yolov5-qat

main.py

+import argparse
+import os
+import sys
+import time
+from copy import deepcopy
+from pathlib import Path
+import quantization.quantize as quantize
+from scripts.qat import export_onnx, create_coco_train_dataloader, create_coco_val_dataloader, evaluate_coco
+from models.common import Conv
+
+import numpy as np
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+import yaml
+from tqdm import tqdm
+from models.yolo import Model
+from tqdm import tqdm
+from utils.loss import ComputeLoss
+from utils.dataloaders import create_dataloader
+from utils.torch_utils import (
+    smart_optimizer,
+)
+
+from utils.general import (
+    LOGGER,
+    check_dataset,
+    check_yaml,
+    colorstr,
+    labels_to_class_weights,
+)
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+
+# ================== 构建数据集 ==================================
+def create_coco_train_dataloader(cocodir, batch_size=10):
+
+    with open("data/hyps/hyp.scratch-low.yaml") as f:
+        hyp = yaml.load(f, Loader=yaml.SafeLoader)  # load hyps
+
+    loader, dataset = create_dataloader(
+        f"{cocodir}/train2017.txt", 
+        imgsz=640, 
+        batch_size=batch_size, 
+        augment=True, hyp=hyp, rect=False, cache=False, stride=32,pad=0, image_weights=False)
+    return loader, dataset
+
+
+def create_coco_val_dataloader(cocodir, batch_size=10, keep_images=None):
+
+    loader, dataset = create_dataloader(
+        f"{cocodir}/val2017.txt", 
+        imgsz=640, 
+        batch_size=32, 
+        augment=False, hyp=None, rect=True, cache=False,stride=32,pad=0.5, image_weights=False)
+
+    def subclass_len(self):
+        if keep_images is not None:
+            return keep_images
+        return len(self.img_files)
+
+    loader.dataset.__len__ = subclass_len
+    return loader, dataset
+
+
+def load_yolov5_model(weight, device) -> Model:
+    if 'yolov5l' in weight:
+        cfg = "models/yolov5l.yaml"
+    elif 'yolov5m' in weight:
+        cfg = "models/yolov5m.yaml"
+    elif 'yolov5n' in weight:
+        cfg = "models/yolov5n.yaml"
+    elif 'yolov5s' in weight:
+        cfg = "models/yolov5s.yaml"
+    elif "yolov5x" in weight:
+        cfg = "models/yolov5x.yaml"
+    else:
+        raise NotImplementedError("Only support yolov5[l, m, n, s, x]")
+    
+    model = Model(cfg=cfg).to(device)
+    weight = torch.load(weight, map_location=device)["model"].state_dict()
+    model.load_state_dict(weight,strict=False)
+    for m in model.modules():
+        if type(m) is nn.Upsample:
+            m.recompute_scale_factor = None  # torch 1.11.0 compatibility
+        elif type(m) is Conv:
+            m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
+            
+    model.float()
+    model.eval()
+
+    with torch.no_grad():
+        model.fuse()
+    return model
+
+
+def qat(hyp, opt, device):
+    # 加载超参数
+    if isinstance(hyp, str):
+        with open(hyp, errors="ignore") as f:
+            hyp = yaml.safe_load(f)  # load hyps dict
+    LOGGER.info(colorstr("hyperparameters: ") + ", ".join(f"{k}={v}" for k, v in hyp.items()))
+    opt.hyp = hyp.copy()  # for saving hyps to checkpoints
+    
+    # ========================== 加载数据集 ==============================
+    data_dict = check_dataset(opt.data)
+    nc = int(data_dict["nc"])
+    names = data_dict["names"]
+    
+    train_dataloader, dataset = create_coco_train_dataloader(opt.cocodir, opt.batch_size)
+    test_dataloader, _ = create_coco_val_dataloader(opt.cocodir, opt.batch_size)
+
+    # =========================== 原始模型及属性 ==============================
+    model = load_yolov5_model(opt.weights, device)
+    
+    nl = model.model[-1].nl
+    hyp["box"] *= 3 / nl  # scale to layers
+    hyp["cls"] *= nc / 80 * 3 / nl  # scale to classes and layers
+    hyp["obj"] *= (opt.imgsz / 640) ** 2 * 3 / nl  # scale to image size and layers
+    model.nc = nc  # attach number of classes to model
+    model.hyp = hyp  # attach hyperparameters to model
+    model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc  # attach class weights
+    model.names = names
+    
+    # =========================== QAT模型 ===============
+    quantize.replace_bottleneck_forward(model)
+    quantize.replace_to_quantization_module(model, ignore_policy=None, all_node_with_qdq=opt.all_node_with_qdq)
+    if not opt.all_node_with_qdq:
+        quantize.apply_custom_rules_to_quantizer(model, export_onnx)
+    quantize.calibrate_model(model, train_dataloader, device)
+    
+    # # ========================== 训练 ====================
+    compute_loss = ComputeLoss(model)  # init loss class
+    # optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
+    optimizer = smart_optimizer(model, 'SGD', 1e-3, 0.9, 1e-5)
+    
+    for epoch in tqdm(range(1, opt.epochs+1)):
+        model.train()
