Add accuracy metrics as iou default

hot_fair_utilities/training/yolo_v8_v1/train.py

@@ -9,7 +9,7 @@
 
 # Reader imports
 from hot_fair_utilities.model.yolo import YOLOSegWithPosWeight
-
+from ...utils import compute_iou_chart_from_yolo_results, get_yolo_iou_metrics
 # Get environment variables with fallbacks
 ROOT = Path(os.getenv("YOLO_ROOT", Path(__file__).parent.absolute()))
 DATA_ROOT = str(Path(os.getenv("YOLO_DATA_ROOT", ROOT / "yolo-training")))
@@ -121,7 +121,14 @@ def train(
         device=[int(i) for i in gpu.split(",")] if "," in gpu else gpu,
         **kwargs,
     )
-    return weights
+    compute_iou_chart_from_yolo_results(results_csv_path=os.path.join(output_path,"checkpoints", name,'results.csv'),results_output_chart_path=os.path.join(output_path,"checkpoints", name,'iou_chart.png'))
+
+    output_model_path=os.path.join(os.path.join(output_path,"checkpoints"), name, "weights", "best.pt")
+
+    iou_model_accuracy=get_yolo_iou_metrics(output_model_path)
+
+    return  output_model_path,iou_model_accuracy
+
 
 
 def check4checkpoint(name, weights,output_path):

hot_fair_utilities/training/yolo_v8_v2/train.py

@@ -6,7 +6,7 @@
 # Third party imports
 import torch
 import ultralytics
-
+from ...utils import get_yolo_iou_metrics,compute_iou_chart_from_yolo_results
 # Reader imports
 from hot_fair_utilities.model.yolo import YOLOSegWithPosWeight
 
@@ -86,7 +86,13 @@ def train(data, weights, epochs, batch_size, pc, output_path, dataset_yaml_path,
 
     # metrics = model.val(save_json=True, plots=True)
     # print(model.val())
-    return  os.path.join(os.path.join(output_path,"checkpoints"), name, "weights", "best.pt")
+    compute_iou_chart_from_yolo_results(results_csv_path=os.path.join(output_path,"checkpoints", name,'results.csv'),results_output_chart_path=os.path.join(output_path,"checkpoints", name,'iou_chart.png'))
+
+    output_model_path=os.path.join(os.path.join(output_path,"checkpoints"), name, "weights", "best.pt")
+
+    iou_model_accuracy=get_yolo_iou_metrics(output_model_path)
+
+    return  output_model_path,iou_model_accuracy
 
 
 def check4checkpoint(name, weights,output_path):

hot_fair_utilities/utils.py

@@ -5,12 +5,15 @@
 import math
 import os
 import re
+import ultralytics
+
 import time
 import urllib.request
 import zipfile
 from glob import glob
 from typing import Tuple
-
+import pandas as pd 
+import matplotlib.pyplot as plt
 # Third party imports
 # Third-party imports
 import geopandas
@@ -251,3 +254,45 @@ def fetch_osm_data(payload: json, API_URL="https://api-prod.raw-data.hotosm.org/
         with zip_ref.open("Export.geojson") as file:
             my_export_geojson = json.loads(file.read())
     return my_export_geojson
+
+
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+def compute_iou_chart_from_yolo_results(results_csv_path,results_output_chart_path):
+
+    data = pd.read_csv(results_csv_path)
+
+
+    data['IoU(M)'] = 1 / (
+        1 / data['metrics/precision(M)'] + 1 / data['metrics/recall(M)'] - 1
+    )
+
+    plt.figure(figsize=(10, 5))
+    plt.plot(data['epoch'], data['IoU(M)'], label='IoU (Mask)')
+    plt.xlabel('Epoch')
+    plt.xticks(data['epoch'].astype(int))
+    plt.ylabel('IoU')
+    plt.title('IoU over Epochs')
+    plt.legend()
+    plt.grid()
+
+    plt.savefig(results_output_chart_path)
+    return results_output_chart_path
+
+
+def get_yolo_iou_metrics(model_path):
+
+    model_val = ultralytics.YOLO(model_path)
+    model_val_metrics = (
+        model_val.val().results_dict
+    )  ### B and M denotes bounding box and mask respectively
+    # print(metrics)
+    iou_accuracy = 1 / (
+        1 / model_val_metrics["metrics/precision(M)"]
+        + 1 / model_val_metrics["metrics/recall(M)"]
+        - 1
+    )  # ref here https://github.com/ultralytics/ultralytics/issues/9984#issuecomment-2422551315
+    final_accuracy = iou_accuracy * 100
+    return final_accuracy

test_yolo_v1.py

@@ -53,17 +53,18 @@ def __exit__(self, type, value, traceback):
         p_val=0.05,
     )
 
-output_model_path = train_yolo(
+output_model_path,output_model_iou_accuracy = train_yolo(
     data=f"{base_path}",
     weights=f"{os.getcwd()}/yolov8s_v1-seg-best.pt",
-    gpu="cpu",
+    # gpu="cpu",
     epochs=2,
     batch_size=16,
     pc=2.0,
     output_path=yolo_data_dir,
     dataset_yaml_path=os.path.join(yolo_data_dir,'yolo_dataset.yaml')
 
 )
+print(output_model_iou_accuracy)
 
 prediction_output = f"{base_path}/prediction/output"
 # model_path = f"{output_path}/weights/best.pt"

test_yolo_v2.py

@@ -2,6 +2,7 @@
 import os
 import time
 import warnings
+import ultralytics
 
 os.environ.update(os.environ)
 os.environ["RAMP_HOME"] = os.getcwd()
@@ -50,18 +51,17 @@ def __exit__(self, type, value, traceback):
         output_path=yolo_data_dir,
     )
 
-output_model_path = train_yolo(
+output_model_path,output_model_iou_accuracy = train_yolo(
     data=f"{base_path}",
-    weights=f"{os.getcwd()}/yolov8s_v2-seg.pt",  ## Todo : replace with finetuned ramp model checkpoint
-    gpu="cpu",
+    weights=f"{os.getcwd()}/yolov8s_v2-seg.pt", 
+    # gpu="cpu",
     epochs=2,
     batch_size=16,
     pc=2.0,
     output_path=yolo_data_dir,
     dataset_yaml_path=os.path.join(yolo_data_dir,'yolo_dataset.yaml')
 )
-
-print(output_model_path)
+print(output_model_iou_accuracy)
 
 prediction_output = f"{base_path}/prediction/output"
 # model_path = f"{output_path}/weights/best.pt"