comparison.ipynb
new file mode 100644
index 0000000..54120c8
--- /dev/null
+++ b/examples/comparison/mmdet/comparison.ipynb
@@ -0,0 +1,589 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Install MMDetection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<https://mmdetection.readthedocs.io/en/latest/get_started.html>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download COCO VAL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bc2a4389",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2024-04-13 21:58:23--  http://images.cocodataset.org/annotations/annotations_trainval2017.zip\n",
+      "Resolving images.cocodataset.org (images.cocodataset.org)... 3.5.28.198, 3.5.25.88, 52.217.14.124, ...\n",
+      "Connecting to images.cocodataset.org (images.cocodataset.org)|3.5.28.198|:80... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 252907541 (241M) [application/zip]\n",
+      "Saving to: ‘COCO/DIR/annotations_trainval2017.zip’\n",
+      "\n",
+      "annotations_trainva 100%[===================>] 241.19M  14.9MB/s    in 26s     \n",
+      "\n",
+      "2024-04-13 21:58:50 (9.27 MB/s) - ‘COCO/DIR/annotations_trainval2017.zip’ saved [252907541/252907541]\n",
+      "\n",
+      "--2024-04-13 21:58:50--  http://images.cocodataset.org/zips/val2017.zip\n",
+      "Resolving images.cocodataset.org (images.cocodataset.org)... 3.5.29.24, 52.216.52.249, 3.5.29.27, ...\n",
+      "Connecting to images.cocodataset.org (images.cocodataset.org)|3.5.29.24|:80... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 815585330 (778M) [application/zip]\n",
+      "Saving to: ‘COCO/DIR/val2017.zip’\n",
+      "\n",
+      "val2017.zip         100%[===================>] 777.80M  13.2MB/s    in 67s     \n",
+      "\n",
+      "2024-04-13 21:59:58 (11.6 MB/s) - ‘COCO/DIR/val2017.zip’ saved [815585330/815585330]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!wget -P COCO/DIR/ http://images.cocodataset.org/annotations/annotations_trainval2017.zip\n",
+    "\n",
+    "!wget -P COCO/DIR/ http://images.cocodataset.org/zips/val2017.zip"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b94cb3d",
+   "metadata": {},
+   "source": [
+    "## Unzip COCO VAL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "94d9a6c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!unzip -qq COCO/DIR/annotations_trainval2017.zip -d COCO/DIR/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c266f572",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!unzip -qq COCO/DIR/val2017.zip -d COCO/DIR/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee83ac7b",
+   "metadata": {},
+   "source": [
+    "## Download model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b6f6860f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "config_file='/home/mixaill76/.local/lib/python3.10/site-packages/mmdet/.mim/configs/rtmdet/rtmdet-ins_tiny_8xb32-300e_coco.py'\n",
+      "model_file='https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet-ins_tiny_8xb32-300e_coco/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth'\n",
+      "--2024-04-13 22:13:51--  https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet-ins_tiny_8xb32-300e_coco/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth\n",
+      "Resolving download.openmmlab.com (download.openmmlab.com)... 104.166.182.22, 104.166.182.24, 104.166.182.27, ...\n",
+      "Connecting to download.openmmlab.com (download.openmmlab.com)|104.166.182.22|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 22757492 (22M) [application/octet-stream]\n",
+      "Saving to: ‘model/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth’\n",
+      "\n",
+      "rtmdet-ins_tiny_8xb 100%[===================>]  21.70M  6.14MB/s    in 4.2s    \n",
+      "\n",
+      "2024-04-13 22:13:57 (5.21 MB/s) - ‘model/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth’ saved [22757492/22757492]\n",
+      "\n",
+      "total 22M\n",
+      "drwxrwxrwx 2 mixaill76 mixaill76 4.0K Apr 13 22:13 .\n",
+      "drwxr-xr-x 4 mixaill76 mixaill76 4.0K Apr 13 22:13 ..\n",
+      "-rw-r--r-- 1 mixaill76 mixaill76  16K Apr 13 22:13 rtmdet-ins_tiny_8xb32-300e_coco.py\n",
+      "-rw-r--r-- 1 mixaill76 mixaill76  22M Dec 19  2022 rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mmdet\n",
+    "import mmengine\n",
+    "import os\n",
+    "import os.path as osp\n",
+    "\n",
+    "config_dir = osp.dirname(mmdet.__file__)\n",
+    "sub_config = \"configs/rtmdet/rtmdet-ins_tiny_8xb32-300e_coco.py\"\n",
+    "config_file = osp.join(config_dir, \".mim\", sub_config)\n",
+    "cfg = mmengine.Config.fromfile(config_file)\n",
+    "\n",
+    "model_file = \"https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet-ins_tiny_8xb32-300e_coco/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth\"\n",
+    "\n",
+    "print(f\"{config_file=}\")\n",
+    "print(f\"{model_file=}\")\n",
+    "\n",
+    "!mkdir -p -m 777 model\n",
+    "\n",
+    "cfg.dump(osp.join(\"model\", osp.basename(config_file)))\n",
+    "!wget -P model/ {model_file}\n",
+    "\n",
+    "!ls -lah model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "313f1978",
+   "metadata": {},
+   "source": [
+    "## Validate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "eebcff84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mmdet.apis import inference_detector, init_detector\n",
+    "from mmengine.registry import init_default_scope\n",
+    "from mmdet.datasets import CocoDataset\n",
+    "import tqdm\n",
+    "import copy\n",
+    "import pickle\n",
+    "from coco_metric import CocoMetric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c2f5fedd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init_default_scope(\"mmdet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1318e70c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loads checkpoint by local backend from path: ./model/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = init_detector(\n",
+    "    \"./model/rtmdet-ins_tiny_8xb32-300e_coco.py\",\n",
+    "    \"./model/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth\",\n",
+    "    device=\"cuda\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1f7878d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading annotations into memory...\n",
+      "Done (t=0.45s)\n",
+      "creating index...\n",
+      "index created!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "5000"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipeline = [\n",
+    "    dict(type=\"LoadImageFromFile\"),\n",
+    "    dict(type=\"mmdet.LoadAnnotations\", with_bbox=True),\n",
+    "    dict(\n",
+    "        type=\"mmdet.PackDetInputs\",\n",
+    "        meta_keys=(\n",
+    "            \"img\",\n",
+    "            \"img_id\",\n",
+    "            \"img_path\",\n",
+    "            \"ori_shape\",\n",
+    "            \"img_shape\",\n",
+    "            \"instances\",\n",
+    "        ),\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "dataset = CocoDataset(\n",
+    "    data_root=\"./COCO/DIR/\",\n",
+    "    ann_file=\"annotations/instances_val2017.json\",\n",
+    "    data_prefix=dict(img=\"val2017/\"),\n",
+    "    pipeline=pipeline,\n",
+    ")\n",
+    "len(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "24d1832a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/500 [00:00<?, ?it/s]/home/mixaill76/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:456: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)\n",
+      "  return F.conv2d(input, weight, bias, self.stride,\n",
+      "/home/mixaill76/.local/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3526.)\n",
+      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
+      "100%|██████████| 500/500 [00:17<00:00, 29.16it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_samples = []\n",
+    "\n",
+    "for i in tqdm.tqdm(range(500)):\n",
+    "    item = dataset[i]\n",
+    "    data_sample = item[\"data_samples\"]\n",
+    "\n",
+    "    img = data_sample.img\n",
+    "    result = inference_detector(model, img)\n",
+    "\n",
+    "    for key in result.pred_instances.all_keys():\n",
+    "        result.pred_instances[key] = result.pred_instances[key].detach().cpu()\n",
+    "\n",
+    "    for bad_key in set(result.pred_instances.all_keys()) - {\n",
+    "        \"masks\",\n",
+    "        \"scores\",\n",
+    "        \"labels\",\n",
+    "        \"bboxes\",\n",
+    "    }:\n",
+    "        del result.pred_instances[bad_key]\n",
+    "\n",
+    "    data_sample.pred_instances = result.pred_instances\n",
+    "\n",
+    "    data_samples.append(data_sample.to_dict())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98376574",
+   "metadata": {},
+   "source": [
+    "## Generate tmp results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "63adfed7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metric = CocoMetric(metric=\"bbox\")\n",
+    "metric.dataset_meta = model.dataset_meta\n",
+    "metric.process({}, data_samples)\n",
+    "tmp_results = copy.deepcopy(metric.results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c1bd202b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"COCO/tmp_results.pkl\", \"wb\") as fd:\n",
+    "    pickle.dump(tmp_results, fd)\n",
+    "\n",
+    "with open(\"COCO/dataset_meta.pkl\", \"wb\") as fd:\n",
+    "    pickle.dump(model.dataset_meta, fd)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50207c98",
+   "metadata": {},
+   "source": [
+    "## Test basic bbox pycocotools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0782d702",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Namespace(tmp_results='COCO/tmp_results.pkl', dataset_meta='COCO/dataset_meta.pkl', metric='bbox', use_faster=False)\n",
+      "04/13 23:25:20 - mmengine - \u001b[4m\u001b[97mINFO\u001b[0m - Converting ground truth to coco format...\n",
+      "loading annotations into memory...\n",
+      "Done (t=0.02s)\n",
+      "creating index...\n",
+      "index created!\n",
+      "04/13 23:25:21 - mmengine - \u001b[4m\u001b[97mINFO\u001b[0m - Evaluating bbox...\n",
+      "Loading and preparing results...\n",
+      "DONE (t=0.02s)\n",
+      "creating index...\n",
+      "index created!\n",
+      "Running per image evaluation...\n",
+      "Evaluate annotation type *bbox*\n",
+      "DONE (t=1.72s).\n",
+      "Accumulating evaluation results...\n",
+      "DONE (t=0.61s).\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.452\n",
+      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.631\n",
+      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.492\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.200\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.452\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.594\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.609\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.609\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.609\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.335\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.623\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.738\n",
+      "04/13 23:25:24 - mmengine - \u001b[4m\u001b[97mINFO\u001b[0m - bbox_mAP_copypaste: 0.452 0.631 0.492 0.200 0.452 0.594\n",
+      "Total evaluate: 3.57s\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python3 coco_metric.py 'COCO/tmp_results.pkl' 'COCO/dataset_meta.pkl' --metric \"bbox\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba8c9c65",
