diff --git a/459_YOLOv9-Wholebody25/yolov9/convert_int64_to_int32.py b/459_YOLOv9-Wholebody25/yolov9/convert_int64_to_int32.py
new file mode 100755
index 0000000000..ad213c5616
--- /dev/null
+++ b/459_YOLOv9-Wholebody25/yolov9/convert_int64_to_int32.py
@@ -0,0 +1,123 @@
+import onnx
+
+from onnx import helper as h
+from onnx import checker as ch
+from onnx import TensorProto, GraphProto
+from onnx import numpy_helper as nph
+
+import numpy as np
+from collections import OrderedDict
+
+import logging as log
+import typer
+
+
+def make_param_dictionary(initializer):
+    params = OrderedDict()
+    for data in initializer:
+        params[data.name] = data
+    return params
+
+
+def convert_params_to_int32(params_dict):
+    converted_params = []
+    for param in params_dict:
+        data = params_dict[param]
+        if data.data_type == TensorProto.INT64:
+            data_cvt = nph.to_array(data).astype(np.int32)
+            data = nph.from_array(data_cvt, data.name)
+        converted_params += [data]
+    return converted_params
+
+
+def convert_constant_nodes_to_int32(nodes):
+    """
+    convert_constant_nodes_to_int32 Convert Constant nodes to INT32. If a constant node has data type INT64, a new version of the
+    node is created with INT32 data type and stored.
+
+    Args:
+        nodes (list): list of nodes
+
+    Returns:
+        list: list of new nodes all with INT32 constants.
+    """
+    new_nodes = []
+    for node in nodes:
+        if (
+            node.op_type == "Constant"
+            and node.attribute[0].t.data_type == TensorProto.INT64
+        ):
+            data = nph.to_array(node.attribute[0].t).astype(np.int32)
+            new_t = nph.from_array(data)
+            new_node = h.make_node(
+                "Constant",
+                inputs=[],
+                outputs=node.output,
+                name=node.name,
+                value=new_t,
+            )
+            new_nodes += [new_node]
+        else:
+            new_nodes += [node]
+
+    return new_nodes
+
+
+def convert_model_to_int32(model_path: str, out_path: str):
+    """
+    convert_model_to_int32 Converts ONNX model with INT64 params to INT32 params.\n
+
+    Args:\n
+        model_path (str): path to original ONNX model.\n
+        out_path (str): path to save converted model.
+    """
+    log.info("ONNX INT64 --> INT32 Converter")
+    log.info(f"Loading Model: {model_path}")
+    # * load model.
+    model = onnx.load_model(model_path)
+    ch.check_model(model)
+    # * get model opset version.
+    opset_version = model.opset_import[0].version
+    domain: str = model.domain
+    ir_version: int = model.ir_version
+    meta_data = {'domain': domain, 'ir_version': ir_version}
+    metadata_props = None
+    if hasattr(model, 'metadata_props'):
+        metadata_props = model.metadata_props
+    graph = model.graph
+    # * The initializer holds all non-constant weights.
+    init = graph.initializer
+    # * collect model params in a dictionary.
+    params_dict = make_param_dictionary(init)
+    log.info("Converting INT64 model params to INT32...")
+    # * convert all INT64 aprams to INT32.
+    converted_params = convert_params_to_int32(params_dict)
+    log.info("Converting constant INT64 nodes to INT32...")
+    new_nodes = convert_constant_nodes_to_int32(graph.node)
+
+    graph_name = f"{graph.name}-int32"
+    log.info("Creating new graph...")
+    # * create a new graph with converted params and new nodes.
+    graph_int32 = h.make_graph(
+        new_nodes,
+        graph_name,
+        graph.input,
+        graph.output,
+        initializer=converted_params,
+    )
+    log.info("Creating new int32 model...")
+    model_int32 = h.make_model(graph_int32, producer_name="onnx-typecast")
+    model_int32.opset_import[0].version = opset_version
+    model_int32.domain = domain
+    model_int32.ir_version = ir_version
+    if metadata_props is not None:
+        model_int32.metadata_props.extend(metadata_props)
+    ch.check_model(model_int32)
+    log.info(f"Saving converted model as: {out_path}")
+    onnx.save_model(model_int32, out_path)
+    log.info(f"Done Done London. 🎉")
+    return
+
+
+if __name__ == "__main__":
+    typer.run(convert_model_to_int32)
diff --git a/459_YOLOv9-Wholebody25/yolov9/models/yolo.py b/459_YOLOv9-Wholebody25/yolov9/models/yolo.py
new file mode 100755
index 0000000000..ae8821f302
--- /dev/null
+++ b/459_YOLOv9-Wholebody25/yolov9/models/yolo.py
@@ -0,0 +1,834 @@
+import argparse
+import os
+import platform
+import sys
+from copy import deepcopy
+from pathlib import Path
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLO root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+if platform.system() != 'Windows':
+    ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.common import *
+from models.experimental import *
+from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
+from utils.plots import feature_visualization
+from utils.torch_utils import (fuse_conv_and_bn, initialize_weights, model_info, profile, scale_img, select_device,
+                               time_sync)
+from utils.tal.anchor_generator import make_anchors, dist2bbox
+
+try:
+    import thop  # for FLOPs computation
+except ImportError:
+    thop = None
+
+
+class Detect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch)  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = max((ch[0] // 4, self.reg_max * 4, 16)), max((ch[0], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch)
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch)
+        self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity()
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        for i in range(self.nl):
+            x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
+        if self.training:
+            return x
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
+            self.shape = shape
+
+        # box, cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).split((self.reg_max * 4, self.nc), 1)
+        box_cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2) # onnxruntime-web
+        cut_ind = self.reg_max * 4 # onnxruntime-web
+        box = box_cls[:, :cut_ind, :] # onnxruntime-web
+        cls = box_cls[:, cut_ind:, :] # onnxruntime-web
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        y = torch.cat((dbox, cls.sigmoid()), 1)
+        return y if self.export else (y, x)
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class DDetect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch)  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = make_divisible(max((ch[0] // 4, self.reg_max * 4, 16)), 4), max((ch[0], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3, g=4), nn.Conv2d(c2, 4 * self.reg_max, 1, groups=4)) for x in ch)
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch)
+        self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity()
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        for i in range(self.nl):
+            x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
+        if self.training:
+            return x
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
+            self.shape = shape
+
+        # box, cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).split((self.reg_max * 4, self.nc), 1)
+        box_cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2) # onnxruntime-web
+        cut_ind = self.reg_max * 4 # onnxruntime-web
+        box = box_cls[:, :cut_ind, :] # onnxruntime-web
+        cls = box_cls[:, cut_ind:, :] # onnxruntime-web
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        y = torch.cat((dbox, cls.sigmoid()), 1)
+        return y if self.export else (y, x)
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class DualDetect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch) // 2  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = max((ch[0] // 4, self.reg_max * 4, 16)), max((ch[0], min((self.nc * 2, 128))))  # channels
+        c4, c5 = max((ch[self.nl] // 4, self.reg_max * 4, 16)), max((ch[self.nl], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch[:self.nl])
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch[:self.nl])
+        self.cv4 = nn.ModuleList(
+            nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, 4 * self.reg_max, 1)) for x in ch[self.nl:])
+        self.cv5 = nn.ModuleList(
+            nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1)) for x in ch[self.nl:])
+        self.dfl = DFL(self.reg_max)
+        self.dfl2 = DFL(self.reg_max)
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        d1 = []
+        d2 = []
+        for i in range(self.nl):
+            d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1))
+            d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1))
+        if self.training:
+            return [d1, d2]
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5))
+            self.shape = shape
+
+        # box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1)
+        box_cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2) # onnxruntime-web
+        cut_ind = self.reg_max * 4 # onnxruntime-web
+        box = box_cls[:, :cut_ind, :] # onnxruntime-web
+        cls = box_cls[:, cut_ind:, :] # onnxruntime-web
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        # box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1)
+        box_cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2) # onnxruntime-web
+        cut_ind2 = self.reg_max * 4 # onnxruntime-web
+        box2 = box_cls2[:, :cut_ind2, :] # onnxruntime-web
+        cls2 = box_cls2[:, cut_ind2:, :] # onnxruntime-web
+        dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        y = [torch.cat((dbox, cls.sigmoid()), 1), torch.cat((dbox2, cls2.sigmoid()), 1)]
+        return y if self.export else (y, [d1, d2])
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv4, m.cv5, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class DualDDetect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch) // 2  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = make_divisible(max((ch[0] // 4, self.reg_max * 4, 16)), 4), max((ch[0], min((self.nc * 2, 128))))  # channels
+        c4, c5 = make_divisible(max((ch[self.nl] // 4, self.reg_max * 4, 16)), 4), max((ch[self.nl], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3, g=4), nn.Conv2d(c2, 4 * self.reg_max, 1, groups=4)) for x in ch[:self.nl])
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch[:self.nl])
+        self.cv4 = nn.ModuleList(
+            nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3, g=4), nn.Conv2d(c4, 4 * self.reg_max, 1, groups=4)) for x in ch[self.nl:])
+        self.cv5 = nn.ModuleList(
+            nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1)) for x in ch[self.nl:])
+        self.dfl = DFL(self.reg_max)
+        self.dfl2 = DFL(self.reg_max)
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        d1 = []
+        d2 = []
+        for i in range(self.nl):
+            d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1))
+            d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1))
+        if self.training:
+            return [d1, d2]
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5))
+            self.shape = shape
+
+        box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        y = [torch.cat((dbox, cls.sigmoid()), 1), torch.cat((dbox2, cls2.sigmoid()), 1)]
+        return y if self.export else (y, [d1, d2])
+        #y = torch.cat((dbox2, cls2.sigmoid()), 1)
+        #return y if self.export else (y, d2)
+        #y1 = torch.cat((dbox, cls.sigmoid()), 1)
+        #y2 = torch.cat((dbox2, cls2.sigmoid()), 1)
+        #return [y1, y2] if self.export else [(y1, d1), (y2, d2)]
+        #return [y1, y2] if self.export else [(y1, y2), (d1, d2)]
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv4, m.cv5, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class TripleDetect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch) // 3  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = max((ch[0] // 4, self.reg_max * 4, 16)), max((ch[0], min((self.nc * 2, 128))))  # channels
+        c4, c5 = max((ch[self.nl] // 4, self.reg_max * 4, 16)), max((ch[self.nl], min((self.nc * 2, 128))))  # channels
+        c6, c7 = max((ch[self.nl * 2] // 4, self.reg_max * 4, 16)), max((ch[self.nl * 2], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch[:self.nl])
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch[:self.nl])
+        self.cv4 = nn.ModuleList(
+            nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, 4 * self.reg_max, 1)) for x in ch[self.nl:self.nl*2])
+        self.cv5 = nn.ModuleList(
+            nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1)) for x in ch[self.nl:self.nl*2])
+        self.cv6 = nn.ModuleList(
+            nn.Sequential(Conv(x, c6, 3), Conv(c6, c6, 3), nn.Conv2d(c6, 4 * self.reg_max, 1)) for x in ch[self.nl*2:self.nl*3])
+        self.cv7 = nn.ModuleList(
+            nn.Sequential(Conv(x, c7, 3), Conv(c7, c7, 3), nn.Conv2d(c7, self.nc, 1)) for x in ch[self.nl*2:self.nl*3])
+        self.dfl = DFL(self.reg_max)
+        self.dfl2 = DFL(self.reg_max)
+        self.dfl3 = DFL(self.reg_max)
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        d1 = []