+        for i, (imgs, targets, paths, _) in enumerate(train_dataloader):
+            pred = model(imgs.to(device))
+            loss, loss_items = compute_loss(pred, targets.to(device))
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+            
+        with quantize.disable_quantization(model.model[24]):
+            ap = evaluate_coco(model, test_dataloader, True)
+            
+    try:
+        save_path = "checkpoints/qat/yolov5s_qat.pt"
+        if os.path.isfile(save_path):
+            os.remove(save_path)
+    except Exception as e:
+        pass
+    finally:
+        torch.save(model, save_path)
+    
+
+def parse_opt(known=False):
+    """Parses command-line arguments for YOLOv5 training, validation, and testing."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="initial weights path")
+    parser.add_argument("--cfg", type=str, default="", help="model.yaml path")
+    parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path")
+    parser.add_argument("--cocodir", type=str, default="/home/temp/coco2017")
+    parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path")
+    parser.add_argument("--epochs", type=int, default=5, help="total training epochs")
+    parser.add_argument("--batch-size", type=int, default=128, help="total batch size for all GPUs, -1 for autobatch")
+    parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)")
+    parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor")
+    parser.add_argument("--all_node_with_qdq", action="store_true")
+    parser.add_argument("--anchors", type=int, default=3)
+    
+    parser.add_argument("--nc", type=int, default=80)
+
+    return parser.parse_known_args()[0] if known else parser.parse_args()
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    opt.cfg, opt.hyp, opt.weights = (
+        check_yaml(opt.cfg),
+        check_yaml(opt.hyp),
+        str(opt.weights),
+    )  # checks
+    
+    qat(opt.hyp, opt, device=torch.device("cuda:2"))
\ No newline at end of file

model.properties

+# 模型唯一标识
+modelCode=576
+# 模型名称
+modelName=yolov5-qat_pytorch
+# 模型描述
+modelDescription=YOLOv5是一种目标检测模型，使用QAT训练并量化可以在保证性能基本一致的前提下获得更快的推理速度。
+# 应用场景
+appScenario=训练,推理,目标检测,金融,交通,教育
+# 框架类型
+frameType=pytorch

models/common.py

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Common modules
+"""
+
+import ast
+import contextlib
+import json
+import math
+import platform
+import warnings
+import zipfile
+from collections import OrderedDict, namedtuple
+from copy import copy
+from pathlib import Path
+from urllib.parse import urlparse
+
+import cv2
+import numpy as np
+import pandas as pd
+import requests
+import torch
+import torch.nn as nn
+from IPython.display import display
+from PIL import Image
+from torch.cuda import amp
+
+from utils import TryExcept
+from utils.dataloaders import exif_transpose, letterbox
+from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,
+                           increment_path, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy,
+                           xyxy2xywh, yaml_load)
+from utils.plots import Annotator, colors, save_one_box
+from utils.torch_utils import copy_attr, smart_inference_mode
+
+
+def autopad(k, p=None, d=1):  # kernel, padding, dilation
+    # Pad to 'same' shape outputs
+    if d > 1:
+        k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]  # actual kernel-size
+    if p is None:
+        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
+    return p
+
+
+class Conv(nn.Module):
+    # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
+    default_act = nn.SiLU()  # default activation
+
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
+        super().__init__()
+        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
+        self.bn = nn.BatchNorm2d(c2)
+        self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
+
+    def forward(self, x):
+        return self.act(self.bn(self.conv(x)))
+
+    def forward_fuse(self, x):
+        return self.act(self.conv(x))
+
+
+class DWConv(Conv):
+    # Depth-wise convolution
+    def __init__(self, c1, c2, k=1, s=1, d=1, act=True):  # ch_in, ch_out, kernel, stride, dilation, activation
+        super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)
+
+
+class DWConvTranspose2d(nn.ConvTranspose2d):
+    # Depth-wise transpose convolution
+    def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):  # ch_in, ch_out, kernel, stride, padding, padding_out
+        super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))
+
+
+class TransformerLayer(nn.Module):
+    # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
+    def __init__(self, c, num_heads):
+        super().__init__()
+        self.q = nn.Linear(c, c, bias=False)
+        self.k = nn.Linear(c, c, bias=False)
+        self.v = nn.Linear(c, c, bias=False)