+   "metadata": {},
+   "source": [
+    "## Test bbox with Faster Coco Eval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c3febd51",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Namespace(tmp_results='COCO/tmp_results.pkl', dataset_meta='COCO/dataset_meta.pkl', metric='bbox', use_faster=True)\n",
+      "04/13 23:25:29 - mmengine - \u001b[4m\u001b[97mINFO\u001b[0m - Converting ground truth to coco format...\n",
+      "04/13 23:25:30 - mmengine - \u001b[4m\u001b[97mINFO\u001b[0m - Evaluating bbox...\n",
+      "Evaluate annotation type *bbox*\n",
+      "COCOeval_opt.evaluate() finished...\n",
+      "DONE (t=0.59s).\n",
+      "Accumulating evaluation results...\n",
+      "COCOeval_opt.accumulate() finished...\n",
+      "DONE (t=0.15s).\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.452\n",
+      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.631\n",
+      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.492\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.200\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.452\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.594\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.609\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.609\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.609\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.335\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.623\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.738\n",
+      " Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.840\n",
+      " Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.660\n",
+      "04/13 23:25:31 - mmengine - \u001b[4m\u001b[97mINFO\u001b[0m - bbox_mAP_copypaste: 0.452 0.631 0.492 0.200 0.452 0.594\n",
+      "Total evaluate: 2.03s\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python3 coco_metric.py 'COCO/tmp_results.pkl' 'COCO/dataset_meta.pkl' --metric \"bbox\" --use_faster"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27858d28",
+   "metadata": {},
+   "source": [
+    "## Test basic segm pycocotools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b9d73bf0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Namespace(tmp_results='COCO/tmp_results.pkl', dataset_meta='COCO/dataset_meta.pkl', metric='segm', use_faster=False)\n",
+      "04/13 23:25:39 - mmengine - \u001b[4m\u001b[97mINFO\u001b[0m - Converting ground truth to coco format...\n",
+      "loading annotations into memory...\n",
+      "Done (t=0.02s)\n",
+      "creating index...\n",
+      "index created!\n",
+      "04/13 23:25:40 - mmengine - \u001b[4m\u001b[97mINFO\u001b[0m - Evaluating segm...\n",
+      "Loading and preparing results...\n",
+      "DONE (t=0.18s)\n",
+      "creating index...\n",
+      "index created!\n",
+      "Running per image evaluation...\n",
+      "Evaluate annotation type *segm*\n",
+      "DONE (t=2.07s).\n",
+      "Accumulating evaluation results...\n",
+      "DONE (t=0.61s).\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.391\n",
+      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.601\n",
+      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.423\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.133\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.377\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.553\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.524\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.524\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.524\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.258\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.531\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.659\n",
+      "04/13 23:25:43 - mmengine - \u001b[4m\u001b[97mINFO\u001b[0m - segm_mAP_copypaste: 0.391 0.601 0.423 0.133 0.377 0.553\n",
+      "Total evaluate: 4.16s\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python3 coco_metric.py 'COCO/tmp_results.pkl' 'COCO/dataset_meta.pkl' --metric \"segm\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62051d38",
+   "metadata": {},
+   "source": [
+    "## Test segm with Faster Coco Eval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e373e750",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Namespace(tmp_results='COCO/tmp_results.pkl', dataset_meta='COCO/dataset_meta.pkl', metric='segm', use_faster=True)\n",
+      "04/13 23:25:48 - mmengine - \u001b[4m\u001b[97mINFO\u001b[0m - Converting ground truth to coco format...\n",
+      "04/13 23:25:48 - mmengine - \u001b[4m\u001b[97mINFO\u001b[0m - Evaluating segm...\n",
+      "Evaluate annotation type *segm*\n",
+      "COCOeval_opt.evaluate() finished...\n",
+      "DONE (t=0.76s).\n",
+      "Accumulating evaluation results...\n",
+      "COCOeval_opt.accumulate() finished...\n",
+      "DONE (t=0.15s).\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.391\n",
+      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.601\n",
+      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.423\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.133\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.377\n",
+      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.553\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.524\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.524\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.524\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.258\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.531\n",
+      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.659\n",
+      " Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.799\n",
+      " Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.560\n",
+      "04/13 23:25:50 - mmengine - \u001b[4m\u001b[97mINFO\u001b[0m - segm_mAP_copypaste: 0.391 0.601 0.423 0.133 0.377 0.553\n",
+      "Total evaluate: 2.41s\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python3 coco_metric.py 'COCO/tmp_results.pkl' 'COCO/dataset_meta.pkl' --metric \"segm\" --use_faster"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "922d0220",
+   "metadata": {},
+   "source": [
+    "## Summary for 500 imgs\n",
+    "\n",
+    "| Type | COCOeval    | COCOeval_faster | Profit      |\n",
+    "| ---- | ----------- | --------------- | ----------- |\n",
+    "| bbox | 3.57s | 2.03s      | ~1.7x faster |\n",
+    "| segm | 4.16s | 2.41s     | ~1.7x faster   |\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/comparison/mmdet/eval_det_model.ipynb b/examples/comparison/mmdet/eval_det_model.ipynb
new file mode 100644
index 0000000..1ac9e70
--- /dev/null
+++ b/examples/comparison/mmdet/eval_det_model.ipynb
@@ -0,0 +1,238 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ed95f83-e863-4b7a-a951-85f667d4f082",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "sys.path.append(\"../../../mmlab_utils/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28769596-7530-45be-be13-9f4adb51115c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "\n",
+    "logging.basicConfig(level=logging.ERROR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bccfaf9-709e-4996-92f3-a8d8dd175d0f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from mmengine.registry import init_default_scope\n",
+    "\n",
+    "init_default_scope(\"mmdet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bb11abe-e52d-43db-8721-72fb026ec8d8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from collections import defaultdict\n",
+    "import torch\n",
+    "from shapely.geometry import Polygon\n",
+    "from tqdm import tqdm\n",
+    "from mmlab_utils.mmdet_utils import Coco2Dataset\n",
+    "from mmlab_utils.mmdet_utils.apis.base_loader import BasePredictor\n",
+    "from mmlab_utils.mmdet_utils.apis.base_loader import find_last_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f1a82a7-a959-4da0-9b7b-e0955de3f9df",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "model_folder = \"/data/projects/yolov8_v1/YOLOV8_v1_scale_640/\"\n",
+    "\n",
+    "config_file, checkpoint = find_last_model(model_folder)\n",
+    "\n",
+    "print(f\"{config_file=}\")\n",
+    "print(f\"{checkpoint=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d42cea46-135a-4b90-b20d-2e22dc855d10",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "detector = BasePredictor(model_folder, min_score=0.7, use_cpu=False)\n",
+    "detector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c437316e-9510-464f-89a0-7a54cb661388",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pipeline = [\n",
+    "    dict(type=\"LoadImageFromFile\"),\n",
+    "    dict(type=\"mmdet.LoadAnnotations\", with_bbox=True),\n",
+    "    dict(\n",
+    "        type=\"mmdet.PackDetInputs\",\n",
+    "        meta_keys=(\n",
+    "            \"img\",\n",
+    "            \"img_id\",\n",
+    "            \"img_path\",\n",
+    "            \"ori_shape\",\n",
+    "            \"img_shape\",\n",
+    "            \"instances\",\n",
+    "        ),\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "dataset = Coco2Dataset(\n",
+    "    \"../det_stones/dataset/test/\",\n",
+    "    classes=detector.inference_model.inference_model.dataset_meta[\"classes\"],\n",
+    "    pipeline=pipeline,\n",
+    "    # MAX_SIZE=detector.MAX_SIZE[0]\n",
+    ")\n",
+    "\n",
+    "len(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1eb68540-648b-4e48-9980-c9f9eae54188",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_samples = []\n",
+    "\n",
+    "for i, item in tqdm(enumerate(dataset)):\n",
+    "    data_sample = item[\"data_samples\"]\n",
+    "\n",
+    "    img = data_sample.img\n",
+    "\n",
+    "    _result_v3 = detector.inference_model.inference_detector([img])[0]\n",
+    "    data_sample.pred_instances = _result_v3.pred_instances\n",
+    "\n",
+    "    data_samples.append(data_sample.to_dict())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5756249c-8a93-4442-a6b1-ac60723701ff",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from mmlab_utils.mmdet_utils.evaluation import FasterCocoMetric\n",
+    "import numpy as np\n",
+    "\n",
+    "iou_thrs = np.linspace(\n",
+    "    0.2, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True\n",
+    ").round(2)\n",
+    "\n",
+    "metric = FasterCocoMetric(\n",
+    "    metric=\"segm\" if detector.mask_model else \"bbox\",\n",
+    "    classwise=True,\n",
+    "    iou_thrs=iou_thrs,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f87ea6f-cd81-4c0d-82a3-8e9f2c202c70",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "metric.dataset_meta = detector.inference_model.inference_model.dataset_meta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22ea3ca3-8bf7-43e3-b11e-279edab59c68",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "metric.process({}, data_samples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1017c6cb-9716-476a-a315-b9a8e8a813d3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "metrics_dict = metric.evaluate(len(data_samples))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4512cd6-4113-44a4-ab87-a76e4a26eb0d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 182f4055767879d8ace3dd164eb6a2ada6062727 Mon Sep 17 00:00:00 2001
From: MiXaiLL76 <mike.milos@yandex.ru>
Date: Sat, 13 Apr 2024 23:34:32 +0300
Subject: [PATCH 09/15] update format