+        d2 = []
+        d3 = []
+        for i in range(self.nl):
+            d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1))
+            d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1))
+            d3.append(torch.cat((self.cv6[i](x[self.nl*2+i]), self.cv7[i](x[self.nl*2+i])), 1))
+        if self.training:
+            return [d1, d2, d3]
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5))
+            self.shape = shape
+
+        box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        box3, cls3 = torch.cat([di.view(shape[0], self.no, -1) for di in d3], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox3 = dist2bbox(self.dfl3(box3), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        y = [torch.cat((dbox, cls.sigmoid()), 1), torch.cat((dbox2, cls2.sigmoid()), 1), torch.cat((dbox3, cls3.sigmoid()), 1)]
+        return y if self.export else (y, [d1, d2, d3])
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv4, m.cv5, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv6, m.cv7, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class TripleDDetect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch) // 3  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = make_divisible(max((ch[0] // 4, self.reg_max * 4, 16)), 4), \
+                                max((ch[0], min((self.nc * 2, 128))))  # channels
+        c4, c5 = make_divisible(max((ch[self.nl] // 4, self.reg_max * 4, 16)), 4), \
+                                max((ch[self.nl], min((self.nc * 2, 128))))  # channels
+        c6, c7 = make_divisible(max((ch[self.nl * 2] // 4, self.reg_max * 4, 16)), 4), \
+                                max((ch[self.nl * 2], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3, g=4),
+                          nn.Conv2d(c2, 4 * self.reg_max, 1, groups=4)) for x in ch[:self.nl])
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch[:self.nl])
+        self.cv4 = nn.ModuleList(
+            nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3, g=4),
+                          nn.Conv2d(c4, 4 * self.reg_max, 1, groups=4)) for x in ch[self.nl:self.nl*2])
+        self.cv5 = nn.ModuleList(
+            nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1)) for x in ch[self.nl:self.nl*2])
+        self.cv6 = nn.ModuleList(
+            nn.Sequential(Conv(x, c6, 3), Conv(c6, c6, 3, g=4),
+                          nn.Conv2d(c6, 4 * self.reg_max, 1, groups=4)) for x in ch[self.nl*2:self.nl*3])
+        self.cv7 = nn.ModuleList(
+            nn.Sequential(Conv(x, c7, 3), Conv(c7, c7, 3), nn.Conv2d(c7, self.nc, 1)) for x in ch[self.nl*2:self.nl*3])
+        self.dfl = DFL(self.reg_max)
+        self.dfl2 = DFL(self.reg_max)
+        self.dfl3 = DFL(self.reg_max)
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        d1 = []
+        d2 = []
+        d3 = []
+        for i in range(self.nl):
+            d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1))
+            d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1))
+            d3.append(torch.cat((self.cv6[i](x[self.nl*2+i]), self.cv7[i](x[self.nl*2+i])), 1))
+        if self.training:
+            return [d1, d2, d3]
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5))
+            self.shape = shape
+
+        box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        box3, cls3 = torch.cat([di.view(shape[0], self.no, -1) for di in d3], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox3 = dist2bbox(self.dfl3(box3), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        #y = [torch.cat((dbox, cls.sigmoid()), 1), torch.cat((dbox2, cls2.sigmoid()), 1), torch.cat((dbox3, cls3.sigmoid()), 1)]
+        #return y if self.export else (y, [d1, d2, d3])
+        y = torch.cat((dbox3, cls3.sigmoid()), 1)
+        return y if self.export else (y, d3)
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv4, m.cv5, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv6, m.cv7, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class Segment(Detect):
+    # YOLO Segment head for segmentation models
+    def __init__(self, nc=80, nm=32, npr=256, ch=(), inplace=True):
+        super().__init__(nc, ch, inplace)
+        self.nm = nm  # number of masks
+        self.npr = npr  # number of protos
+        self.proto = Proto(ch[0], self.npr, self.nm)  # protos
+        self.detect = Detect.forward
+
+        c4 = max(ch[0] // 4, self.nm)
+        self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch)
+
+    def forward(self, x):
+        p = self.proto(x[0])
+        bs = p.shape[0]
+
+        mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2)  # mask coefficients
+        x = self.detect(self, x)
+        if self.training:
+            return x, mc, p
+        return (torch.cat([x, mc], 1), p) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p))
+
+
+class DSegment(DDetect):
+    # YOLO Segment head for segmentation models
+    def __init__(self, nc=80, nm=32, npr=256, ch=(), inplace=True):
+        super().__init__(nc, ch[:-1], inplace)
+        self.nl = len(ch)-1
+        self.nm = nm  # number of masks
+        self.npr = npr  # number of protos
+        self.proto = Conv(ch[-1], self.nm, 1)  # protos
+        self.detect = DDetect.forward
+
+        c4 = max(ch[0] // 4, self.nm)
+        self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch[:-1])
+
+    def forward(self, x):
+        p = self.proto(x[-1])
+        bs = p.shape[0]
+
+        mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2)  # mask coefficients
+        x = self.detect(self, x[:-1])
+        if self.training:
+            return x, mc, p
+        return (torch.cat([x, mc], 1), p) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p))
+
+
+class DualDSegment(DualDDetect):
+    # YOLO Segment head for segmentation models
+    def __init__(self, nc=80, nm=32, npr=256, ch=(), inplace=True):
+        super().__init__(nc, ch[:-2], inplace)
+        self.nl = (len(ch)-2) // 2
+        self.nm = nm  # number of masks
+        self.npr = npr  # number of protos
+        self.proto = Conv(ch[-2], self.nm, 1)  # protos
+        self.proto2 = Conv(ch[-1], self.nm, 1)  # protos
+        self.detect = DualDDetect.forward
+
+        c6 = max(ch[0] // 4, self.nm)
+        c7 = max(ch[self.nl] // 4, self.nm)
+        self.cv6 = nn.ModuleList(nn.Sequential(Conv(x, c6, 3), Conv(c6, c6, 3), nn.Conv2d(c6, self.nm, 1)) for x in ch[:self.nl])
+        self.cv7 = nn.ModuleList(nn.Sequential(Conv(x, c7, 3), Conv(c7, c7, 3), nn.Conv2d(c7, self.nm, 1)) for x in ch[self.nl:self.nl*2])
+
+    def forward(self, x):
+        p = [self.proto(x[-2]), self.proto2(x[-1])]
+        bs = p[0].shape[0]
+
+        mc = [torch.cat([self.cv6[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2),
+              torch.cat([self.cv7[i](x[self.nl+i]).view(bs, self.nm, -1) for i in range(self.nl)], 2)]  # mask coefficients
+        d = self.detect(self, x[:-2])
+        if self.training:
+            return d, mc, p
+        return (torch.cat([d[0][1], mc[1]], 1), (d[1][1], mc[1], p[1]))
+
+
+class Panoptic(Detect):
+    # YOLO Panoptic head for panoptic segmentation models
+    def __init__(self, nc=80, sem_nc=93, nm=32, npr=256, ch=(), inplace=True):
+        super().__init__(nc, ch, inplace)
+        self.sem_nc = sem_nc
+        self.nm = nm  # number of masks
+        self.npr = npr  # number of protos
+        self.proto = Proto(ch[0], self.npr, self.nm)  # protos
+        self.uconv = UConv(ch[0], ch[0]//4, self.sem_nc+self.nc)
+        self.detect = Detect.forward
+
+        c4 = max(ch[0] // 4, self.nm)
+        self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch)
+
+
+    def forward(self, x):
+        p = self.proto(x[0])
+        s = self.uconv(x[0])
+        bs = p.shape[0]
+
+        mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2)  # mask coefficients
+        x = self.detect(self, x)
+        if self.training:
+            return x, mc, p, s
+        return (torch.cat([x, mc], 1), p, s) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p, s))
+
+
+class BaseModel(nn.Module):
+    # YOLO base model
+    def forward(self, x, profile=False, visualize=False):
+        return self._forward_once(x, profile, visualize)  # single-scale inference, train
+
+    def _forward_once(self, x, profile=False, visualize=False):
+        y, dt = [], []  # outputs
+        for m in self.model:
+            if m.f != -1:  # if not from previous layer
+                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
+            if profile:
+                self._profile_one_layer(m, x, dt)
+            x = m(x)  # run
+            y.append(x if m.i in self.save else None)  # save output
+            if visualize:
+                feature_visualization(x, m.type, m.i, save_dir=visualize)
+        return x
+
+    def _profile_one_layer(self, m, x, dt):
+        c = m == self.model[-1]  # is final layer, copy input as inplace fix
+        o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
+        t = time_sync()
+        for _ in range(10):
+            m(x.copy() if c else x)
+        dt.append((time_sync() - t) * 100)
+        if m == self.model[0]:
+            LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  module")
+        LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}')
+        if c:
+            LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")
+
+    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
+        LOGGER.info('Fusing layers... ')
+        for m in self.model.modules():
+            if isinstance(m, (RepConvN)) and hasattr(m, 'fuse_convs'):
+                m.fuse_convs()
+                m.forward = m.forward_fuse  # update forward
+            if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
+                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
+                delattr(m, 'bn')  # remove batchnorm
+                m.forward = m.forward_fuse  # update forward
+        self.info()
+        return self
+
+    def info(self, verbose=False, img_size=640):  # print model information
+        model_info(self, verbose, img_size)
+
+    def _apply(self, fn):
+        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
+        self = super()._apply(fn)
+        m = self.model[-1]  # Detect()
+        if isinstance(m, (Detect, DualDetect, TripleDetect, DDetect, DualDDetect, TripleDDetect, Segment, DSegment, DualDSegment, Panoptic)):
+            m.stride = fn(m.stride)
+            m.anchors = fn(m.anchors)
+            m.strides = fn(m.strides)
+            # m.grid = list(map(fn, m.grid))
+        return self
+
+
+class DetectionModel(BaseModel):
+    # YOLO detection model
+    def __init__(self, cfg='yolo.yaml', ch=3, nc=None, anchors=None):  # model, input channels, number of classes
+        super().__init__()
+        if isinstance(cfg, dict):
+            self.yaml = cfg  # model dict
+        else:  # is *.yaml
+            import yaml  # for torch hub
+            self.yaml_file = Path(cfg).name
+            with open(cfg, encoding='ascii', errors='ignore') as f:
+                self.yaml = yaml.safe_load(f)  # model dict
+
+        # Define model
+        ch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channels
+        if nc and nc != self.yaml['nc']:
+            LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
+            self.yaml['nc'] = nc  # override yaml value
+        if anchors:
+            LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')
+            self.yaml['anchors'] = round(anchors)  # override yaml value
+        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist
+        self.names = [str(i) for i in range(self.yaml['nc'])]  # default names
+        self.inplace = self.yaml.get('inplace', True)
+
+        # Build strides, anchors
+        m = self.model[-1]  # Detect()
+        if isinstance(m, (Detect, DDetect, Segment, DSegment, Panoptic)):
+            s = 256  # 2x min stride
+            m.inplace = self.inplace
+            forward = lambda x: self.forward(x)[0] if isinstance(m, (Segment, DSegment, Panoptic)) else self.forward(x)
+            m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))])  # forward
+            # check_anchor_order(m)
+            # m.anchors /= m.stride.view(-1, 1, 1)
+            self.stride = m.stride
+            m.bias_init()  # only run once
+        if isinstance(m, (DualDetect, TripleDetect, DualDDetect, TripleDDetect, DualDSegment)):
+            s = 256  # 2x min stride
+            m.inplace = self.inplace
+            forward = lambda x: self.forward(x)[0][0] if isinstance(m, (DualDSegment)) else self.forward(x)[0]
+            m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))])  # forward
+            # check_anchor_order(m)
+            # m.anchors /= m.stride.view(-1, 1, 1)
+            self.stride = m.stride
+            m.bias_init()  # only run once
+
+        # Init weights, biases
+        initialize_weights(self)
+        self.info()
+        LOGGER.info('')
+
+    def forward(self, x, augment=False, profile=False, visualize=False):
+        if augment:
+            return self._forward_augment(x)  # augmented inference, None
+        return self._forward_once(x, profile, visualize)  # single-scale inference, train
+