+        self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
+        self.fc1 = nn.Linear(c, c, bias=False)
+        self.fc2 = nn.Linear(c, c, bias=False)
+
+    def forward(self, x):
+        x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
+        x = self.fc2(self.fc1(x)) + x
+        return x
+
+
+class TransformerBlock(nn.Module):
+    # Vision Transformer https://arxiv.org/abs/2010.11929
+    def __init__(self, c1, c2, num_heads, num_layers):
+        super().__init__()
+        self.conv = None
+        if c1 != c2:
+            self.conv = Conv(c1, c2)
+        self.linear = nn.Linear(c2, c2)  # learnable position embedding
+        self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers)))
+        self.c2 = c2
+
+    def forward(self, x):
+        if self.conv is not None:
+            x = self.conv(x)
+        b, _, w, h = x.shape
+        p = x.flatten(2).permute(2, 0, 1)
+        return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h)
+
+
+class Bottleneck(nn.Module):
+    # Standard bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c_, c2, 3, 1, g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class BottleneckCSP(nn.Module):
+    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
+        self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
+        self.cv4 = Conv(2 * c_, c2, 1, 1)
+        self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
+        self.act = nn.SiLU()
+        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
+        self.concat = Concat(1)
+        
+    def forward(self, x):
+        y1 = self.cv3(self.m(self.cv1(x)))
+        y2 = self.cv2(x)
+        return self.cv4(self.act(self.bn(self.concat((y1, y2)))))
+
+
+class CrossConv(nn.Module):
+    # Cross Convolution Downsample
+    def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
+        # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, (1, k), (1, s))
+        self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class C3(nn.Module):
+    # CSP Bottleneck with 3 convolutions
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c1, c_, 1, 1)
+        self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)
+        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
+        self.concat =Concat(1)
+    def forward(self, x):
+        return self.cv3(self.concat((self.m(self.cv1(x)), self.cv2(x))))
+
+
+class C3x(C3):
+    # C3 module with cross-convolutions
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
+        super().__init__(c1, c2, n, shortcut, g, e)
+        c_ = int(c2 * e)
+        self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)))
+
+
+class C3TR(C3):
+    # C3 module with TransformerBlock()
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
+        super().__init__(c1, c2, n, shortcut, g, e)
+        c_ = int(c2 * e)
+        self.m = TransformerBlock(c_, c_, 4, n)
+
+
+class C3SPP(C3):
+    # C3 module with SPP()
+    def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
+        super().__init__(c1, c2, n, shortcut, g, e)
+        c_ = int(c2 * e)
+        self.m = SPP(c_, c_, k)
+
+
+class C3Ghost(C3):
+    # C3 module with GhostBottleneck()
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
+        super().__init__(c1, c2, n, shortcut, g, e)
+        c_ = int(c2 * e)  # hidden channels
+        self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))
+
+
+class SPP(nn.Module):
+    # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
+    def __init__(self, c1, c2, k=(5, 9, 13)):
+        super().__init__()
+        c_ = c1 // 2  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
+        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
+        self.concat =Concat(1)
+    def forward(self, x):
+        x = self.cv1(x)
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
+            return self.cv2(self.concat([x] + [m(x) for m in self.m]))
+
+
+class SPPF(nn.Module):
+    # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
+    def __init__(self, c1, c2, k=5):  # equivalent to SPP(k=(5, 9, 13))
+        super().__init__()
+        c_ = c1 // 2  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c_ * 4, c2, 1, 1)
+        self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
+        self.concat =Concat(1)
+        
+    def forward(self, x):
+        x = self.cv1(x)
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
+            y1 = self.m(x)
+            y2 = self.m(y1)
+            return self.cv2(self.concat((x, y1, y2, self.m(y2))))
+
+
+class Focus(nn.Module):
+    # Focus wh information into c-space
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
+        super().__init__()
+        self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act)
+        # self.contract = Contract(gain=2)