---
 faster_coco_eval/core/coco.py            | 128 ++++++++++++++++-------
 faster_coco_eval/core/cocoeval.py        |  56 +++++-----
 faster_coco_eval/core/faster_eval_api.py |  42 ++++----
 faster_coco_eval/core/mask.py            |   6 +-
 faster_coco_eval/extra/display.py        |  16 ++-
 faster_coco_eval/extra/draw.py           |  36 +++++--
 faster_coco_eval/extra/extra.py          |   1 +
 faster_coco_eval/version.py              |   2 +-
 tests/basic.py                           |  22 ++--
 9 files changed, 193 insertions(+), 116 deletions(-)

diff --git a/faster_coco_eval/core/coco.py b/faster_coco_eval/core/coco.py
index 83d144c..948bfc0 100644
--- a/faster_coco_eval/core/coco.py
+++ b/faster_coco_eval/core/coco.py
@@ -8,7 +8,7 @@
 # Please visit http://mscoco.org/ for more information on COCO, including
 # for the data, paper, and tutorials. The exact format of the annotations
 # is also described on the COCO website. For example usage of the pycocotools
-# please see pycocotools_demo.ipynb. In addition to this API, please download both
+# please see pycocotools_demo.ipynb. In addition to this API, please download both # noqa: E501
 # the COCO images and annotations in order to run the demo.
 
 # An alternative to using the API is to load the annotations directly
@@ -18,7 +18,7 @@
 # captions not all functions are defined (e.g. categories are undefined).
 
 # The following API functions are defined:
-#  COCO       - COCO api class that loads COCO annotation file and prepare data structures.
+#  COCO       - COCO api class that loads COCO annotation file and prepare data structures. # noqa: E501
 #  decodeMask - Decode binary mask M encoded via run-length encoding.
 #  encodeMask - Encode binary mask M using run-length encoding.
 #  getAnnIds  - Get ann ids that satisfy given filter conditions.
@@ -48,11 +48,12 @@
 import itertools
 import json
 import logging
-import numpy as np
 import time
 import warnings
 from collections import defaultdict
 
+import numpy as np
+
 from . import mask as maskUtils
 
 logger = logging.getLogger(__name__)
@@ -64,11 +65,14 @@ def _isArrayLike(obj):
 
 class COCO:
     def __init__(self, annotation_file=None):
-        """
-        Constructor of Microsoft COCO helper class for reading and visualizing annotations.
+        """Constructor of Microsoft COCO helper class for reading and
+        visualizing annotations.
+
         :param annotation_file (str): location of annotation file
-        :param image_folder (str): location to the folder that hosts images.
+        :param image_folder (str): location to the folder that hosts
+            images.
         :return:
+
         """
         # load dataset
         self.dataset, self.anns, self.cats, self.imgs = (
@@ -84,8 +88,7 @@ def __init__(self, annotation_file=None):
             logger.debug("loading annotations into memory...")
             tic = time.time()
             if type(annotation_file) is str:
-                with open(annotation_file, "r") as f:
-                    self.dataset = json.load(f)
+                self.dataset = self.load_json(annotation_file)
             elif type(annotation_file) is dict:
                 self.dataset = annotation_file
             else:
@@ -136,21 +139,26 @@ def createIndex(self):
         self.cats = cats
 
     def info(self):
-        """
-        Print information about the annotation file.
+        """Print information about the annotation file.
+
         :return:
+
         """
         for key, value in self.dataset["info"].items():
             logger.debug("{}: {}".format(key, value))
 