+    def _forward_augment(self, x):
+        img_size = x.shape[-2:]  # height, width
+        s = [1, 0.83, 0.67]  # scales
+        f = [None, 3, None]  # flips (2-ud, 3-lr)
+        y = []  # outputs
+        for si, fi in zip(s, f):
+            xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
+            yi = self._forward_once(xi)[0]  # forward
+            # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
+            yi = self._descale_pred(yi, fi, si, img_size)
+            y.append(yi)
+        y = self._clip_augmented(y)  # clip augmented tails
+        return torch.cat(y, 1), None  # augmented inference, train
+
+    def _descale_pred(self, p, flips, scale, img_size):
+        # de-scale predictions following augmented inference (inverse operation)
+        if self.inplace:
+            p[..., :4] /= scale  # de-scale
+            if flips == 2:
+                p[..., 1] = img_size[0] - p[..., 1]  # de-flip ud
+            elif flips == 3:
+                p[..., 0] = img_size[1] - p[..., 0]  # de-flip lr
+        else:
+            x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale  # de-scale
+            if flips == 2:
+                y = img_size[0] - y  # de-flip ud
+            elif flips == 3:
+                x = img_size[1] - x  # de-flip lr
+            p = torch.cat((x, y, wh, p[..., 4:]), -1)
+        return p
+
+    def _clip_augmented(self, y):
+        # Clip YOLO augmented inference tails
+        nl = self.model[-1].nl  # number of detection layers (P3-P5)
+        g = sum(4 ** x for x in range(nl))  # grid points
+        e = 1  # exclude layer count
+        i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e))  # indices
+        y[0] = y[0][:, :-i]  # large
+        i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indices
+        y[-1] = y[-1][:, i:]  # small
+        return y
+
+
+Model = DetectionModel  # retain YOLO 'Model' class for backwards compatibility
+
+
+class SegmentationModel(DetectionModel):
+    # YOLO segmentation model
+    def __init__(self, cfg='yolo-seg.yaml', ch=3, nc=None, anchors=None):
+        super().__init__(cfg, ch, nc, anchors)
+
+
+class ClassificationModel(BaseModel):
+    # YOLO classification model
+    def __init__(self, cfg=None, model=None, nc=1000, cutoff=10):  # yaml, model, number of classes, cutoff index
+        super().__init__()
+        self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg)
+
+    def _from_detection_model(self, model, nc=1000, cutoff=10):
+        # Create a YOLO classification model from a YOLO detection model
+        if isinstance(model, DetectMultiBackend):
+            model = model.model  # unwrap DetectMultiBackend
+        model.model = model.model[:cutoff]  # backbone
+        m = model.model[-1]  # last layer
+        ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels  # ch into module
+        c = Classify(ch, nc)  # Classify()
+        c.i, c.f, c.type = m.i, m.f, 'models.common.Classify'  # index, from, type
+        model.model[-1] = c  # replace
+        self.model = model.model
+        self.stride = model.stride
+        self.save = []
+        self.nc = nc
+
+    def _from_yaml(self, cfg):
+        # Create a YOLO classification model from a *.yaml file
+        self.model = None
+
+
+def parse_model(d, ch):  # model_dict, input_channels(3)
+    # Parse a YOLO model.yaml dictionary
+    LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
+    anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
+    if act:
+        Conv.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
+        RepConvN.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
+        LOGGER.info(f"{colorstr('activation:')} {act}")  # print
+    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
+    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
+
+    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
+    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
+        m = eval(m) if isinstance(m, str) else m  # eval strings
+        for j, a in enumerate(args):
+            with contextlib.suppress(NameError):
+                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
+
+        n = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gain
+        if m in {
+            Conv, AConv, ConvTranspose,
+            Bottleneck, SPP, SPPF, DWConv, BottleneckCSP, nn.ConvTranspose2d, DWConvTranspose2d, SPPCSPC, ADown,
+            ELAN1, RepNCSPELAN4, SPPELAN}:
+            c1, c2 = ch[f], args[0]
+            if c2 != no:  # if not output
+                c2 = make_divisible(c2 * gw, 8)
+
+            args = [c1, c2, *args[1:]]
+            if m in {BottleneckCSP, SPPCSPC}:
+                args.insert(2, n)  # number of repeats
+                n = 1
+        elif m is nn.BatchNorm2d:
+            args = [ch[f]]
+        elif m is Concat:
+            c2 = sum(ch[x] for x in f)
+        elif m is Shortcut:
+            c2 = ch[f[0]]
+        elif m is ReOrg:
+            c2 = ch[f] * 4
+        elif m is CBLinear:
+            c2 = args[0]
+            c1 = ch[f]
+            args = [c1, c2, *args[1:]]
+        elif m is CBFuse:
+            c2 = ch[f[-1]]
+        # TODO: channel, gw, gd
+        elif m in {Detect, DualDetect, TripleDetect, DDetect, DualDDetect, TripleDDetect, Segment, DSegment, DualDSegment, Panoptic}:
+            args.append([ch[x] for x in f])
+            # if isinstance(args[1], int):  # number of anchors
+            #     args[1] = [list(range(args[1] * 2))] * len(f)
+            if m in {Segment, DSegment, DualDSegment, Panoptic}:
+                args[2] = make_divisible(args[2] * gw, 8)
+        elif m is Contract:
+            c2 = ch[f] * args[0] ** 2
+        elif m is Expand:
+            c2 = ch[f] // args[0] ** 2
+        else:
+            c2 = ch[f]
+
+        m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
+        t = str(m)[8:-2].replace('__main__.', '')  # module type
+        np = sum(x.numel() for x in m_.parameters())  # number params
+        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
+        LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f}  {t:<40}{str(args):<30}')  # print
+        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
+        layers.append(m_)
+        if i == 0:
+            ch = []
+        ch.append(c2)
+    return nn.Sequential(*layers), sorted(save)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--cfg', type=str, default='yolo.yaml', help='model.yaml')
+    parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs')
+    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--profile', action='store_true', help='profile model speed')
+    parser.add_argument('--line-profile', action='store_true', help='profile model speed layer by layer')
+    parser.add_argument('--test', action='store_true', help='test all yolo*.yaml')
+    opt = parser.parse_args()
+    opt.cfg = check_yaml(opt.cfg)  # check YAML
+    print_args(vars(opt))
+    device = select_device(opt.device)
+
+    # Create model
+    im = torch.rand(opt.batch_size, 3, 640, 640).to(device)
+    model = Model(opt.cfg).to(device)
+    model.eval()
+
+    # Options
+    if opt.line_profile:  # profile layer by layer
+        model(im, profile=True)
+
+    elif opt.profile:  # profile forward-backward
+        results = profile(input=im, ops=[model], n=3)
+
+    elif opt.test:  # test all models
+        for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'):
+            try:
+                _ = Model(cfg)
+            except Exception as e:
+                print(f'Error in {cfg}: {e}')
+
+    else:  # report fused model summary
+        model.fuse()
diff --git a/459_YOLOv9-Wholebody25/yolov9/utils/tal/anchor_generator.py b/459_YOLOv9-Wholebody25/yolov9/utils/tal/anchor_generator.py
new file mode 100755
index 0000000000..e27a5ac887
--- /dev/null
+++ b/459_YOLOv9-Wholebody25/yolov9/utils/tal/anchor_generator.py
@@ -0,0 +1,43 @@
+import torch
+
+from utils.general import check_version
+
+TORCH_1_10 = check_version(torch.__version__, '1.10.0')
+
+
+def make_anchors(feats, strides, grid_cell_offset=0.5):
+    """Generate anchors from features."""
+    anchor_points, stride_tensor = [], []
+    assert feats is not None
+    dtype, device = feats[0].dtype, feats[0].device
+    for i, stride in enumerate(strides):
+        _, _, h, w = feats[i].shape
+        sx = torch.arange(end=w, device=device, dtype=dtype) + grid_cell_offset  # shift x
+        sy = torch.arange(end=h, device=device, dtype=dtype) + grid_cell_offset  # shift y
+        sy, sx = torch.meshgrid(sy, sx, indexing='ij') if TORCH_1_10 else torch.meshgrid(sy, sx)
+        anchor_points.append(torch.stack((sx, sy), -1).view(-1, 2))
+        stride_tensor.append(torch.full((h * w, 1), stride, dtype=dtype, device=device))
+    return torch.cat(anchor_points), torch.cat(stride_tensor)
+
+
+def dist2bbox(distance, anchor_points, xywh=True, dim=-1):
+    """Transform distance(ltrb) to box(xywh or xyxy)."""
+    if dim != 1:
+        lt, rb = torch.split(distance, 2, dim)
+    else:
+        # onnxruntime-web
+        lt = distance[:, :2, :]
+        rb = distance[:, 2:, :]
+    x1y1 = anchor_points - lt
+    x2y2 = anchor_points + rb
+    if xywh:
+        c_xy = (x1y1 + x2y2) / 2
+        wh = x2y2 - x1y1
+        return torch.cat((c_xy, wh), dim)  # xywh bbox
+    return torch.cat((x1y1, x2y2), dim)  # xyxy bbox
+
+
+def bbox2dist(anchor_points, bbox, reg_max):
+    """Transform bbox(xyxy) to dist(ltrb)."""
+    x1y1, x2y2 = torch.split(bbox, 2, -1)
+    return torch.cat((anchor_points - x1y1, x2y2 - anchor_points), -1).clamp(0, reg_max - 0.01)  # dist (lt, rb)
diff --git a/461_YOLOv9-Phone/yolov9/convert_int64_to_int32.py b/461_YOLOv9-Phone/yolov9/convert_int64_to_int32.py
new file mode 100755
index 0000000000..ad213c5616
--- /dev/null
+++ b/461_YOLOv9-Phone/yolov9/convert_int64_to_int32.py
@@ -0,0 +1,123 @@
+import onnx
+
+from onnx import helper as h
+from onnx import checker as ch
+from onnx import TensorProto, GraphProto
+from onnx import numpy_helper as nph
+
+import numpy as np
+from collections import OrderedDict
+
+import logging as log
+import typer
+
+
+def make_param_dictionary(initializer):
+    params = OrderedDict()
+    for data in initializer:
+        params[data.name] = data
+    return params
+
+
+def convert_params_to_int32(params_dict):
+    converted_params = []
+    for param in params_dict:
+        data = params_dict[param]
+        if data.data_type == TensorProto.INT64:
+            data_cvt = nph.to_array(data).astype(np.int32)
+            data = nph.from_array(data_cvt, data.name)
+        converted_params += [data]
+    return converted_params
+
+
+def convert_constant_nodes_to_int32(nodes):
+    """
+    convert_constant_nodes_to_int32 Convert Constant nodes to INT32. If a constant node has data type INT64, a new version of the
+    node is created with INT32 data type and stored.
+
+    Args:
+        nodes (list): list of nodes
+
+    Returns:
+        list: list of new nodes all with INT32 constants.
+    """
+    new_nodes = []
+    for node in nodes:
+        if (
+            node.op_type == "Constant"
+            and node.attribute[0].t.data_type == TensorProto.INT64
+        ):
+            data = nph.to_array(node.attribute[0].t).astype(np.int32)
+            new_t = nph.from_array(data)
+            new_node = h.make_node(
+                "Constant",
+                inputs=[],
+                outputs=node.output,
+                name=node.name,
+                value=new_t,
+            )
+            new_nodes += [new_node]
+        else:
+            new_nodes += [node]
+
+    return new_nodes
+
+
+def convert_model_to_int32(model_path: str, out_path: str):
+    """
+    convert_model_to_int32 Converts ONNX model with INT64 params to INT32 params.\n
+
+    Args:\n
+        model_path (str): path to original ONNX model.\n
+        out_path (str): path to save converted model.
+    """
+    log.info("ONNX INT64 --> INT32 Converter")
+    log.info(f"Loading Model: {model_path}")
+    # * load model.
+    model = onnx.load_model(model_path)
+    ch.check_model(model)
+    # * get model opset version.
+    opset_version = model.opset_import[0].version
+    domain: str = model.domain
+    ir_version: int = model.ir_version
+    meta_data = {'domain': domain, 'ir_version': ir_version}
+    metadata_props = None
+    if hasattr(model, 'metadata_props'):
+        metadata_props = model.metadata_props
+    graph = model.graph
+    # * The initializer holds all non-constant weights.
+    init = graph.initializer
+    # * collect model params in a dictionary.
+    params_dict = make_param_dictionary(init)
+    log.info("Converting INT64 model params to INT32...")
+    # * convert all INT64 aprams to INT32.
+    converted_params = convert_params_to_int32(params_dict)
+    log.info("Converting constant INT64 nodes to INT32...")
+    new_nodes = convert_constant_nodes_to_int32(graph.node)
+
+    graph_name = f"{graph.name}-int32"
+    log.info("Creating new graph...")
+    # * create a new graph with converted params and new nodes.
+    graph_int32 = h.make_graph(
+        new_nodes,
+        graph_name,
+        graph.input,
+        graph.output,
+        initializer=converted_params,
+    )
+    log.info("Creating new int32 model...")