+        self.concat =Concat(1)
+    def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
+        return self.conv(self.concat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2])))
+        # return self.conv(self.contract(x))
+
+
+class GhostConv(nn.Module):
+    # Ghost Convolution https://github.com/huawei-noah/ghostnet
+    def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out, kernel, stride, groups
+        super().__init__()
+        c_ = c2 // 2  # hidden channels
+        self.cv1 = Conv(c1, c_, k, s, None, g, act=act)
+        self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act)
+        self.concat = Concat(1)
+        
+    def forward(self, x):
+        y = self.cv1(x)
+        return self.concat((y, self.cv2(y)))
+
+
+class GhostBottleneck(nn.Module):
+    # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
+    def __init__(self, c1, c2, k=3, s=1):  # ch_in, ch_out, kernel, stride
+        super().__init__()
+        c_ = c2 // 2
+        self.conv = nn.Sequential(
+            GhostConv(c1, c_, 1, 1),  # pw
+            DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),  # dw
+            GhostConv(c_, c2, 1, 1, act=False))  # pw-linear
+        self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1,
+                                                                            act=False)) if s == 2 else nn.Identity()
+
+    def forward(self, x):
+        return self.conv(x) + self.shortcut(x)
+
+
+class Contract(nn.Module):
+    # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
+    def __init__(self, gain=2):
+        super().__init__()
+        self.gain = gain
+
+    def forward(self, x):
+        b, c, h, w = x.size()  # assert (h / s == 0) and (W / s == 0), 'Indivisible gain'
+        s = self.gain
+        x = x.view(b, c, h // s, s, w // s, s)  # x(1,64,40,2,40,2)
+        x = x.permute(0, 3, 5, 1, 2, 4).contiguous()  # x(1,2,2,64,40,40)
+        return x.view(b, c * s * s, h // s, w // s)  # x(1,256,40,40)
+
+
+class Expand(nn.Module):
+    # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
+    def __init__(self, gain=2):
+        super().__init__()
+        self.gain = gain
+
+    def forward(self, x):
+        b, c, h, w = x.size()  # assert C / s ** 2 == 0, 'Indivisible gain'
+        s = self.gain
+        x = x.view(b, s, s, c // s ** 2, h, w)  # x(1,2,2,16,80,80)
+        x = x.permute(0, 3, 4, 1, 5, 2).contiguous()  # x(1,16,80,2,80,2)
+        return x.view(b, c // s ** 2, h * s, w * s)  # x(1,16,160,160)
+
+
+class Concat(nn.Module):
+    # Concatenate a list of tensors along dimension
+    def __init__(self, dimension=1):
+        super().__init__()
+        self.d = dimension
+
+    def forward(self, x):
+        return torch.cat(x, self.d)
+
+
+class DetectMultiBackend(nn.Module):
+    # YOLOv5 MultiBackend class for python inference on various backends
+    def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True):
+        # Usage:
+        #   PyTorch:              weights = *.pt
+        #   TorchScript:                    *.torchscript
+        #   ONNX Runtime:                   *.onnx
+        #   ONNX OpenCV DNN:                *.onnx --dnn
+        #   OpenVINO:                       *_openvino_model
+        #   CoreML:                         *.mlmodel
+        #   TensorRT:                       *.engine
+        #   TensorFlow SavedModel:          *_saved_model
+        #   TensorFlow GraphDef:            *.pb
+        #   TensorFlow Lite:                *.tflite
+        #   TensorFlow Edge TPU:            *_edgetpu.tflite
+        #   PaddlePaddle:                   *_paddle_model
+        from models.experimental import attempt_download, attempt_load  # scoped to avoid circular import
+
+        super().__init__()
+        w = str(weights[0] if isinstance(weights, list) else weights)
+        pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)
+        fp16 &= pt or jit or onnx or engine  # FP16
+        nhwc = coreml or saved_model or pb or tflite or edgetpu  # BHWC formats (vs torch BCWH)
+        stride = 32  # default stride
+        cuda = torch.cuda.is_available() and device.type != 'cpu'  # use CUDA
+        if not (pt or triton):
+            w = attempt_download(w)  # download if not local
+
+        if pt:  # PyTorch
+            model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)
+            stride = max(int(model.stride.max()), 32)  # model stride
+            names = model.module.names if hasattr(model, 'module') else model.names  # get class names
+            model.half() if fp16 else model.float()
+            self.model = model  # explicitly assign for to(), cpu(), cuda(), half()
+        elif jit:  # TorchScript
+            LOGGER.info(f'Loading {w} for TorchScript inference...')