     def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
-        """
-        Get ann ids that satisfy given filter conditions. default skips that filter
-        :param imgIds  (int array)     : get anns for given imgs
-               catIds  (int array)     : get anns for given cats
-               areaRng (float array)   : get anns for given area range (e.g. [0 inf])
-               iscrowd (boolean)       : get anns for given crowd label (False or True)
+        """Get ann ids that satisfy given filter conditions.
+
+        default skips that filter
+        :param imgIds (int array) : get anns for given imgs :param
+            catIds (int array) : get anns for given cats
+        :param areaRng (float array) : get anns for given area range
+            (e.g. [0 inf])
+        :param iscrowd (boolean) : get anns for given crowd label (False
+            or True)
         :return: ids (int array)       : integer array of ann ids
+
         """
         imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]
         catIds = catIds if _isArrayLike(catIds) else [catIds]
@@ -188,12 +196,15 @@ def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
         return ids
 
     def getCatIds(self, catNms=[], supNms=[], catIds=[]):
-        """
-        filtering parameters. default skips that filter.
+        """Filtering parameters.
+
+        default skips that filter.
         :param catNms (str array)  : get cats for given cat names
-        :param supNms (str array)  : get cats for given supercategory names
+        :param supNms (str array) : get cats for given supercategory
+            names
         :param catIds (int array)  : get cats for given cat ids
         :return: ids (int array)   : integer array of cat ids
+
         """
         catNms = catNms if _isArrayLike(catNms) else [catNms]
         supNms = supNms if _isArrayLike(supNms) else [supNms]
@@ -222,11 +233,12 @@ def getCatIds(self, catNms=[], supNms=[], catIds=[]):
         return ids
 
     def getImgIds(self, imgIds=[], catIds=[]):
-        """
-        Get img ids that satisfy given filter conditions.
+        """Get img ids that satisfy given filter conditions.
+
         :param imgIds (int array) : get imgs for given ids
         :param catIds (int array) : get imgs with all given cats
         :return: ids (int array)  : integer array of img ids
+
         """
         imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]
         catIds = catIds if _isArrayLike(catIds) else [catIds]
@@ -243,10 +255,11 @@ def getImgIds(self, imgIds=[], catIds=[]):
         return list(ids)
 
     def loadAnns(self, ids=[]):
-        """
-        Load anns with the specified ids.
+        """Load anns with the specified ids.
+
         :param ids (int array)       : integer ids specifying anns
         :return: anns (object array) : loaded ann objects
+
         """
         if _isArrayLike(ids):
             return [self.anns[id] for id in ids]
@@ -254,10 +267,11 @@ def loadAnns(self, ids=[]):
             return [self.anns[ids]]
 
     def loadCats(self, ids=[]):
-        """
-        Load cats with the specified ids.
+        """Load cats with the specified ids.
+
         :param ids (int array)       : integer ids specifying cats
         :return: cats (object array) : loaded cat objects
+
         """
         if _isArrayLike(ids):
             return [self.cats[id] for id in ids]
@@ -265,21 +279,29 @@ def loadCats(self, ids=[]):
             return [self.cats[ids]]
 
     def loadImgs(self, ids=[]):
-        """
-        Load anns with the specified ids.
+        """Load anns with the specified ids.
+
         :param ids (int array)       : integer ids specifying img
         :return: imgs (object array) : loaded img objects
+
         """
         if _isArrayLike(ids):
             return [self.imgs[id] for id in ids]
         elif type(ids) is int:
             return [self.imgs[ids]]
 
+    @staticmethod
+    def load_json(json_file):
+        with open(json_file) as io:
+            _data = json.load(io)
+        return _data
+
     def loadRes(self, resFile, min_score=0):
-        """
-        Load result file and return a result api object.
+        """Load result file and return a result api object.
+
         :param   resFile (str)     : file name of result file
         :return: res (obj)         : result api object
+
         """
         self.score_tresh = min_score
         res = COCO()
@@ -288,7 +310,7 @@ def loadRes(self, resFile, min_score=0):
         logger.debug("Loading and preparing results...")
         tic = time.time()
         if type(resFile) is str:
-            anns = json.load(open(resFile))
+            anns = self.load_json(resFile)
         elif type(resFile) is np.ndarray:
             anns = self.loadNumpyAnnotations(resFile)
         else:
@@ -361,11 +383,14 @@ def download(self, tarDir=None, imgIds=[]):
         warnings.warn("download deprecated in 1.3.0", DeprecationWarning)
 
     def loadNumpyAnnotations(self, data):
-        """
-        Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class}
-        :param  data (numpy.ndarray)
+        """Convert result data from array to anns.
+
+        :param data (numpy.ndarray): array [Nx7] where each row contains
+            [imageID,x1,y1,w,h,score,class]
         :return: annotations (python nested list)
+
         """
+
         logger.debug("Converting ndarray to lists...")
         assert type(data) is np.ndarray
         logger.debug(data.shape)
@@ -386,9 +411,10 @@ def loadNumpyAnnotations(self, data):
         return ann
 
     def annToRLE(self, ann):
-        """
-        Convert annotation which can be polygons, uncompressed RLE to RLE.
+        """Convert annotation which can be polygons, uncompressed RLE to RLE.
+
         :return: binary mask (numpy 2D array)
+
         """
         t = self.imgs[ann["image_id"]]
         h, w = t["height"], t["width"]
@@ -407,10 +433,38 @@ def annToRLE(self, ann):
         return rle
 
     def annToMask(self, ann):
-        """
-        Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask.
+        """Convert annotation which can be polygons, uncompressed RLE, or RLE
+        to binary mask.
+
         :return: binary mask (numpy 2D array)
+
         """
         rle = self.annToRLE(ann)
         m = maskUtils.decode(rle)
         return m
+
+    def get_ann_ids(self, img_ids=[], cat_ids=[], area_rng=[], iscrowd=None):
+        return self.getAnnIds(img_ids, cat_ids, area_rng, iscrowd)
+
+    def get_cat_ids(self, cat_names=[], sup_names=[], cat_ids=[]):
+        return self.getCatIds(cat_names, sup_names, cat_ids)
+
+    def get_img_ids(self, img_ids=[], cat_ids=[]):
+        return self.getImgIds(img_ids, cat_ids)
+
+    def load_anns(self, ids):
+        return self.loadAnns(ids)
+
+    def load_cats(self, ids):
+        return self.loadCats(ids)
+
+    def load_imgs(self, ids):
+        return self.loadImgs(ids)
+
+    @property
+    def img_ann_map(self):
+        return self.imgToAnns
+
+    @property
+    def cat_img_map(self):
+        return self.catToImgs
diff --git a/faster_coco_eval/core/cocoeval.py b/faster_coco_eval/core/cocoeval.py
index 3dd153b..f8ac1a3 100644
--- a/faster_coco_eval/core/cocoeval.py
+++ b/faster_coco_eval/core/cocoeval.py
@@ -2,10 +2,11 @@
 
 import copy
 import logging
-import numpy as np
 import time
 from collections import defaultdict
 