+    model_int32 = h.make_model(graph_int32, producer_name="onnx-typecast")
+    model_int32.opset_import[0].version = opset_version
+    model_int32.domain = domain
+    model_int32.ir_version = ir_version
+    if metadata_props is not None:
+        model_int32.metadata_props.extend(metadata_props)
+    ch.check_model(model_int32)
+    log.info(f"Saving converted model as: {out_path}")
+    onnx.save_model(model_int32, out_path)
+    log.info(f"Done Done London. 🎉")
+    return
+
+
+if __name__ == "__main__":
+    typer.run(convert_model_to_int32)
diff --git a/461_YOLOv9-Phone/yolov9/models/yolo.py b/461_YOLOv9-Phone/yolov9/models/yolo.py
new file mode 100755
index 0000000000..ae8821f302
--- /dev/null
+++ b/461_YOLOv9-Phone/yolov9/models/yolo.py
@@ -0,0 +1,834 @@
+import argparse
+import os
+import platform
+import sys
+from copy import deepcopy
+from pathlib import Path
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLO root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+if platform.system() != 'Windows':
+    ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.common import *
+from models.experimental import *
+from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
+from utils.plots import feature_visualization
+from utils.torch_utils import (fuse_conv_and_bn, initialize_weights, model_info, profile, scale_img, select_device,
+                               time_sync)
+from utils.tal.anchor_generator import make_anchors, dist2bbox
+
+try:
+    import thop  # for FLOPs computation
+except ImportError:
+    thop = None
+
+
+class Detect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch)  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = max((ch[0] // 4, self.reg_max * 4, 16)), max((ch[0], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch)
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch)
+        self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity()
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        for i in range(self.nl):
+            x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
+        if self.training:
+            return x
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
+            self.shape = shape
+
+        # box, cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).split((self.reg_max * 4, self.nc), 1)
+        box_cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2) # onnxruntime-web
+        cut_ind = self.reg_max * 4 # onnxruntime-web
+        box = box_cls[:, :cut_ind, :] # onnxruntime-web
+        cls = box_cls[:, cut_ind:, :] # onnxruntime-web
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        y = torch.cat((dbox, cls.sigmoid()), 1)
+        return y if self.export else (y, x)
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class DDetect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch)  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = make_divisible(max((ch[0] // 4, self.reg_max * 4, 16)), 4), max((ch[0], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3, g=4), nn.Conv2d(c2, 4 * self.reg_max, 1, groups=4)) for x in ch)
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch)
+        self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity()
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        for i in range(self.nl):
+            x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
+        if self.training:
+            return x
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
+            self.shape = shape
+
+        # box, cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).split((self.reg_max * 4, self.nc), 1)
+        box_cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2) # onnxruntime-web
+        cut_ind = self.reg_max * 4 # onnxruntime-web
+        box = box_cls[:, :cut_ind, :] # onnxruntime-web
+        cls = box_cls[:, cut_ind:, :] # onnxruntime-web
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        y = torch.cat((dbox, cls.sigmoid()), 1)
+        return y if self.export else (y, x)
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class DualDetect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch) // 2  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = max((ch[0] // 4, self.reg_max * 4, 16)), max((ch[0], min((self.nc * 2, 128))))  # channels
+        c4, c5 = max((ch[self.nl] // 4, self.reg_max * 4, 16)), max((ch[self.nl], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch[:self.nl])
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch[:self.nl])
+        self.cv4 = nn.ModuleList(
+            nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, 4 * self.reg_max, 1)) for x in ch[self.nl:])
+        self.cv5 = nn.ModuleList(
+            nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1)) for x in ch[self.nl:])
+        self.dfl = DFL(self.reg_max)
+        self.dfl2 = DFL(self.reg_max)
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        d1 = []
+        d2 = []
+        for i in range(self.nl):
+            d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1))
+            d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1))
+        if self.training:
+            return [d1, d2]
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5))
+            self.shape = shape
+
+        # box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1)
+        box_cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2) # onnxruntime-web
+        cut_ind = self.reg_max * 4 # onnxruntime-web
+        box = box_cls[:, :cut_ind, :] # onnxruntime-web
+        cls = box_cls[:, cut_ind:, :] # onnxruntime-web
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        # box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1)
+        box_cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2) # onnxruntime-web
+        cut_ind2 = self.reg_max * 4 # onnxruntime-web
+        box2 = box_cls2[:, :cut_ind2, :] # onnxruntime-web
+        cls2 = box_cls2[:, cut_ind2:, :] # onnxruntime-web
+        dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        y = [torch.cat((dbox, cls.sigmoid()), 1), torch.cat((dbox2, cls2.sigmoid()), 1)]
+        return y if self.export else (y, [d1, d2])
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv4, m.cv5, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class DualDDetect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch) // 2  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = make_divisible(max((ch[0] // 4, self.reg_max * 4, 16)), 4), max((ch[0], min((self.nc * 2, 128))))  # channels
+        c4, c5 = make_divisible(max((ch[self.nl] // 4, self.reg_max * 4, 16)), 4), max((ch[self.nl], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3, g=4), nn.Conv2d(c2, 4 * self.reg_max, 1, groups=4)) for x in ch[:self.nl])
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch[:self.nl])
+        self.cv4 = nn.ModuleList(
+            nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3, g=4), nn.Conv2d(c4, 4 * self.reg_max, 1, groups=4)) for x in ch[self.nl:])
+        self.cv5 = nn.ModuleList(
+            nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1)) for x in ch[self.nl:])
+        self.dfl = DFL(self.reg_max)
+        self.dfl2 = DFL(self.reg_max)
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        d1 = []
+        d2 = []
+        for i in range(self.nl):
+            d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1))
+            d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1))
+        if self.training:
+            return [d1, d2]
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5))
+            self.shape = shape
+
+        box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        y = [torch.cat((dbox, cls.sigmoid()), 1), torch.cat((dbox2, cls2.sigmoid()), 1)]
+        return y if self.export else (y, [d1, d2])
+        #y = torch.cat((dbox2, cls2.sigmoid()), 1)
+        #return y if self.export else (y, d2)
+        #y1 = torch.cat((dbox, cls.sigmoid()), 1)
+        #y2 = torch.cat((dbox2, cls2.sigmoid()), 1)
+        #return [y1, y2] if self.export else [(y1, d1), (y2, d2)]
+        #return [y1, y2] if self.export else [(y1, y2), (d1, d2)]
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv4, m.cv5, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class TripleDetect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch) // 3  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = max((ch[0] // 4, self.reg_max * 4, 16)), max((ch[0], min((self.nc * 2, 128))))  # channels
+        c4, c5 = max((ch[self.nl] // 4, self.reg_max * 4, 16)), max((ch[self.nl], min((self.nc * 2, 128))))  # channels
+        c6, c7 = max((ch[self.nl * 2] // 4, self.reg_max * 4, 16)), max((ch[self.nl * 2], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch[:self.nl])
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch[:self.nl])
+        self.cv4 = nn.ModuleList(
+            nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, 4 * self.reg_max, 1)) for x in ch[self.nl:self.nl*2])
+        self.cv5 = nn.ModuleList(
+            nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1)) for x in ch[self.nl:self.nl*2])
+        self.cv6 = nn.ModuleList(
+            nn.Sequential(Conv(x, c6, 3), Conv(c6, c6, 3), nn.Conv2d(c6, 4 * self.reg_max, 1)) for x in ch[self.nl*2:self.nl*3])
+        self.cv7 = nn.ModuleList(
+            nn.Sequential(Conv(x, c7, 3), Conv(c7, c7, 3), nn.Conv2d(c7, self.nc, 1)) for x in ch[self.nl*2:self.nl*3])
+        self.dfl = DFL(self.reg_max)
+        self.dfl2 = DFL(self.reg_max)
+        self.dfl3 = DFL(self.reg_max)
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        d1 = []
+        d2 = []
+        d3 = []
+        for i in range(self.nl):
+            d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1))
+            d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1))
+            d3.append(torch.cat((self.cv6[i](x[self.nl*2+i]), self.cv7[i](x[self.nl*2+i])), 1))
+        if self.training:
+            return [d1, d2, d3]
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5))
+            self.shape = shape
+
+        box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        box3, cls3 = torch.cat([di.view(shape[0], self.no, -1) for di in d3], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox3 = dist2bbox(self.dfl3(box3), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        y = [torch.cat((dbox, cls.sigmoid()), 1), torch.cat((dbox2, cls2.sigmoid()), 1), torch.cat((dbox3, cls3.sigmoid()), 1)]
+        return y if self.export else (y, [d1, d2, d3])
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv4, m.cv5, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv6, m.cv7, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class TripleDDetect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch) // 3  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = make_divisible(max((ch[0] // 4, self.reg_max * 4, 16)), 4), \
+                                max((ch[0], min((self.nc * 2, 128))))  # channels
+        c4, c5 = make_divisible(max((ch[self.nl] // 4, self.reg_max * 4, 16)), 4), \
+                                max((ch[self.nl], min((self.nc * 2, 128))))  # channels
+        c6, c7 = make_divisible(max((ch[self.nl * 2] // 4, self.reg_max * 4, 16)), 4), \
+                                max((ch[self.nl * 2], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3, g=4),
+                          nn.Conv2d(c2, 4 * self.reg_max, 1, groups=4)) for x in ch[:self.nl])
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch[:self.nl])
+        self.cv4 = nn.ModuleList(
+            nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3, g=4),
+                          nn.Conv2d(c4, 4 * self.reg_max, 1, groups=4)) for x in ch[self.nl:self.nl*2])
+        self.cv5 = nn.ModuleList(
+            nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1)) for x in ch[self.nl:self.nl*2])
+        self.cv6 = nn.ModuleList(
+            nn.Sequential(Conv(x, c6, 3), Conv(c6, c6, 3, g=4),
+                          nn.Conv2d(c6, 4 * self.reg_max, 1, groups=4)) for x in ch[self.nl*2:self.nl*3])
+        self.cv7 = nn.ModuleList(
+            nn.Sequential(Conv(x, c7, 3), Conv(c7, c7, 3), nn.Conv2d(c7, self.nc, 1)) for x in ch[self.nl*2:self.nl*3])
+        self.dfl = DFL(self.reg_max)
+        self.dfl2 = DFL(self.reg_max)
+        self.dfl3 = DFL(self.reg_max)
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        d1 = []
+        d2 = []
+        d3 = []
+        for i in range(self.nl):
+            d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1))
+            d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1))
+            d3.append(torch.cat((self.cv6[i](x[self.nl*2+i]), self.cv7[i](x[self.nl*2+i])), 1))
+        if self.training:
+            return [d1, d2, d3]
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5))
+            self.shape = shape
+
+        box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        box3, cls3 = torch.cat([di.view(shape[0], self.no, -1) for di in d3], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox3 = dist2bbox(self.dfl3(box3), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        #y = [torch.cat((dbox, cls.sigmoid()), 1), torch.cat((dbox2, cls2.sigmoid()), 1), torch.cat((dbox3, cls3.sigmoid()), 1)]
+        #return y if self.export else (y, [d1, d2, d3])
+        y = torch.cat((dbox3, cls3.sigmoid()), 1)
+        return y if self.export else (y, d3)
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv4, m.cv5, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv6, m.cv7, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class Segment(Detect):
+    # YOLO Segment head for segmentation models
+    def __init__(self, nc=80, nm=32, npr=256, ch=(), inplace=True):
+        super().__init__(nc, ch, inplace)
+        self.nm = nm  # number of masks
+        self.npr = npr  # number of protos
+        self.proto = Proto(ch[0], self.npr, self.nm)  # protos
+        self.detect = Detect.forward
+
+        c4 = max(ch[0] // 4, self.nm)
+        self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch)
+
+    def forward(self, x):
+        p = self.proto(x[0])
+        bs = p.shape[0]
+
+        mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2)  # mask coefficients
+        x = self.detect(self, x)
+        if self.training:
+            return x, mc, p
+        return (torch.cat([x, mc], 1), p) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p))
+
+
+class DSegment(DDetect):
+    # YOLO Segment head for segmentation models
+    def __init__(self, nc=80, nm=32, npr=256, ch=(), inplace=True):
+        super().__init__(nc, ch[:-1], inplace)
+        self.nl = len(ch)-1
+        self.nm = nm  # number of masks
+        self.npr = npr  # number of protos
+        self.proto = Conv(ch[-1], self.nm, 1)  # protos
+        self.detect = DDetect.forward
+
+        c4 = max(ch[0] // 4, self.nm)
+        self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch[:-1])
+
+    def forward(self, x):
+        p = self.proto(x[-1])
+        bs = p.shape[0]
+
+        mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2)  # mask coefficients
+        x = self.detect(self, x[:-1])
+        if self.training:
+            return x, mc, p
+        return (torch.cat([x, mc], 1), p) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p))
+
+
+class DualDSegment(DualDDetect):
+    # YOLO Segment head for segmentation models
+    def __init__(self, nc=80, nm=32, npr=256, ch=(), inplace=True):