+            extra_files = {'config.txt': ''}  # model metadata
+            model = torch.jit.load(w, _extra_files=extra_files, map_location=device)
+            model.half() if fp16 else model.float()
+            if extra_files['config.txt']:  # load metadata dict
+                d = json.loads(extra_files['config.txt'],
+                               object_hook=lambda d: {int(k) if k.isdigit() else k: v
+                                                      for k, v in d.items()})
+                stride, names = int(d['stride']), d['names']
+        elif dnn:  # ONNX OpenCV DNN
+            LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
+            check_requirements('opencv-python>=4.5.4')
+            net = cv2.dnn.readNetFromONNX(w)
+        elif onnx:  # ONNX Runtime
+            LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
+            check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
+            import onnxruntime
+            providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
+            session = onnxruntime.InferenceSession(w, providers=providers)
+            output_names = [x.name for x in session.get_outputs()]
+            meta = session.get_modelmeta().custom_metadata_map  # metadata
+            if 'stride' in meta:
+                stride, names = int(meta['stride']), eval(meta['names'])
+        elif xml:  # OpenVINO
+            LOGGER.info(f'Loading {w} for OpenVINO inference...')
+            check_requirements('openvino')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
+            from openvino.runtime import Core, Layout, get_batch
+            ie = Core()
+            if not Path(w).is_file():  # if not *.xml
+                w = next(Path(w).glob('*.xml'))  # get *.xml file from *_openvino_model dir
+            network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
+            if network.get_parameters()[0].get_layout().empty:
+                network.get_parameters()[0].set_layout(Layout("NCHW"))
+            batch_dim = get_batch(network)
+            if batch_dim.is_static:
+                batch_size = batch_dim.get_length()
+            executable_network = ie.compile_model(network, device_name="CPU")  # device_name="MYRIAD" for Intel NCS2
+            stride, names = self._load_metadata(Path(w).with_suffix('.yaml'))  # load metadata
+        elif engine:  # TensorRT
+            LOGGER.info(f'Loading {w} for TensorRT inference...')
+            import tensorrt as trt  # https://developer.nvidia.com/nvidia-tensorrt-download
+            check_version(trt.__version__, '7.0.0', hard=True)  # require tensorrt>=7.0.0
+            if device.type == 'cpu':
+                device = torch.device('cuda:0')
+            Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
+            logger = trt.Logger(trt.Logger.INFO)
+            with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
+                model = runtime.deserialize_cuda_engine(f.read())
+            context = model.create_execution_context()
+            bindings = OrderedDict()
+            output_names = []
+            fp16 = False  # default updated below
+            dynamic = False
+            for i in range(model.num_bindings):
+                name = model.get_binding_name(i)
+                dtype = trt.nptype(model.get_binding_dtype(i))
+                if model.binding_is_input(i):
+                    if -1 in tuple(model.get_binding_shape(i)):  # dynamic
+                        dynamic = True
+                        context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))
+                    if dtype == np.float16:
+                        fp16 = True
+                else:  # output
+                    output_names.append(name)
+                shape = tuple(context.get_binding_shape(i))
+                im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
+                bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
+            binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
+            batch_size = bindings['images'].shape[0]  # if dynamic, this is instead max batch size
+        elif coreml:  # CoreML
+            LOGGER.info(f'Loading {w} for CoreML inference...')