+import numpy as np
+
 from . import mask as maskUtils
 from .coco import COCO
 
@@ -70,11 +71,12 @@ def __init__(
         print_function=logger.debug,
         extra_calc=False,
     ):
-        """
-        Initialize CocoEval using coco APIs for gt and dt
-        :param cocoGt: coco object with ground truth annotations
+        """Initialize CocoEval using coco APIs for gt and dt :param cocoGt:
+
+        coco object with ground truth annotations
         :param cocoDt: coco object with detection results
-        :return: None
+        :return: None.
+
         """
         if not iouType:
             logger.warning("iouType not specified. use default iouType segm")
@@ -101,9 +103,10 @@ def __init__(
         self.print_function = print_function  # output print function
 
     def _prepare(self):
-        """
-        Prepare ._gts and ._dts for evaluation based on params
+        """Prepare ._gts and ._dts for evaluation based on params.
+
         :return: None
+
         """
 
         def _toMask(anns, coco):
@@ -145,9 +148,11 @@ def _toMask(anns, coco):
         self.eval = {}  # accumulated evaluation results
 
     def evaluate(self):
-        """
-        Run per image evaluation on given images and store results (a list of dict) in self.evalImgs
+        """Run per image evaluation on given images and store results (a list
+        of dict) in self.evalImgs.
+
         :return: None
+
         """
         tic = time.time()
         self.print_function("Running per image evaluation...")
@@ -156,9 +161,8 @@ def evaluate(self):
         if p.useSegm is not None:
             p.iouType = "segm" if p.useSegm == 1 else "bbox"
             logger.warning(
-                "useSegm (deprecated) is not None. Running {} evaluation".format(
-                    p.iouType
-                )
+                "useSegm (deprecated) is not None. Running {} evaluation"
+                .format(p.iouType)
             )
         self.print_function("Evaluate annotation type *{}*".format(p.iouType))
         p.imgIds = list(np.unique(p.imgIds))
@@ -275,9 +279,10 @@ def computeOks(self, imgId, catId):
         return ious
 
     def evaluateImg(self, imgId, catId, aRng, maxDet):
-        """
-        perform evaluation for single category and image
+        """Perform evaluation for single category and image.
+
         :return: dict (single image results)
+
         """
         p = self.params
         if p.useCats:
@@ -331,7 +336,7 @@ def evaluateImg(self, imgId, catId, aRng, maxDet):
                         # continue to next gt unless better match made
                         if ious[dind, gind] < iou:
                             continue
-                        # if match successful and best so far, store appropriately
+                        # if match successful and best so far, store appropriately # noqa: E501
                         iou = ious[dind, gind]
                         m = gind
                     # if match made store id of match for both dt and gt
@@ -364,14 +369,19 @@ def accumulate(self, p=None):
         raise DeprecationWarning("deprecated")
 
     def summarize(self):
-        """
-        Compute and display summary metrics for evaluation results.
-        Note this functin can *only* be applied on the default parameter setting
+        """Compute and display summary metrics for evaluation results.
+
+        Note this functin can *only* be applied on the default parameter
+        setting
+
         """
 
         def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100):
             p = self.params
-            iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}"
+            iStr = (
+                " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] ="
+                " {:0.3f}"
+            )
             titleStr = "Average Precision" if ap == 1 else "Average Recall"
             typeStr = "(AP)" if ap == 1 else "(AR)"
             iouStr = (
@@ -484,14 +494,12 @@ def __str__(self):
 
 
 class Params:
-    """
-    Params for coco evaluation api
-    """
+    """Params for coco evaluation api."""
 
     def setDetParams(self):
         self.imgIds = []
         self.catIds = []
-        # np.arange causes trouble.  the data point on arange is slightly larger than the true value
+        # np.arange causes trouble.  the data point on arange is slightly larger than the true value # noqa: E501
         self.iouThrs = np.linspace(
             0.5, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True
         )
@@ -511,7 +519,7 @@ def setDetParams(self):
     def setKpParams(self):
         self.imgIds = []
         self.catIds = []
-        # np.arange causes trouble.  the data point on arange is slightly larger than the true value
+        # np.arange causes trouble.  the data point on arange is slightly larger than the true value # noqa: E501
         self.iouThrs = np.linspace(
             0.5, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True
         )
diff --git a/faster_coco_eval/core/faster_eval_api.py b/faster_coco_eval/core/faster_eval_api.py
index 19b5a63..e4ee2da 100644
--- a/faster_coco_eval/core/faster_eval_api.py
+++ b/faster_coco_eval/core/faster_eval_api.py
@@ -3,10 +3,12 @@
 
 import copy
 import logging
-import numpy as np
 import time
 
+import numpy as np
+
 import faster_coco_eval.faster_eval_api_cpp as _C
+
 from . import mask as maskUtils
 from .cocoeval import COCOeval
 
@@ -14,18 +16,20 @@
 
 
 class COCOeval_faster(COCOeval):
-    """
-    This is a slightly modified version of the original COCO API, where the functions evaluateImg()
-    and accumulate() are implemented in C++ to speedup evaluation
-    """
+    """This is a slightly modified version of the original COCO API, where the
+    functions evaluateImg() and accumulate() are implemented in C++ to speedup
+    evaluation."""
 
     def evaluate(self):
-        """
-        Run per image evaluation on given images and store results in self.evalImgs_cpp, a
-        datastructure that isn't readable from Python but is used by a c++ implementation of
-        accumulate().  Unlike the original COCO PythonAPI, we don't populate the datastructure
-        self.evalImgs because this datastructure is a computational bottleneck.
+        """Run per image evaluation on given images and store results in
+        self.evalImgs_cpp, a datastructure that isn't readable from Python but
+        is used by a c++ implementation of accumulate().
+
+        Unlike the original COCO PythonAPI, we don't populate the
+        datastructure self.evalImgs because this datastructure is a
+        computational bottleneck.
         :return: None
+
         """
         tic = time.time()
 
@@ -61,7 +65,7 @@ def evaluate(self):
 
         # <<<< Beginning of code differences with original COCO API
         def convert_instances_to_cpp(instances, is_det=False):
-            # Convert annotations for a list of instances in an image to a format that's fast
+            # Convert annotations for a list of instances in an image to a format that's fast # noqa: E501
             # to access in C++
             instances_cpp = []
             for instance in instances:
@@ -75,7 +79,7 @@ def convert_instances_to_cpp(instances, is_det=False):
                 instances_cpp.append(instance_cpp)
             return instances_cpp
 
-        # Convert GT annotations, detections, and IOUs to a format that's fast to access in C++
+        # Convert GT annotations, detections, and IOUs to a format that's fast to access in C++ # noqa: E501
         ground_truth_instances = [
             [
                 convert_instances_to_cpp(self._gts[imgId, catId])
@@ -122,9 +126,12 @@ def convert_instances_to_cpp(instances, is_det=False):
         self.print_function("DONE (t={:0.2f}s).".format(toc - tic))
 
     def accumulate(self):
-        """
-        Accumulate per image evaluation results and store the result in self.eval.  Does not
-        support changing parameter settings from those used by self.evaluate()
+        """Accumulate per image evaluation results and store the result in
+        self.eval.
+
+        Does not support changing parameter settings from those used by
+        self.evaluate()
+
         """
         self.print_function("Accumulating evaluation results...")
         tic = time.time()
@@ -134,13 +141,12 @@ def accumulate(self):
 
         self.eval = _C.COCOevalAccumulate(self._paramsEval, self._evalImgs_cpp)
 
-        # recall is num_iou_thresholds X num_categories X num_area_ranges X num_max_detections
+        # recall is num_iou_thresholds X num_categories X num_area_ranges X num_max_detections # noqa: E501
         self.eval["recall"] = np.array(self.eval["recall"]).reshape(
             self.eval["counts"][:1] + self.eval["counts"][2:]
         )
 