+        super().__init__(nc, ch[:-2], inplace)
+        self.nl = (len(ch)-2) // 2
+        self.nm = nm  # number of masks
+        self.npr = npr  # number of protos
+        self.proto = Conv(ch[-2], self.nm, 1)  # protos
+        self.proto2 = Conv(ch[-1], self.nm, 1)  # protos
+        self.detect = DualDDetect.forward
+
+        c6 = max(ch[0] // 4, self.nm)
+        c7 = max(ch[self.nl] // 4, self.nm)
+        self.cv6 = nn.ModuleList(nn.Sequential(Conv(x, c6, 3), Conv(c6, c6, 3), nn.Conv2d(c6, self.nm, 1)) for x in ch[:self.nl])
+        self.cv7 = nn.ModuleList(nn.Sequential(Conv(x, c7, 3), Conv(c7, c7, 3), nn.Conv2d(c7, self.nm, 1)) for x in ch[self.nl:self.nl*2])
+
+    def forward(self, x):
+        p = [self.proto(x[-2]), self.proto2(x[-1])]
+        bs = p[0].shape[0]
+
+        mc = [torch.cat([self.cv6[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2),
+              torch.cat([self.cv7[i](x[self.nl+i]).view(bs, self.nm, -1) for i in range(self.nl)], 2)]  # mask coefficients
+        d = self.detect(self, x[:-2])
+        if self.training:
+            return d, mc, p
+        return (torch.cat([d[0][1], mc[1]], 1), (d[1][1], mc[1], p[1]))
+
+
+class Panoptic(Detect):
+    # YOLO Panoptic head for panoptic segmentation models
+    def __init__(self, nc=80, sem_nc=93, nm=32, npr=256, ch=(), inplace=True):
+        super().__init__(nc, ch, inplace)
+        self.sem_nc = sem_nc
+        self.nm = nm  # number of masks
+        self.npr = npr  # number of protos
+        self.proto = Proto(ch[0], self.npr, self.nm)  # protos
+        self.uconv = UConv(ch[0], ch[0]//4, self.sem_nc+self.nc)
+        self.detect = Detect.forward
+
+        c4 = max(ch[0] // 4, self.nm)
+        self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch)
+
+
+    def forward(self, x):
+        p = self.proto(x[0])
+        s = self.uconv(x[0])
+        bs = p.shape[0]
+
+        mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2)  # mask coefficients
+        x = self.detect(self, x)
+        if self.training:
+            return x, mc, p, s
+        return (torch.cat([x, mc], 1), p, s) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p, s))
+
+
+class BaseModel(nn.Module):
+    # YOLO base model
+    def forward(self, x, profile=False, visualize=False):
+        return self._forward_once(x, profile, visualize)  # single-scale inference, train
+
+    def _forward_once(self, x, profile=False, visualize=False):
+        y, dt = [], []  # outputs
+        for m in self.model:
+            if m.f != -1:  # if not from previous layer
+                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
+            if profile:
+                self._profile_one_layer(m, x, dt)
+            x = m(x)  # run
+            y.append(x if m.i in self.save else None)  # save output
+            if visualize:
+                feature_visualization(x, m.type, m.i, save_dir=visualize)
+        return x
+
+    def _profile_one_layer(self, m, x, dt):
+        c = m == self.model[-1]  # is final layer, copy input as inplace fix
+        o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
+        t = time_sync()
+        for _ in range(10):
+            m(x.copy() if c else x)
+        dt.append((time_sync() - t) * 100)
+        if m == self.model[0]:
+            LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  module")
+        LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}')
+        if c:
+            LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")
+
+    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
+        LOGGER.info('Fusing layers... ')
+        for m in self.model.modules():
+            if isinstance(m, (RepConvN)) and hasattr(m, 'fuse_convs'):
+                m.fuse_convs()
+                m.forward = m.forward_fuse  # update forward
+            if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
+                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
+                delattr(m, 'bn')  # remove batchnorm
+                m.forward = m.forward_fuse  # update forward
+        self.info()
+        return self
+
+    def info(self, verbose=False, img_size=640):  # print model information
+        model_info(self, verbose, img_size)
+
+    def _apply(self, fn):
+        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
+        self = super()._apply(fn)
+        m = self.model[-1]  # Detect()
+        if isinstance(m, (Detect, DualDetect, TripleDetect, DDetect, DualDDetect, TripleDDetect, Segment, DSegment, DualDSegment, Panoptic)):
+            m.stride = fn(m.stride)
+            m.anchors = fn(m.anchors)
+            m.strides = fn(m.strides)
+            # m.grid = list(map(fn, m.grid))
+        return self
+
+
+class DetectionModel(BaseModel):
+    # YOLO detection model
+    def __init__(self, cfg='yolo.yaml', ch=3, nc=None, anchors=None):  # model, input channels, number of classes
+        super().__init__()
+        if isinstance(cfg, dict):
+            self.yaml = cfg  # model dict
+        else:  # is *.yaml
+            import yaml  # for torch hub
+            self.yaml_file = Path(cfg).name
+            with open(cfg, encoding='ascii', errors='ignore') as f:
+                self.yaml = yaml.safe_load(f)  # model dict
+
+        # Define model
+        ch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channels
+        if nc and nc != self.yaml['nc']:
+            LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
+            self.yaml['nc'] = nc  # override yaml value
+        if anchors:
+            LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')
+            self.yaml['anchors'] = round(anchors)  # override yaml value
+        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist
+        self.names = [str(i) for i in range(self.yaml['nc'])]  # default names
+        self.inplace = self.yaml.get('inplace', True)
+
+        # Build strides, anchors
+        m = self.model[-1]  # Detect()
+        if isinstance(m, (Detect, DDetect, Segment, DSegment, Panoptic)):
+            s = 256  # 2x min stride
+            m.inplace = self.inplace
+            forward = lambda x: self.forward(x)[0] if isinstance(m, (Segment, DSegment, Panoptic)) else self.forward(x)
+            m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))])  # forward
+            # check_anchor_order(m)
+            # m.anchors /= m.stride.view(-1, 1, 1)
+            self.stride = m.stride
+            m.bias_init()  # only run once
+        if isinstance(m, (DualDetect, TripleDetect, DualDDetect, TripleDDetect, DualDSegment)):
+            s = 256  # 2x min stride
+            m.inplace = self.inplace
+            forward = lambda x: self.forward(x)[0][0] if isinstance(m, (DualDSegment)) else self.forward(x)[0]
+            m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))])  # forward
+            # check_anchor_order(m)
+            # m.anchors /= m.stride.view(-1, 1, 1)
+            self.stride = m.stride
+            m.bias_init()  # only run once
+
+        # Init weights, biases
+        initialize_weights(self)
+        self.info()
+        LOGGER.info('')
+
+    def forward(self, x, augment=False, profile=False, visualize=False):
+        if augment:
+            return self._forward_augment(x)  # augmented inference, None
+        return self._forward_once(x, profile, visualize)  # single-scale inference, train
+
+    def _forward_augment(self, x):
+        img_size = x.shape[-2:]  # height, width
+        s = [1, 0.83, 0.67]  # scales
+        f = [None, 3, None]  # flips (2-ud, 3-lr)
+        y = []  # outputs
+        for si, fi in zip(s, f):
+            xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
+            yi = self._forward_once(xi)[0]  # forward
+            # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
+            yi = self._descale_pred(yi, fi, si, img_size)
+            y.append(yi)
+        y = self._clip_augmented(y)  # clip augmented tails
+        return torch.cat(y, 1), None  # augmented inference, train
+
+    def _descale_pred(self, p, flips, scale, img_size):
+        # de-scale predictions following augmented inference (inverse operation)
+        if self.inplace:
+            p[..., :4] /= scale  # de-scale
+            if flips == 2:
+                p[..., 1] = img_size[0] - p[..., 1]  # de-flip ud
+            elif flips == 3:
+                p[..., 0] = img_size[1] - p[..., 0]  # de-flip lr
+        else:
+            x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale  # de-scale
+            if flips == 2:
+                y = img_size[0] - y  # de-flip ud
+            elif flips == 3:
+                x = img_size[1] - x  # de-flip lr
+            p = torch.cat((x, y, wh, p[..., 4:]), -1)
+        return p
+
+    def _clip_augmented(self, y):
+        # Clip YOLO augmented inference tails
+        nl = self.model[-1].nl  # number of detection layers (P3-P5)
+        g = sum(4 ** x for x in range(nl))  # grid points
+        e = 1  # exclude layer count
+        i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e))  # indices
+        y[0] = y[0][:, :-i]  # large
+        i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indices
+        y[-1] = y[-1][:, i:]  # small
+        return y
+
+
+Model = DetectionModel  # retain YOLO 'Model' class for backwards compatibility
+
+
+class SegmentationModel(DetectionModel):
+    # YOLO segmentation model
+    def __init__(self, cfg='yolo-seg.yaml', ch=3, nc=None, anchors=None):
+        super().__init__(cfg, ch, nc, anchors)
+
+
+class ClassificationModel(BaseModel):
+    # YOLO classification model
+    def __init__(self, cfg=None, model=None, nc=1000, cutoff=10):  # yaml, model, number of classes, cutoff index
+        super().__init__()
+        self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg)
+
+    def _from_detection_model(self, model, nc=1000, cutoff=10):
+        # Create a YOLO classification model from a YOLO detection model
+        if isinstance(model, DetectMultiBackend):
+            model = model.model  # unwrap DetectMultiBackend
+        model.model = model.model[:cutoff]  # backbone
+        m = model.model[-1]  # last layer
+        ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels  # ch into module
+        c = Classify(ch, nc)  # Classify()
+        c.i, c.f, c.type = m.i, m.f, 'models.common.Classify'  # index, from, type
+        model.model[-1] = c  # replace
+        self.model = model.model
+        self.stride = model.stride
+        self.save = []
+        self.nc = nc
+
+    def _from_yaml(self, cfg):
+        # Create a YOLO classification model from a *.yaml file
+        self.model = None
+
+
+def parse_model(d, ch):  # model_dict, input_channels(3)
+    # Parse a YOLO model.yaml dictionary
+    LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
+    anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
+    if act:
+        Conv.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
+        RepConvN.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
+        LOGGER.info(f"{colorstr('activation:')} {act}")  # print
+    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
+    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
+
+    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
+    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
+        m = eval(m) if isinstance(m, str) else m  # eval strings
+        for j, a in enumerate(args):
+            with contextlib.suppress(NameError):
+                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
+
+        n = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gain
+        if m in {
+            Conv, AConv, ConvTranspose,
+            Bottleneck, SPP, SPPF, DWConv, BottleneckCSP, nn.ConvTranspose2d, DWConvTranspose2d, SPPCSPC, ADown,
+            ELAN1, RepNCSPELAN4, SPPELAN}:
+            c1, c2 = ch[f], args[0]
+            if c2 != no:  # if not output
+                c2 = make_divisible(c2 * gw, 8)
+
+            args = [c1, c2, *args[1:]]
+            if m in {BottleneckCSP, SPPCSPC}:
+                args.insert(2, n)  # number of repeats
+                n = 1
+        elif m is nn.BatchNorm2d:
+            args = [ch[f]]
+        elif m is Concat:
+            c2 = sum(ch[x] for x in f)
+        elif m is Shortcut:
+            c2 = ch[f[0]]
+        elif m is ReOrg:
+            c2 = ch[f] * 4
+        elif m is CBLinear:
+            c2 = args[0]
+            c1 = ch[f]
+            args = [c1, c2, *args[1:]]
+        elif m is CBFuse:
+            c2 = ch[f[-1]]
+        # TODO: channel, gw, gd
+        elif m in {Detect, DualDetect, TripleDetect, DDetect, DualDDetect, TripleDDetect, Segment, DSegment, DualDSegment, Panoptic}:
+            args.append([ch[x] for x in f])
+            # if isinstance(args[1], int):  # number of anchors
+            #     args[1] = [list(range(args[1] * 2))] * len(f)
+            if m in {Segment, DSegment, DualDSegment, Panoptic}:
+                args[2] = make_divisible(args[2] * gw, 8)
+        elif m is Contract:
+            c2 = ch[f] * args[0] ** 2
+        elif m is Expand:
+            c2 = ch[f] // args[0] ** 2
+        else:
+            c2 = ch[f]
+
+        m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
+        t = str(m)[8:-2].replace('__main__.', '')  # module type
+        np = sum(x.numel() for x in m_.parameters())  # number params
+        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
+        LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f}  {t:<40}{str(args):<30}')  # print
+        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
+        layers.append(m_)
+        if i == 0:
+            ch = []
+        ch.append(c2)
+    return nn.Sequential(*layers), sorted(save)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--cfg', type=str, default='yolo.yaml', help='model.yaml')
+    parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs')
+    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--profile', action='store_true', help='profile model speed')
+    parser.add_argument('--line-profile', action='store_true', help='profile model speed layer by layer')
+    parser.add_argument('--test', action='store_true', help='test all yolo*.yaml')
+    opt = parser.parse_args()
+    opt.cfg = check_yaml(opt.cfg)  # check YAML
+    print_args(vars(opt))
+    device = select_device(opt.device)
+
+    # Create model
+    im = torch.rand(opt.batch_size, 3, 640, 640).to(device)
+    model = Model(opt.cfg).to(device)
+    model.eval()
+
+    # Options
+    if opt.line_profile:  # profile layer by layer
+        model(im, profile=True)
+
+    elif opt.profile:  # profile forward-backward
+        results = profile(input=im, ops=[model], n=3)
+
+    elif opt.test:  # test all models
+        for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'):
+            try:
+                _ = Model(cfg)
+            except Exception as e:
+                print(f'Error in {cfg}: {e}')
+
+    else:  # report fused model summary
+        model.fuse()
diff --git a/461_YOLOv9-Phone/yolov9/utils/tal/anchor_generator.py b/461_YOLOv9-Phone/yolov9/utils/tal/anchor_generator.py
new file mode 100755
index 0000000000..e27a5ac887
--- /dev/null
+++ b/461_YOLOv9-Phone/yolov9/utils/tal/anchor_generator.py
@@ -0,0 +1,43 @@
+import torch
+
+from utils.general import check_version
+
+TORCH_1_10 = check_version(torch.__version__, '1.10.0')
+
+
+def make_anchors(feats, strides, grid_cell_offset=0.5):
+    """Generate anchors from features."""