-        # precision and scores are num_iou_thresholds X num_recall_thresholds X num_categories X
-        # num_area_ranges X num_max_detections
+        # precision and scores are num_iou_thresholds X num_recall_thresholds X num_categories X num_area_ranges X num_max_detections # noqa: E501
         self.eval["precision"] = np.array(self.eval["precision"]).reshape(
             self.eval["counts"]
         )
diff --git a/faster_coco_eval/core/mask.py b/faster_coco_eval/core/mask.py
index 1cb5ce1..f1fd7a3 100644
--- a/faster_coco_eval/core/mask.py
+++ b/faster_coco_eval/core/mask.py
@@ -34,7 +34,7 @@
 #  iou            - Compute intersection over union between masks.
 #  area           - Compute area of encoded masks.
 #  toBbox         - Get bounding boxes surrounding encoded masks.
-#  frPyObjects    - Convert polygon, bbox, and uncompressed RLE to encoded RLE mask.
+#  frPyObjects    - Convert polygon, bbox, and uncompressed RLE to encoded RLE mask. # noqa: E501
 #
 # Usage:
 #  Rs     = encode( masks )
@@ -48,8 +48,8 @@
 # In the API the following formats are used:
 #  Rs      - [dict] Run-length encoding of binary masks
 #  R       - dict Run-length encoding of binary mask
-#  masks   - [hxwxn] Binary mask(s) (must have type np.ndarray(dtype=uint8) in column-major order)
-#  iscrowd - [nx1] list of np.ndarray. 1 indicates corresponding gt image has crowd region to ignore
+#  masks   - [hxwxn] Binary mask(s) (must have type np.ndarray(dtype=uint8) in column-major order) # noqa: E501
+#  iscrowd - [nx1] list of np.ndarray. 1 indicates corresponding gt image has crowd region to ignore # noqa: E501
 #  bbs     - [nx4] Bounding box(es) stored as [x y w h]
 #  poly    - Polygon stored as [[x1 y1 x2 y2...],[x1 y1 ...],...] (2D list)
 #  dt,gt   - May be either bounding boxes or encoded masks
diff --git a/faster_coco_eval/extra/display.py b/faster_coco_eval/extra/display.py
index cfdf9e2..54c9cf3 100644
--- a/faster_coco_eval/extra/display.py
+++ b/faster_coco_eval/extra/display.py
@@ -1,7 +1,8 @@
 import logging
-import numpy as np
 from typing import Optional
 
+import numpy as np
+
 from .draw import display_image, display_matrix
 from .extra import ExtraEval
 
@@ -57,9 +58,7 @@ def display_tp_fp_fn(
                 )
 
     def _compute_confusion_matrix(self, y_true, y_pred, fp={}, fn={}):
-        """
-        return classes*(classes + fp col + fn col)
-        """
+        """Return classes*(classes + fp col + fn col)"""
         categories_real_ids = list(self.cocoGt.cats)
         categories_enum_ids = {
             category_id: _i
@@ -82,9 +81,8 @@ def compute_confusion_matrix(self):
 
         if self.useCats:
             logger.warning(
-                "The calculation may not be accurate. No intersection of classes. useCats={}".format(
-                    self.useCats
-                )
+                "The calculation may not be accurate. No intersection of"
+                " classes. useCats={}".format(self.useCats)
             )
 
         y_true = []
@@ -116,7 +114,7 @@ def compute_confusion_matrix(self):
         return cm
 
     def display_matrix(
-        self, in_percent=False, conf_matrix=None, return_fig: bool = False
+        self, normalize=False, conf_matrix=None, return_fig: bool = False
     ):
         if conf_matrix is None:
             conf_matrix = self.compute_confusion_matrix()
@@ -124,5 +122,5 @@ def display_matrix(
         labels = [category["name"] for _, category in self.cocoGt.cats.items()]
 
         return display_matrix(
-            conf_matrix, labels, in_percent=in_percent, return_fig=return_fig
+            conf_matrix, labels, normalize=normalize, return_fig=return_fig
         )
diff --git a/faster_coco_eval/extra/draw.py b/faster_coco_eval/extra/draw.py
index a584dba..6149e36 100644
--- a/faster_coco_eval/extra/draw.py
+++ b/faster_coco_eval/extra/draw.py
@@ -1,10 +1,11 @@
 import logging
-import numpy as np
 import os.path as osp
+from typing import Optional
+
+import numpy as np
 import plotly.express as px
 import plotly.graph_objs as go
 from PIL import Image
-from typing import Optional
 
 from ..core import COCO
 
@@ -139,7 +140,13 @@ def display_image(
                             ann,
                             color=DT_COLOR,
                             iouType=iouType,
-                            text="<b>DT</b><br>id={}<br>category={}<br>score={:.2f}<br>IoU={:.2f}".format(
+                            text=(
+                                "<b>DT</b><br>"
+                                "id={}<br>"
+                                "category={}<br>"
+                                "score={:.2f}<br>"
+                                "IoU={:.2f}"
+                            ).format(
                                 ann["id"],
                                 categories_labels[ann["category_id"]],
                                 ann["score"],
@@ -154,7 +161,12 @@ def display_image(
                             ann,
                             color=FP_COLOR,
                             iouType=iouType,
-                            text="<b>FP</b><br>id={}<br>category={}<br>score={:.2f}".format(
+                            text=(
+                                "<b>FP</b><br>"
+                                "id={}<br>"
+                                "category={}<br>"
+                                "score={:.2f}"
+                            ).format(
                                 ann["id"],
                                 categories_labels[ann["category_id"]],
                                 ann["score"],
@@ -199,18 +211,18 @@ def display_image(
 def display_matrix(
     conf_matrix: np.ndarray,
     labels: list,
-    in_percent: bool = False,
+    normalize: bool = False,
     return_fig: bool = False,
 ):
     _labels = labels + ["fp", "fn"]
 
-    if in_percent:
+    if normalize:
         conf_matrix /= conf_matrix.sum(axis=1).reshape(-1, 1)
         conf_matrix *= 100
 
-    hovertemplate = "Real: %{y}<br>" "Predict: %{x}<br>"
+    hovertemplate = "Real: %{y}<br>Predict: %{x}<br>"
 
-    if in_percent:
+    if normalize:
         hovertemplate += "Percent: %{z:.0f}<extra></extra>"
     else:
         hovertemplate += "Count: %{z:.0f}<extra></extra>"
@@ -227,7 +239,7 @@ def display_matrix(
     for j, row in enumerate(conf_matrix):
         for i, value in enumerate(row):
             text_value = "{:.0f}".format(value)
-            if in_percent:
+            if normalize:
                 text_value += "%"
 
             annotations.append(
@@ -242,8 +254,12 @@ def display_matrix(
                 }
             )
 
+    title = "Confusion Matrix"
+    if normalize:
+        title = "Normalized " + title
+
     layout = {
-        "title": "Confusion Matrix",
+        "title": title,
         "xaxis": {"title": "Predicted value"},
         "yaxis": {"title": "Real value"},
         "annotations": annotations,
diff --git a/faster_coco_eval/extra/extra.py b/faster_coco_eval/extra/extra.py
index 14598fc..cce9f50 100644
--- a/faster_coco_eval/extra/extra.py
+++ b/faster_coco_eval/extra/extra.py
@@ -1,5 +1,6 @@
 import copy
 import logging
+
 import numpy as np
 
 from ..core import COCO, COCOeval_faster
diff --git a/faster_coco_eval/version.py b/faster_coco_eval/version.py
index 45ba11b..748975b 100644
--- a/faster_coco_eval/version.py
+++ b/faster_coco_eval/version.py
@@ -1,2 +1,2 @@
-__version__ = "1.5.1"
+__version__ = "1.5.2"
 __author__ = "MiXaiLL76"
diff --git a/tests/basic.py b/tests/basic.py
index e57c4d5..ee290fb 100644
--- a/tests/basic.py
+++ b/tests/basic.py
@@ -1,24 +1,18 @@
 #!/usr/bin/python3
 
-import json
-import numpy as np
 import unittest
 
+import numpy as np
+
 import faster_coco_eval.core.mask as mask_util
 from faster_coco_eval import COCO, COCOeval_faster
 from faster_coco_eval.extra import PreviewResults
 