+    anchor_points, stride_tensor = [], []
+    assert feats is not None
+    dtype, device = feats[0].dtype, feats[0].device
+    for i, stride in enumerate(strides):
+        _, _, h, w = feats[i].shape
+        sx = torch.arange(end=w, device=device, dtype=dtype) + grid_cell_offset  # shift x
+        sy = torch.arange(end=h, device=device, dtype=dtype) + grid_cell_offset  # shift y
+        sy, sx = torch.meshgrid(sy, sx, indexing='ij') if TORCH_1_10 else torch.meshgrid(sy, sx)
+        anchor_points.append(torch.stack((sx, sy), -1).view(-1, 2))
+        stride_tensor.append(torch.full((h * w, 1), stride, dtype=dtype, device=device))
+    return torch.cat(anchor_points), torch.cat(stride_tensor)
+
+
+def dist2bbox(distance, anchor_points, xywh=True, dim=-1):
+    """Transform distance(ltrb) to box(xywh or xyxy)."""
+    if dim != 1:
+        lt, rb = torch.split(distance, 2, dim)
+    else:
+        # onnxruntime-web
+        lt = distance[:, :2, :]
+        rb = distance[:, 2:, :]
+    x1y1 = anchor_points - lt
+    x2y2 = anchor_points + rb
+    if xywh:
+        c_xy = (x1y1 + x2y2) / 2
+        wh = x2y2 - x1y1
+        return torch.cat((c_xy, wh), dim)  # xywh bbox
+    return torch.cat((x1y1, x2y2), dim)  # xyxy bbox
+
+
+def bbox2dist(anchor_points, bbox, reg_max):
+    """Transform bbox(xyxy) to dist(ltrb)."""
+    x1y1, x2y2 = torch.split(bbox, 2, -1)
+    return torch.cat((anchor_points - x1y1, x2y2 - anchor_points), -1).clamp(0, reg_max - 0.01)  # dist (lt, rb)
diff --git a/463_YOLOv9-Shoulder-Elbow-Knee/yolov9/convert_int64_to_int32.py b/463_YOLOv9-Shoulder-Elbow-Knee/yolov9/convert_int64_to_int32.py
new file mode 100755
index 0000000000..ad213c5616
--- /dev/null
+++ b/463_YOLOv9-Shoulder-Elbow-Knee/yolov9/convert_int64_to_int32.py
@@ -0,0 +1,123 @@
+import onnx
+
+from onnx import helper as h
+from onnx import checker as ch
+from onnx import TensorProto, GraphProto
+from onnx import numpy_helper as nph
+
+import numpy as np
+from collections import OrderedDict
+
+import logging as log
+import typer
+
+
+def make_param_dictionary(initializer):
+    params = OrderedDict()
+    for data in initializer:
+        params[data.name] = data
+    return params
+
+
+def convert_params_to_int32(params_dict):
+    converted_params = []
+    for param in params_dict:
+        data = params_dict[param]
+        if data.data_type == TensorProto.INT64:
+            data_cvt = nph.to_array(data).astype(np.int32)
+            data = nph.from_array(data_cvt, data.name)
+        converted_params += [data]
+    return converted_params
+
+
+def convert_constant_nodes_to_int32(nodes):
+    """
+    convert_constant_nodes_to_int32 Convert Constant nodes to INT32. If a constant node has data type INT64, a new version of the
+    node is created with INT32 data type and stored.
+
+    Args:
+        nodes (list): list of nodes
+
+    Returns:
+        list: list of new nodes all with INT32 constants.
+    """
+    new_nodes = []
+    for node in nodes:
+        if (
+            node.op_type == "Constant"
+            and node.attribute[0].t.data_type == TensorProto.INT64
+        ):
+            data = nph.to_array(node.attribute[0].t).astype(np.int32)
+            new_t = nph.from_array(data)
+            new_node = h.make_node(
+                "Constant",
+                inputs=[],
+                outputs=node.output,
+                name=node.name,
+                value=new_t,
+            )
+            new_nodes += [new_node]
+        else:
+            new_nodes += [node]
+
+    return new_nodes
+
+
+def convert_model_to_int32(model_path: str, out_path: str):
+    """
+    convert_model_to_int32 Converts ONNX model with INT64 params to INT32 params.\n
+
+    Args:\n
+        model_path (str): path to original ONNX model.\n
+        out_path (str): path to save converted model.
+    """
+    log.info("ONNX INT64 --> INT32 Converter")
+    log.info(f"Loading Model: {model_path}")
+    # * load model.
+    model = onnx.load_model(model_path)
+    ch.check_model(model)
+    # * get model opset version.
+    opset_version = model.opset_import[0].version
+    domain: str = model.domain
+    ir_version: int = model.ir_version
+    meta_data = {'domain': domain, 'ir_version': ir_version}
+    metadata_props = None
+    if hasattr(model, 'metadata_props'):
+        metadata_props = model.metadata_props
+    graph = model.graph
+    # * The initializer holds all non-constant weights.
+    init = graph.initializer
+    # * collect model params in a dictionary.
+    params_dict = make_param_dictionary(init)
+    log.info("Converting INT64 model params to INT32...")
+    # * convert all INT64 aprams to INT32.
+    converted_params = convert_params_to_int32(params_dict)
+    log.info("Converting constant INT64 nodes to INT32...")
+    new_nodes = convert_constant_nodes_to_int32(graph.node)
+
+    graph_name = f"{graph.name}-int32"
+    log.info("Creating new graph...")
+    # * create a new graph with converted params and new nodes.
+    graph_int32 = h.make_graph(
+        new_nodes,
+        graph_name,
+        graph.input,
+        graph.output,
+        initializer=converted_params,
+    )
+    log.info("Creating new int32 model...")
+    model_int32 = h.make_model(graph_int32, producer_name="onnx-typecast")
+    model_int32.opset_import[0].version = opset_version
+    model_int32.domain = domain
+    model_int32.ir_version = ir_version
+    if metadata_props is not None:
+        model_int32.metadata_props.extend(metadata_props)
+    ch.check_model(model_int32)
+    log.info(f"Saving converted model as: {out_path}")
+    onnx.save_model(model_int32, out_path)
+    log.info(f"Done Done London. 🎉")
+    return
+
+
+if __name__ == "__main__":
+    typer.run(convert_model_to_int32)
diff --git a/463_YOLOv9-Shoulder-Elbow-Knee/yolov9/models/yolo.py b/463_YOLOv9-Shoulder-Elbow-Knee/yolov9/models/yolo.py
new file mode 100755
index 0000000000..ae8821f302
--- /dev/null
+++ b/463_YOLOv9-Shoulder-Elbow-Knee/yolov9/models/yolo.py
@@ -0,0 +1,834 @@
+import argparse
+import os
+import platform
+import sys
+from copy import deepcopy
+from pathlib import Path
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLO root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+if platform.system() != 'Windows':
+    ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.common import *
+from models.experimental import *
+from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
+from utils.plots import feature_visualization
+from utils.torch_utils import (fuse_conv_and_bn, initialize_weights, model_info, profile, scale_img, select_device,
+                               time_sync)
+from utils.tal.anchor_generator import make_anchors, dist2bbox
+
+try:
+    import thop  # for FLOPs computation
+except ImportError:
+    thop = None
+
+
+class Detect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch)  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = max((ch[0] // 4, self.reg_max * 4, 16)), max((ch[0], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch)
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch)
+        self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity()
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        for i in range(self.nl):
+            x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
+        if self.training:
+            return x
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
+            self.shape = shape
+
+        # box, cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).split((self.reg_max * 4, self.nc), 1)
+        box_cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2) # onnxruntime-web
+        cut_ind = self.reg_max * 4 # onnxruntime-web
+        box = box_cls[:, :cut_ind, :] # onnxruntime-web
+        cls = box_cls[:, cut_ind:, :] # onnxruntime-web
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        y = torch.cat((dbox, cls.sigmoid()), 1)
+        return y if self.export else (y, x)
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class DDetect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch)  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = make_divisible(max((ch[0] // 4, self.reg_max * 4, 16)), 4), max((ch[0], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3, g=4), nn.Conv2d(c2, 4 * self.reg_max, 1, groups=4)) for x in ch)
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch)
+        self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity()
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        for i in range(self.nl):
+            x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
+        if self.training:
+            return x
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
+            self.shape = shape
+
+        # box, cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).split((self.reg_max * 4, self.nc), 1)
+        box_cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2) # onnxruntime-web
+        cut_ind = self.reg_max * 4 # onnxruntime-web
+        box = box_cls[:, :cut_ind, :] # onnxruntime-web
+        cls = box_cls[:, cut_ind:, :] # onnxruntime-web
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        y = torch.cat((dbox, cls.sigmoid()), 1)
+        return y if self.export else (y, x)
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class DualDetect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch) // 2  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = max((ch[0] // 4, self.reg_max * 4, 16)), max((ch[0], min((self.nc * 2, 128))))  # channels
+        c4, c5 = max((ch[self.nl] // 4, self.reg_max * 4, 16)), max((ch[self.nl], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch[:self.nl])
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch[:self.nl])
+        self.cv4 = nn.ModuleList(
+            nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, 4 * self.reg_max, 1)) for x in ch[self.nl:])
+        self.cv5 = nn.ModuleList(
+            nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1)) for x in ch[self.nl:])
+        self.dfl = DFL(self.reg_max)
+        self.dfl2 = DFL(self.reg_max)
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        d1 = []
+        d2 = []
+        for i in range(self.nl):
+            d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1))
+            d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1))
+        if self.training:
+            return [d1, d2]
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5))
+            self.shape = shape
+
+        # box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1)
+        box_cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2) # onnxruntime-web
+        cut_ind = self.reg_max * 4 # onnxruntime-web
+        box = box_cls[:, :cut_ind, :] # onnxruntime-web
+        cls = box_cls[:, cut_ind:, :] # onnxruntime-web
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        # box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1)
+        box_cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2) # onnxruntime-web
+        cut_ind2 = self.reg_max * 4 # onnxruntime-web
+        box2 = box_cls2[:, :cut_ind2, :] # onnxruntime-web
+        cls2 = box_cls2[:, cut_ind2:, :] # onnxruntime-web
+        dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        y = [torch.cat((dbox, cls.sigmoid()), 1), torch.cat((dbox2, cls2.sigmoid()), 1)]
+        return y if self.export else (y, [d1, d2])
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv4, m.cv5, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class DualDDetect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch) // 2  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = make_divisible(max((ch[0] // 4, self.reg_max * 4, 16)), 4), max((ch[0], min((self.nc * 2, 128))))  # channels
+        c4, c5 = make_divisible(max((ch[self.nl] // 4, self.reg_max * 4, 16)), 4), max((ch[self.nl], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3, g=4), nn.Conv2d(c2, 4 * self.reg_max, 1, groups=4)) for x in ch[:self.nl])
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch[:self.nl])
+        self.cv4 = nn.ModuleList(
+            nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3, g=4), nn.Conv2d(c4, 4 * self.reg_max, 1, groups=4)) for x in ch[self.nl:])
+        self.cv5 = nn.ModuleList(
+            nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1)) for x in ch[self.nl:])
+        self.dfl = DFL(self.reg_max)
+        self.dfl2 = DFL(self.reg_max)
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        d1 = []
+        d2 = []
+        for i in range(self.nl):
+            d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1))
+            d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1))
+        if self.training:
+            return [d1, d2]
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5))
+            self.shape = shape
+
+        box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        y = [torch.cat((dbox, cls.sigmoid()), 1), torch.cat((dbox2, cls2.sigmoid()), 1)]
+        return y if self.export else (y, [d1, d2])
+        #y = torch.cat((dbox2, cls2.sigmoid()), 1)
+        #return y if self.export else (y, d2)
+        #y1 = torch.cat((dbox, cls.sigmoid()), 1)
+        #y2 = torch.cat((dbox2, cls2.sigmoid()), 1)
+        #return [y1, y2] if self.export else [(y1, d1), (y2, d2)]
+        #return [y1, y2] if self.export else [(y1, y2), (d1, d2)]
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv4, m.cv5, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class TripleDetect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch) // 3  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = max((ch[0] // 4, self.reg_max * 4, 16)), max((ch[0], min((self.nc * 2, 128))))  # channels
+        c4, c5 = max((ch[self.nl] // 4, self.reg_max * 4, 16)), max((ch[self.nl], min((self.nc * 2, 128))))  # channels
+        c6, c7 = max((ch[self.nl * 2] // 4, self.reg_max * 4, 16)), max((ch[self.nl * 2], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch[:self.nl])
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch[:self.nl])
+        self.cv4 = nn.ModuleList(
+            nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, 4 * self.reg_max, 1)) for x in ch[self.nl:self.nl*2])
+        self.cv5 = nn.ModuleList(
+            nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1)) for x in ch[self.nl:self.nl*2])
+        self.cv6 = nn.ModuleList(
+            nn.Sequential(Conv(x, c6, 3), Conv(c6, c6, 3), nn.Conv2d(c6, 4 * self.reg_max, 1)) for x in ch[self.nl*2:self.nl*3])
+        self.cv7 = nn.ModuleList(
+            nn.Sequential(Conv(x, c7, 3), Conv(c7, c7, 3), nn.Conv2d(c7, self.nc, 1)) for x in ch[self.nl*2:self.nl*3])
+        self.dfl = DFL(self.reg_max)
+        self.dfl2 = DFL(self.reg_max)
+        self.dfl3 = DFL(self.reg_max)
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        d1 = []
+        d2 = []
+        d3 = []
+        for i in range(self.nl):
+            d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1))
+            d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1))
+            d3.append(torch.cat((self.cv6[i](x[self.nl*2+i]), self.cv7[i](x[self.nl*2+i])), 1))
+        if self.training:
+            return [d1, d2, d3]
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5))
+            self.shape = shape
+
+        box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        box3, cls3 = torch.cat([di.view(shape[0], self.no, -1) for di in d3], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox3 = dist2bbox(self.dfl3(box3), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        y = [torch.cat((dbox, cls.sigmoid()), 1), torch.cat((dbox2, cls2.sigmoid()), 1), torch.cat((dbox3, cls3.sigmoid()), 1)]
+        return y if self.export else (y, [d1, d2, d3])
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv4, m.cv5, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv6, m.cv7, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class TripleDDetect(nn.Module):
+    # YOLO Detect head for detection models
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=(), inplace=True):  # detection layer
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch) // 3  # number of detection layers
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+
+        c2, c3 = make_divisible(max((ch[0] // 4, self.reg_max * 4, 16)), 4), \
+                                max((ch[0], min((self.nc * 2, 128))))  # channels
+        c4, c5 = make_divisible(max((ch[self.nl] // 4, self.reg_max * 4, 16)), 4), \
+                                max((ch[self.nl], min((self.nc * 2, 128))))  # channels
+        c6, c7 = make_divisible(max((ch[self.nl * 2] // 4, self.reg_max * 4, 16)), 4), \
+                                max((ch[self.nl * 2], min((self.nc * 2, 128))))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3, g=4),
+                          nn.Conv2d(c2, 4 * self.reg_max, 1, groups=4)) for x in ch[:self.nl])
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch[:self.nl])
+        self.cv4 = nn.ModuleList(
+            nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3, g=4),
+                          nn.Conv2d(c4, 4 * self.reg_max, 1, groups=4)) for x in ch[self.nl:self.nl*2])
+        self.cv5 = nn.ModuleList(
+            nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1)) for x in ch[self.nl:self.nl*2])
+        self.cv6 = nn.ModuleList(
+            nn.Sequential(Conv(x, c6, 3), Conv(c6, c6, 3, g=4),
+                          nn.Conv2d(c6, 4 * self.reg_max, 1, groups=4)) for x in ch[self.nl*2:self.nl*3])
+        self.cv7 = nn.ModuleList(
+            nn.Sequential(Conv(x, c7, 3), Conv(c7, c7, 3), nn.Conv2d(c7, self.nc, 1)) for x in ch[self.nl*2:self.nl*3])
+        self.dfl = DFL(self.reg_max)
+        self.dfl2 = DFL(self.reg_max)
+        self.dfl3 = DFL(self.reg_max)
+
+    def forward(self, x):
+        shape = x[0].shape  # BCHW
+        d1 = []
+        d2 = []
+        d3 = []
+        for i in range(self.nl):
+            d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1))
+            d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1))
+            d3.append(torch.cat((self.cv6[i](x[self.nl*2+i]), self.cv7[i](x[self.nl*2+i])), 1))
+        if self.training:
+            return [d1, d2, d3]
+        elif self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5))
+            self.shape = shape
+
+        box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        box3, cls3 = torch.cat([di.view(shape[0], self.no, -1) for di in d3], 2).split((self.reg_max * 4, self.nc), 1)
+        dbox3 = dist2bbox(self.dfl3(box3), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        #y = [torch.cat((dbox, cls.sigmoid()), 1), torch.cat((dbox2, cls2.sigmoid()), 1), torch.cat((dbox3, cls3.sigmoid()), 1)]
+        #return y if self.export else (y, [d1, d2, d3])
+        y = torch.cat((dbox3, cls3.sigmoid()), 1)
+        return y if self.export else (y, d3)
+
+    def bias_init(self):
+        # Initialize Detect() biases, WARNING: requires stride availability
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv4, m.cv5, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+        for a, b, s in zip(m.cv6, m.cv7, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (5 objects and 80 classes per 640 image)
+
+
+class Segment(Detect):
+    # YOLO Segment head for segmentation models
+    def __init__(self, nc=80, nm=32, npr=256, ch=(), inplace=True):
+        super().__init__(nc, ch, inplace)
+        self.nm = nm  # number of masks
+        self.npr = npr  # number of protos
+        self.proto = Proto(ch[0], self.npr, self.nm)  # protos
+        self.detect = Detect.forward
+
+        c4 = max(ch[0] // 4, self.nm)
+        self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch)
+
+    def forward(self, x):
+        p = self.proto(x[0])
+        bs = p.shape[0]
+
+        mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2)  # mask coefficients
+        x = self.detect(self, x)
+        if self.training:
+            return x, mc, p
+        return (torch.cat([x, mc], 1), p) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p))
+
+
+class DSegment(DDetect):
+    # YOLO Segment head for segmentation models
+    def __init__(self, nc=80, nm=32, npr=256, ch=(), inplace=True):
+        super().__init__(nc, ch[:-1], inplace)
+        self.nl = len(ch)-1
+        self.nm = nm  # number of masks
+        self.npr = npr  # number of protos
+        self.proto = Conv(ch[-1], self.nm, 1)  # protos
+        self.detect = DDetect.forward
+
+        c4 = max(ch[0] // 4, self.nm)
+        self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch[:-1])
+
+    def forward(self, x):
+        p = self.proto(x[-1])
+        bs = p.shape[0]
+
+        mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2)  # mask coefficients
+        x = self.detect(self, x[:-1])
+        if self.training:
+            return x, mc, p
+        return (torch.cat([x, mc], 1), p) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p))
+
+
+class DualDSegment(DualDDetect):
+    # YOLO Segment head for segmentation models
+    def __init__(self, nc=80, nm=32, npr=256, ch=(), inplace=True):
+        super().__init__(nc, ch[:-2], inplace)
+        self.nl = (len(ch)-2) // 2
+        self.nm = nm  # number of masks
+        self.npr = npr  # number of protos
+        self.proto = Conv(ch[-2], self.nm, 1)  # protos
+        self.proto2 = Conv(ch[-1], self.nm, 1)  # protos
+        self.detect = DualDDetect.forward
+
+        c6 = max(ch[0] // 4, self.nm)
+        c7 = max(ch[self.nl] // 4, self.nm)
+        self.cv6 = nn.ModuleList(nn.Sequential(Conv(x, c6, 3), Conv(c6, c6, 3), nn.Conv2d(c6, self.nm, 1)) for x in ch[:self.nl])
+        self.cv7 = nn.ModuleList(nn.Sequential(Conv(x, c7, 3), Conv(c7, c7, 3), nn.Conv2d(c7, self.nm, 1)) for x in ch[self.nl:self.nl*2])
+
+    def forward(self, x):
+        p = [self.proto(x[-2]), self.proto2(x[-1])]
+        bs = p[0].shape[0]
+
+        mc = [torch.cat([self.cv6[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2),
+              torch.cat([self.cv7[i](x[self.nl+i]).view(bs, self.nm, -1) for i in range(self.nl)], 2)]  # mask coefficients
+        d = self.detect(self, x[:-2])
+        if self.training:
+            return d, mc, p
+        return (torch.cat([d[0][1], mc[1]], 1), (d[1][1], mc[1], p[1]))
+
+
+class Panoptic(Detect):
+    # YOLO Panoptic head for panoptic segmentation models
+    def __init__(self, nc=80, sem_nc=93, nm=32, npr=256, ch=(), inplace=True):
+        super().__init__(nc, ch, inplace)
+        self.sem_nc = sem_nc
+        self.nm = nm  # number of masks
+        self.npr = npr  # number of protos
+        self.proto = Proto(ch[0], self.npr, self.nm)  # protos
+        self.uconv = UConv(ch[0], ch[0]//4, self.sem_nc+self.nc)
+        self.detect = Detect.forward
+
+        c4 = max(ch[0] // 4, self.nm)
+        self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch)
+
+
+    def forward(self, x):
+        p = self.proto(x[0])
+        s = self.uconv(x[0])
+        bs = p.shape[0]
+
+        mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2)  # mask coefficients
+        x = self.detect(self, x)
+        if self.training:
+            return x, mc, p, s
+        return (torch.cat([x, mc], 1), p, s) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p, s))
+
+
+class BaseModel(nn.Module):
+    # YOLO base model
+    def forward(self, x, profile=False, visualize=False):
+        return self._forward_once(x, profile, visualize)  # single-scale inference, train
+
+    def _forward_once(self, x, profile=False, visualize=False):
+        y, dt = [], []  # outputs
+        for m in self.model:
+            if m.f != -1:  # if not from previous layer
+                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
+            if profile:
+                self._profile_one_layer(m, x, dt)
+            x = m(x)  # run
+            y.append(x if m.i in self.save else None)  # save output
+            if visualize:
+                feature_visualization(x, m.type, m.i, save_dir=visualize)
+        return x
+
+    def _profile_one_layer(self, m, x, dt):
+        c = m == self.model[-1]  # is final layer, copy input as inplace fix
+        o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
+        t = time_sync()
+        for _ in range(10):
+            m(x.copy() if c else x)
+        dt.append((time_sync() - t) * 100)
+        if m == self.model[0]:
+            LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  module")
+        LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}')
+        if c:
+            LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")
+
+    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
+        LOGGER.info('Fusing layers... ')
+        for m in self.model.modules():
+            if isinstance(m, (RepConvN)) and hasattr(m, 'fuse_convs'):
+                m.fuse_convs()
+                m.forward = m.forward_fuse  # update forward
+            if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
+                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
+                delattr(m, 'bn')  # remove batchnorm
+                m.forward = m.forward_fuse  # update forward
+        self.info()
+        return self
+
+    def info(self, verbose=False, img_size=640):  # print model information
+        model_info(self, verbose, img_size)
+
+    def _apply(self, fn):
+        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