 
-def load(file):
-    with open(file) as io:
-        _data = json.load(io)
-    return _data
-
-
 class TestBaseCoco(unittest.TestCase):
     def test_coco(self):
-        prepared_coco_in_dict = load("tests/dataset/gt_dataset.json")
-        prepared_anns = load("tests/dataset/dt_dataset.json")
+        prepared_coco_in_dict = COCO.load_json("tests/dataset/gt_dataset.json")
+        prepared_anns = COCO.load_json("tests/dataset/dt_dataset.json")
 
         stats_as_dict = {
             "AP_all": 0.7832783278327835,
@@ -60,8 +54,8 @@ def test_coco(self):
 
 class TestConfusionMatrix(unittest.TestCase):
     def test_coco(self):
-        prepared_coco_in_dict = load("tests/dataset/gt_dataset.json")
-        prepared_anns = load("tests/dataset/dt_dataset.json")
+        prepared_coco_in_dict = COCO.load_json("tests/dataset/gt_dataset.json")
+        prepared_anns = COCO.load_json("tests/dataset/dt_dataset.json")
 
         prepared_result = [
             [2.0, 1.0, 0.0, 0.0, 1.0, 0.0],
@@ -114,7 +108,7 @@ class TestMaskUtil(unittest.TestCase):
     def testInvalidRLECounts(self):
         rle = {
             "size": [1024, 1024],
-            "counts": "jd`0=`o06J5L4M3L3N2N2N2N2N1O2N2N101N1O2O0O1O2N100O1O2N100O1O1O1O1O101N1O1O1O1O1O1O101N1O100O101O0O100000000000000001O00001O1O0O2O1N3N1N3N3L5Kh0XO6J4K5L5Id[o5N]dPJ7K4K4M3N2M3N2N1O2N100O2O0O1000O01000O101N1O1O2N2N2M3M3M4J7Inml5H[RSJ6L2N2N2N2O000000000000O2O1N2N2Mkm81SRG6L3L3N2O1N2N2O0O2O00001O0000000000O2O001N2O0O2N2N3M3L5JRjf6MPVYI8J4L3N3M2N1O2O1N101N1000000O10000001O000O101N101N1O2N2N2N3L4L7FWZ_50ne`J0000001O000000001O0000001O1O0N3M3N1O2N2N2O1N2O001N2`RO^O`k0c0[TOEak0;\\\\TOJbk07\\\\TOLck03[TO0dk01ZTO2dk0OYTO4gk0KXTO7gk0IXTO8ik0HUTO:kk0ETTO=lk0CRTO>Pl0@oSOb0Rl0\\\\OmSOe0Tl0[OjSOg0Ul0YOiSOi0Wl0XOgSOi0Yl0WOeSOk0[l0VOaSOn0kh0cNmYO",
+            "counts": "jd`0=`o06J5L4M3L3N2N2N2N2N1O2N2N101N1O2O0O1O2N100O1O2N100O1O1O1O1O101N1O1O1O1O1O1O101N1O100O101O0O100000000000000001O00001O1O0O2O1N3N1N3N3L5Kh0XO6J4K5L5Id[o5N]dPJ7K4K4M3N2M3N2N1O2N100O2O0O1000O01000O101N1O1O2N2N2M3M3M4J7Inml5H[RSJ6L2N2N2N2O000000000000O2O1N2N2Mkm81SRG6L3L3N2O1N2N2O0O2O00001O0000000000O2O001N2O0O2N2N3M3L5JRjf6MPVYI8J4L3N3M2N1O2O1N101N1000000O10000001O000O101N101N1O2N2N2N3L4L7FWZ_50ne`J0000001O000000001O0000001O1O0N3M3N1O2N2N2O1N2O001N2`RO^O`k0c0[TOEak0;\\\\TOJbk07\\\\TOLck03[TO0dk01ZTO2dk0OYTO4gk0KXTO7gk0IXTO8ik0HUTO:kk0ETTO=lk0CRTO>Pl0@oSOb0Rl0\\\\OmSOe0Tl0[OjSOg0Ul0YOiSOi0Wl0XOgSOi0Yl0WOeSOk0[l0VOaSOn0kh0cNmYO",  # noqa: E501
         }
         with self.assertRaises(ValueError):
             mask_util.decode(rle)
@@ -124,7 +118,7 @@ def testZeroLeadingRLE(self):
         # This input rle has 3 leading zeros.
         rle = {
             "size": [1350, 1080],
-            "counts": "000lg0Zb01O00001O00001O001O00001O00001O001O00001O01O2N3M3M3M2N3M3N2M3M2N1O1O1O1O2N1O1O1O2N1O1O101N1O1O1O2N1O1O1O2N3M2N1O2N1O2O0O2N1O1O2N1O2N1O2N1O2N1O2N1O2O0O2N1O3M2N1O2N2N2N2N2N1O2N2N2N2N1O2N2N2N2N2N1N3N2N00O1O1O1O100000000000000O100000000000000001O0000001O00001O0O5L7I5K4L4L3M2N2N2N1O2m]OoXOm`0Sg0j^OVYOTa0lf0c^O]YO[a0ef0\\^OdYOba0bg0N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N3M2M4M2N3M2N3M2N3M2N3M2N3M2N3M2N3M2N3M2M4M2N2N3M2M4M2N2N3M2M3N3M2N3M2M3N3M2N2N3L3N2N3M2N3L3N2N3M5J4M3M4L3M3L5L3M3M4L3L4\\EXTOd6jo0K6J5K6I4M1O1O1O1N2O1O1O001N2O00001O0O101O000O2O00001N101N101N2N101N101N101N2O0O2O0O2O0O2O1N101N2N2O1N2O1N2O1N101N2O1N2O1N2O0O2O1N2N2O1N2O0O2O1N2O1N2N2N1N4M2N2M4M2N3L3N2N3L3N3L3N2N3L3N2N3L3M4L3M3M4L3M5K5K5K6J5K5K6J7I7I7Ibijn0",
+            "counts": "000lg0Zb01O00001O00001O001O00001O00001O001O00001O01O2N3M3M3M2N3M3N2M3M2N1O1O1O1O2N1O1O1O2N1O1O101N1O1O1O2N1O1O1O2N3M2N1O2N1O2O0O2N1O1O2N1O2N1O2N1O2N1O2N1O2O0O2N1O3M2N1O2N2N2N2N2N1O2N2N2N2N1O2N2N2N2N2N1N3N2N00O1O1O1O100000000000000O100000000000000001O0000001O00001O0O5L7I5K4L4L3M2N2N2N1O2m]OoXOm`0Sg0j^OVYOTa0lf0c^O]YO[a0ef0\\^OdYOba0bg0N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N3M2M4M2N3M2N3M2N3M2N3M2N3M2N3M2N3M2N3M2M4M2N2N3M2M4M2N2N3M2M3N3M2N3M2M3N3M2N2N3L3N2N3M2N3L3N2N3M5J4M3M4L3M3L5L3M3M4L3L4\\EXTOd6jo0K6J5K6I4M1O1O1O1N2O1O1O001N2O00001O0O101O000O2O00001N101N101N2N101N101N101N2O0O2O0O2O0O2O1N101N2N2O1N2O1N2O1N101N2O1N2O1N2O0O2O1N2N2O1N2O0O2O1N2O1N2N2N1N4M2N2M4M2N3L3N2N3L3N3L3N2N3L3N2N3L3M4L3M3M4L3M5K5K5K6J5K5K6J7I7I7Ibijn0",  # noqa: E501
         }
         orig_bbox = mask_util.toBbox(rle)
         mask = mask_util.decode(rle)

From b54ac781789cc968dacc961bf7a0dad86fda43af Mon Sep 17 00:00:00 2001
From: MiXaiLL76 <mike.milos@yandex.ru>
Date: Sat, 13 Apr 2024 23:36:17 +0300
Subject: [PATCH 10/15] fix version

---
 faster_coco_eval/version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/faster_coco_eval/version.py b/faster_coco_eval/version.py
index 748975b..45ba11b 100644
--- a/faster_coco_eval/version.py
+++ b/faster_coco_eval/version.py
@@ -1,2 +1,2 @@
-__version__ = "1.5.2"
+__version__ = "1.5.1"
 __author__ = "MiXaiLL76"

From 43ccc35be22c8f328fb8f3fe4b0bdb3131d852e4 Mon Sep 17 00:00:00 2001
From: MiXaiLL76 <mike.milos@yandex.ru>
Date: Sat, 13 Apr 2024 23:39:51 +0300
Subject: [PATCH 11/15] update examples

---
 examples/curve_example.ipynb | 51 ++++++++++++++----------------------
 examples/eval_example.ipynb  | 32 +++++++---------------
 2 files changed, 28 insertions(+), 55 deletions(-)

diff --git a/examples/curve_example.ipynb b/examples/curve_example.ipynb
index 256c2ad..46b7b59 100644
--- a/examples/curve_example.ipynb
+++ b/examples/curve_example.ipynb
@@ -10,7 +10,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "faster_coco_eval.__version__='1.5.0'\n"
+      "faster_coco_eval.__version__='1.5.1'\n"
      ]
     }
    ],
@@ -31,31 +31,17 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "9a6c7b67-616b-4feb-9f1d-9dcbb384ed2c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def load(file):\n",
-    "    with open(file) as io:\n",
-    "        _data = json.load(io)\n",
-    "\n",
-    "    return _data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
    "id": "a28dde2b-7219-4d9c-acbe-0e4d9db05692",
    "metadata": {},
    "outputs": [],
    "source": [
-    "prepared_coco_in_dict = load(\"../tests/dataset/gt_dataset.json\")\n",
-    "prepared_anns = load(\"../tests/dataset/dt_dataset.json\")"
+    "prepared_coco_in_dict = COCO.load_json(\"../tests/dataset/gt_dataset.json\")\n",
+    "prepared_anns = COCO.load_json(\"../tests/dataset/dt_dataset.json\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "1b95cda6",
    "metadata": {},
    "outputs": [
@@ -1258,9 +1244,9 @@
        }
       },
       "text/html": [
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+   "execution_count": 4,
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        "                            \n",
-       "var gd = document.getElementById('1aa7e8fe-2763-4351-97d9-9a4cc201b135');\n",
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@@ -3646,7 +3633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "8106fc0e",
    "metadata": {},
    "outputs": [
@@ -5030,9 +5017,9 @@
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                       {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('6f23faf8-fb85-4ef2-be79-dac098a98f50');\n",
+       "var gd = document.getElementById('f9ba5577-3da4-485d-8300-68496100faed');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
diff --git a/examples/eval_example.ipynb b/examples/eval_example.ipynb
index b930b6a..cba7cbe 100644
--- a/examples/eval_example.ipynb
+++ b/examples/eval_example.ipynb
@@ -10,7 +10,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "faster_coco_eval.__version__='1.5.0'\n"
+      "faster_coco_eval.__version__='1.5.1'\n"
      ]
     }
    ],
@@ -29,31 +29,17 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "9a6c7b67-616b-4feb-9f1d-9dcbb384ed2c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def load(file):\n",
-    "    with open(file) as io:\n",
-    "        _data = json.load(io)\n",
-    "\n",
-    "    return _data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
    "id": "a28dde2b-7219-4d9c-acbe-0e4d9db05692",
    "metadata": {},
    "outputs": [],
    "source": [
-    "prepared_coco_in_dict = load(\"../tests/dataset/gt_dataset.json\")\n",
-    "prepared_anns = load(\"../tests/dataset/dt_dataset.json\")"
+    "prepared_coco_in_dict = COCO.load_json(\"../tests/dataset/gt_dataset.json\")\n",
+    "prepared_anns = COCO.load_json(\"../tests/dataset/dt_dataset.json\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "22132ee6-11c0-4c43-8f08-cf5729cd4727",
    "metadata": {},
    "outputs": [],
@@ -64,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "1539cfd1-e76c-45bb-91b2-b3afa52eec68",
    "metadata": {},
    "outputs": [],
@@ -85,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "6990cc70-10aa-4f08-bfe7-9d23d6a28cb6",
    "metadata": {},
    "outputs": [
@@ -97,7 +83,7 @@
        "        1.        ,  0.        ])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -108,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "587b3440-9c35-4363-a89f-9d485084f9fe",
    "metadata": {},
    "outputs": [
@@ -133,7 +119,7 @@
        " 'mAUC_50': 0.594074074074074}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }

From ae75c0323529122b21489277c6bce7a54cffe55c Mon Sep 17 00:00:00 2001
From: MiXaiLL76 <mike.milos@yandex.ru>
Date: Sat, 13 Apr 2024 23:40:01 +0300
Subject: [PATCH 12/15] update ignore

---
 .gitignore | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.gitignore b/.gitignore
index 94a9dd7..984a847 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,6 +4,6 @@ dist
 faster_coco_eval.egg-info
 *.pyc
 __pycache__
-examples/comparison/COCO
-examples/comparison/model
-csrc/mask/pycocotools/_mask.c
+examples/comparison/mmdet/COCO
+examples/comparison/mmdet/model
+csrc/mask/pycocotools/_mask.c
\ No newline at end of file

From 388c7a98ffbba0cb4f72fcb1b1d0dde77c0dea55 Mon Sep 17 00:00:00 2001
From: MiXaiLL76 <mike.milos@yandex.ru>
Date: Sat, 13 Apr 2024 23:40:13 +0300
Subject: [PATCH 13/15] update build yml

---
 .github/workflows/build.yml | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 2e554d7..67da11c 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -45,7 +45,7 @@ jobs:
       - uses: actions/checkout@v4
 
       - name: Build sdist
-        run: pipx run build --sdist .
+        run: make sdist
 
       - uses: actions/upload-artifact@v4
         with:
@@ -67,12 +67,12 @@ jobs:
         with:
           name: sdist
           path: dist
-      
+
       - uses: actions/download-artifact@v4
         with:
           name: wheels
           path: dist
-      
+
       - name: Display structure of downloaded files
         run: ls -R dist
 
@@ -95,16 +95,16 @@ jobs:
         with:
           name: sdist
           path: dist
-      
+
       - uses: actions/download-artifact@v4
         with:
           name: wheels
           path: dist
-      
+
       - name: Display structure of downloaded files
         run: ls -R dist
 
       - name: Publish package distributions to TestPyPI
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
-          password: ${{ secrets.PYPI_API_TOKEN }}
\ No newline at end of file
+          password: ${{ secrets.PYPI_API_TOKEN }}

From 98c8ddc3e5cc2c2d000a7b6e6c8b0dff3fbd9de2 Mon Sep 17 00:00:00 2001
From: MiXaiLL76 <mike.milos@yandex.ru>
Date: Sat, 13 Apr 2024 23:41:50 +0300
Subject: [PATCH 14/15] add docformatter to Lint

---
 .github/workflows/linter.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/linter.yml b/.github/workflows/linter.yml
index f1eabdd..ee15cca 100644
--- a/.github/workflows/linter.yml
+++ b/.github/workflows/linter.yml
@@ -15,7 +15,7 @@ jobs:
           python-version: "3.x"
 
       - name: Install dependencies
-        run: pip3 install flake8==7.0.0 black==24.2.0 isort==5.13.2
+        run: pip3 install flake8==7.0.0 black==24.2.0 isort==5.13.2 docformatter==1.7.5
 
       - name: Lint with flake8
-        run: make linter
\ No newline at end of file
+        run: make linter

From fbff493b67fdcfefd9818df01bb23ced13612417 Mon Sep 17 00:00:00 2001
From: MiXaiLL76 <mike.milos@yandex.ru>
Date: Sat, 13 Apr 2024 23:48:11 +0300
Subject: [PATCH 15/15] update history

---
 history.md | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/history.md b/history.md
index 2544503..530379e 100644
--- a/history.md
+++ b/history.md
@@ -1,8 +1,11 @@
 
 # history
 
-## v1.4.3
-- [x] fix [issue](https://github.com/MiXaiLL76/faster_coco_eval/issues/19)
+## v1.5.1
+- [x] **breaking change** | new static function COCO.load_json
+- [x] new curve f1_confidence with ```cur.plot_f1_confidence()```
+- [x] **breaking change** | replace display_matrix arg ```in_percent``` to ```normalize```
+- [x] **breaking change** | rework draw functions
 
 ## v1.4.2
 - [x] append Auto-formatters