+        self = super()._apply(fn)
+        m = self.model[-1]  # Detect()
+        if isinstance(m, (Detect, DualDetect, TripleDetect, DDetect, DualDDetect, TripleDDetect, Segment, DSegment, DualDSegment, Panoptic)):
+            m.stride = fn(m.stride)
+            m.anchors = fn(m.anchors)
+            m.strides = fn(m.strides)
+            # m.grid = list(map(fn, m.grid))
+        return self
+
+
+class DetectionModel(BaseModel):
+    # YOLO detection model
+    def __init__(self, cfg='yolo.yaml', ch=3, nc=None, anchors=None):  # model, input channels, number of classes
+        super().__init__()
+        if isinstance(cfg, dict):
+            self.yaml = cfg  # model dict
+        else:  # is *.yaml
+            import yaml  # for torch hub
+            self.yaml_file = Path(cfg).name
+            with open(cfg, encoding='ascii', errors='ignore') as f:
+                self.yaml = yaml.safe_load(f)  # model dict
+
+        # Define model
+        ch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channels
+        if nc and nc != self.yaml['nc']:
+            LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
+            self.yaml['nc'] = nc  # override yaml value
+        if anchors:
+            LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')
+            self.yaml['anchors'] = round(anchors)  # override yaml value
+        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist
+        self.names = [str(i) for i in range(self.yaml['nc'])]  # default names
+        self.inplace = self.yaml.get('inplace', True)
+
+        # Build strides, anchors
+        m = self.model[-1]  # Detect()
+        if isinstance(m, (Detect, DDetect, Segment, DSegment, Panoptic)):
+            s = 256  # 2x min stride
+            m.inplace = self.inplace
+            forward = lambda x: self.forward(x)[0] if isinstance(m, (Segment, DSegment, Panoptic)) else self.forward(x)
+            m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))])  # forward
+            # check_anchor_order(m)
+            # m.anchors /= m.stride.view(-1, 1, 1)
+            self.stride = m.stride
+            m.bias_init()  # only run once
+        if isinstance(m, (DualDetect, TripleDetect, DualDDetect, TripleDDetect, DualDSegment)):
+            s = 256  # 2x min stride
+            m.inplace = self.inplace
+            forward = lambda x: self.forward(x)[0][0] if isinstance(m, (DualDSegment)) else self.forward(x)[0]
+            m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))])  # forward
+            # check_anchor_order(m)
+            # m.anchors /= m.stride.view(-1, 1, 1)
+            self.stride = m.stride
+            m.bias_init()  # only run once
+
+        # Init weights, biases
+        initialize_weights(self)
+        self.info()
+        LOGGER.info('')
+
+    def forward(self, x, augment=False, profile=False, visualize=False):
+        if augment:
+            return self._forward_augment(x)  # augmented inference, None
+        return self._forward_once(x, profile, visualize)  # single-scale inference, train
+
+    def _forward_augment(self, x):
+        img_size = x.shape[-2:]  # height, width
+        s = [1, 0.83, 0.67]  # scales
+        f = [None, 3, None]  # flips (2-ud, 3-lr)
+        y = []  # outputs
+        for si, fi in zip(s, f):
+            xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
+            yi = self._forward_once(xi)[0]  # forward
+            # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
+            yi = self._descale_pred(yi, fi, si, img_size)
+            y.append(yi)
+        y = self._clip_augmented(y)  # clip augmented tails
+        return torch.cat(y, 1), None  # augmented inference, train
+
+    def _descale_pred(self, p, flips, scale, img_size):
+        # de-scale predictions following augmented inference (inverse operation)
+        if self.inplace:
+            p[..., :4] /= scale  # de-scale
+            if flips == 2:
+                p[..., 1] = img_size[0] - p[..., 1]  # de-flip ud
+            elif flips == 3:
+                p[..., 0] = img_size[1] - p[..., 0]  # de-flip lr
+        else:
+            x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale  # de-scale
+            if flips == 2:
+                y = img_size[0] - y  # de-flip ud
+            elif flips == 3:
+                x = img_size[1] - x  # de-flip lr
+            p = torch.cat((x, y, wh, p[..., 4:]), -1)
+        return p
+
+    def _clip_augmented(self, y):
+        # Clip YOLO augmented inference tails
+        nl = self.model[-1].nl  # number of detection layers (P3-P5)
+        g = sum(4 ** x for x in range(nl))  # grid points
+        e = 1  # exclude layer count
+        i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e))  # indices
+        y[0] = y[0][:, :-i]  # large
+        i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indices
+        y[-1] = y[-1][:, i:]  # small
+        return y
+
+
+Model = DetectionModel  # retain YOLO 'Model' class for backwards compatibility
+
+
+class SegmentationModel(DetectionModel):
+    # YOLO segmentation model
+    def __init__(self, cfg='yolo-seg.yaml', ch=3, nc=None, anchors=None):
+        super().__init__(cfg, ch, nc, anchors)
+
+
+class ClassificationModel(BaseModel):
+    # YOLO classification model
+    def __init__(self, cfg=None, model=None, nc=1000, cutoff=10):  # yaml, model, number of classes, cutoff index
+        super().__init__()
+        self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg)
+
+    def _from_detection_model(self, model, nc=1000, cutoff=10):
+        # Create a YOLO classification model from a YOLO detection model
+        if isinstance(model, DetectMultiBackend):
+            model = model.model  # unwrap DetectMultiBackend
+        model.model = model.model[:cutoff]  # backbone
+        m = model.model[-1]  # last layer
+        ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels  # ch into module
+        c = Classify(ch, nc)  # Classify()
+        c.i, c.f, c.type = m.i, m.f, 'models.common.Classify'  # index, from, type
+        model.model[-1] = c  # replace
+        self.model = model.model
+        self.stride = model.stride
+        self.save = []
+        self.nc = nc
+
+    def _from_yaml(self, cfg):
+        # Create a YOLO classification model from a *.yaml file
+        self.model = None
+
+
+def parse_model(d, ch):  # model_dict, input_channels(3)
+    # Parse a YOLO model.yaml dictionary
+    LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
+    anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
+    if act:
+        Conv.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
+        RepConvN.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
+        LOGGER.info(f"{colorstr('activation:')} {act}")  # print
+    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
+    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
+
+    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
+    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
+        m = eval(m) if isinstance(m, str) else m  # eval strings
+        for j, a in enumerate(args):
+            with contextlib.suppress(NameError):
+                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
+
+        n = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gain
+        if m in {
+            Conv, AConv, ConvTranspose,
+            Bottleneck, SPP, SPPF, DWConv, BottleneckCSP, nn.ConvTranspose2d, DWConvTranspose2d, SPPCSPC, ADown,
+            ELAN1, RepNCSPELAN4, SPPELAN}:
+            c1, c2 = ch[f], args[0]
+            if c2 != no:  # if not output
+                c2 = make_divisible(c2 * gw, 8)
+
+            args = [c1, c2, *args[1:]]
+            if m in {BottleneckCSP, SPPCSPC}:
+                args.insert(2, n)  # number of repeats
+                n = 1
+        elif m is nn.BatchNorm2d:
+            args = [ch[f]]
+        elif m is Concat:
+            c2 = sum(ch[x] for x in f)
+        elif m is Shortcut:
+            c2 = ch[f[0]]
+        elif m is ReOrg:
+            c2 = ch[f] * 4
+        elif m is CBLinear:
+            c2 = args[0]
+            c1 = ch[f]
+            args = [c1, c2, *args[1:]]
+        elif m is CBFuse:
+            c2 = ch[f[-1]]
+        # TODO: channel, gw, gd
+        elif m in {Detect, DualDetect, TripleDetect, DDetect, DualDDetect, TripleDDetect, Segment, DSegment, DualDSegment, Panoptic}:
+            args.append([ch[x] for x in f])
+            # if isinstance(args[1], int):  # number of anchors
+            #     args[1] = [list(range(args[1] * 2))] * len(f)
+            if m in {Segment, DSegment, DualDSegment, Panoptic}:
+                args[2] = make_divisible(args[2] * gw, 8)
+        elif m is Contract:
+            c2 = ch[f] * args[0] ** 2
+        elif m is Expand:
+            c2 = ch[f] // args[0] ** 2
+        else:
+            c2 = ch[f]
+
+        m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
+        t = str(m)[8:-2].replace('__main__.', '')  # module type
+        np = sum(x.numel() for x in m_.parameters())  # number params
+        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
+        LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f}  {t:<40}{str(args):<30}')  # print
+        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
+        layers.append(m_)
+        if i == 0:
+            ch = []
+        ch.append(c2)
+    return nn.Sequential(*layers), sorted(save)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--cfg', type=str, default='yolo.yaml', help='model.yaml')
+    parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs')
+    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--profile', action='store_true', help='profile model speed')
+    parser.add_argument('--line-profile', action='store_true', help='profile model speed layer by layer')
+    parser.add_argument('--test', action='store_true', help='test all yolo*.yaml')
+    opt = parser.parse_args()
+    opt.cfg = check_yaml(opt.cfg)  # check YAML
+    print_args(vars(opt))
+    device = select_device(opt.device)
+
+    # Create model
+    im = torch.rand(opt.batch_size, 3, 640, 640).to(device)
+    model = Model(opt.cfg).to(device)
+    model.eval()
+
+    # Options
+    if opt.line_profile:  # profile layer by layer
+        model(im, profile=True)
+
+    elif opt.profile:  # profile forward-backward
+        results = profile(input=im, ops=[model], n=3)
+
+    elif opt.test:  # test all models
+        for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'):
+            try:
+                _ = Model(cfg)
+            except Exception as e:
+                print(f'Error in {cfg}: {e}')
+
+    else:  # report fused model summary
+        model.fuse()
diff --git a/463_YOLOv9-Shoulder-Elbow-Knee/yolov9/utils/tal/anchor_generator.py b/463_YOLOv9-Shoulder-Elbow-Knee/yolov9/utils/tal/anchor_generator.py
new file mode 100755
index 0000000000..e27a5ac887
--- /dev/null
+++ b/463_YOLOv9-Shoulder-Elbow-Knee/yolov9/utils/tal/anchor_generator.py
@@ -0,0 +1,43 @@
+import torch
+
+from utils.general import check_version
+
+TORCH_1_10 = check_version(torch.__version__, '1.10.0')
+
+
+def make_anchors(feats, strides, grid_cell_offset=0.5):
+    """Generate anchors from features."""
+    anchor_points, stride_tensor = [], []
+    assert feats is not None
+    dtype, device = feats[0].dtype, feats[0].device
+    for i, stride in enumerate(strides):
+        _, _, h, w = feats[i].shape
+        sx = torch.arange(end=w, device=device, dtype=dtype) + grid_cell_offset  # shift x
+        sy = torch.arange(end=h, device=device, dtype=dtype) + grid_cell_offset  # shift y
+        sy, sx = torch.meshgrid(sy, sx, indexing='ij') if TORCH_1_10 else torch.meshgrid(sy, sx)
+        anchor_points.append(torch.stack((sx, sy), -1).view(-1, 2))
+        stride_tensor.append(torch.full((h * w, 1), stride, dtype=dtype, device=device))
+    return torch.cat(anchor_points), torch.cat(stride_tensor)
+
+
+def dist2bbox(distance, anchor_points, xywh=True, dim=-1):
+    """Transform distance(ltrb) to box(xywh or xyxy)."""
+    if dim != 1:
+        lt, rb = torch.split(distance, 2, dim)
+    else:
+        # onnxruntime-web
+        lt = distance[:, :2, :]
+        rb = distance[:, 2:, :]
+    x1y1 = anchor_points - lt
+    x2y2 = anchor_points + rb
+    if xywh:
+        c_xy = (x1y1 + x2y2) / 2
+        wh = x2y2 - x1y1
+        return torch.cat((c_xy, wh), dim)  # xywh bbox
+    return torch.cat((x1y1, x2y2), dim)  # xyxy bbox
+
+
+def bbox2dist(anchor_points, bbox, reg_max):
+    """Transform bbox(xyxy) to dist(ltrb)."""
+    x1y1, x2y2 = torch.split(bbox, 2, -1)
+    return torch.cat((anchor_points - x1y1, x2y2 - anchor_points), -1).clamp(0, reg_max - 0.01)  # dist (lt, rb)