+            import coremltools as ct
+            model = ct.models.MLModel(w)
+        elif saved_model:  # TF SavedModel
+            LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
+            import tensorflow as tf
+            keras = False  # assume TF1 saved_model
+            model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)
+        elif pb:  # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
+            LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')
+            import tensorflow as tf
+
+            def wrap_frozen_graph(gd, inputs, outputs):
+                x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), [])  # wrapped
+                ge = x.graph.as_graph_element
+                return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
+
+            def gd_outputs(gd):
+                name_list, input_list = [], []
+                for node in gd.node:  # tensorflow.core.framework.node_def_pb2.NodeDef
+                    name_list.append(node.name)
+                    input_list.extend(node.input)
+                return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))
+
+            gd = tf.Graph().as_graph_def()  # TF GraphDef
+            with open(w, 'rb') as f:
+                gd.ParseFromString(f.read())
+            frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd))
+        elif tflite or edgetpu:  # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
+            try:  # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
+                from tflite_runtime.interpreter import Interpreter, load_delegate
+            except ImportError:
+                import tensorflow as tf
+                Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,
+            if edgetpu:  # TF Edge TPU https://coral.ai/software/#edgetpu-runtime
+                LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
+                delegate = {
+                    'Linux': 'libedgetpu.so.1',
+                    'Darwin': 'libedgetpu.1.dylib',
+                    'Windows': 'edgetpu.dll'}[platform.system()]
+                interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
+            else:  # TFLite
+                LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
+                interpreter = Interpreter(model_path=w)  # load TFLite model
+            interpreter.allocate_tensors()  # allocate
+            input_details = interpreter.get_input_details()  # inputs
+            output_details = interpreter.get_output_details()  # outputs
+            # load metadata
+            with contextlib.suppress(zipfile.BadZipFile):
+                with zipfile.ZipFile(w, "r") as model:
+                    meta_file = model.namelist()[0]
+                    meta = ast.literal_eval(model.read(meta_file).decode("utf-8"))
+                    stride, names = int(meta['stride']), meta['names']
+        elif tfjs:  # TF.js
+            raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported')
+        elif paddle:  # PaddlePaddle
+            LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
+            check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
+            import paddle.inference as pdi
+            if not Path(w).is_file():  # if not *.pdmodel
+                w = next(Path(w).rglob('*.pdmodel'))  # get *.pdmodel file from *_paddle_model dir
+            weights = Path(w).with_suffix('.pdiparams')
+            config = pdi.Config(str(w), str(weights))
+            if cuda:
+                config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)
+            predictor = pdi.create_predictor(config)
+            input_handle = predictor.get_input_handle(predictor.get_input_names()[0])
+            output_names = predictor.get_output_names()
+        elif triton:  # NVIDIA Triton Inference Server
+            LOGGER.info(f'Using {w} as Triton Inference Server...')
+            check_requirements('tritonclient[all]')
+            from utils.triton import TritonRemoteModel
+            model = TritonRemoteModel(url=w)
+            nhwc = model.runtime.startswith("tensorflow")
+        else:
+            raise NotImplementedError(f'ERROR: {w} is not a supported format')
+
+        # class names
+        if 'names' not in locals():
+            names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)}
+        if names[0] == 'n01440764' and len(names) == 1000:  # ImageNet
+            names = yaml_load(ROOT / 'data/ImageNet.yaml')['names']  # human-readable names
+
+        self.__dict__.update(locals())  # assign all variables to self
+
+    def forward(self, im, augment=False, visualize=False):
+        # YOLOv5 MultiBackend inference
+        b, ch, h, w = im.shape  # batch, channel, height, width
+        if self.fp16 and im.dtype != torch.float16:
+            im = im.half()  # to FP16
+        if self.nhwc:
+            im = im.permute(0, 2, 3, 1)  # torch BCHW to numpy BHWC shape(1,320,192,3)
+
+        if self.pt:  # PyTorch
+            y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
+        elif self.jit:  # TorchScript
+            y = self.model(im)
+        elif self.dnn:  # ONNX OpenCV DNN
+            im = im.cpu().numpy()  # torch to numpy
+            self.net.setInput(im)
+            y = self.net.forward()
+        elif self.onnx:  # ONNX Runtime
+            im = im.cpu().numpy()  # torch to numpy
+            y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
+        elif self.xml:  # OpenVINO
+            im = im.cpu().numpy()  # FP32
+            y = list(self.executable_network([im]).values())
+        elif self.engine:  # TensorRT
+            if self.dynamic and im.shape != self.bindings['images'].shape:
+                i = self.model.get_binding_index('images')
+                self.context.set_binding_shape(i, im.shape)  # reshape if dynamic
+                self.bindings['images'] = self.bindings['images']._replace(shape=im.shape)
+                for name in self.output_names:
+                    i = self.model.get_binding_index(name)
+                    self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))
+            s = self.bindings['images'].shape
+            assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}"
+            self.binding_addrs['images'] = int(im.data_ptr())
+            self.context.execute_v2(list(self.binding_addrs.values()))
+            y = [self.bindings[x].data for x in sorted(self.output_names)]
+        elif self.coreml:  # CoreML
+            im = im.cpu().numpy()
+            im = Image.fromarray((im[0] * 255).astype('uint8'))
+            # im = im.resize((192, 320), Image.ANTIALIAS)
+            y = self.model.predict({'image': im})  # coordinates are xywh normalized
+            if 'confidence' in y:
+                box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]])  # xyxy pixels
+                conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
+                y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
+            else:
+                y = list(reversed(y.values()))  # reversed for segmentation models (pred, proto)
+        elif self.paddle:  # PaddlePaddle
+            im = im.cpu().numpy().astype(np.float32)
+            self.input_handle.copy_from_cpu(im)
+            self.predictor.run()
+            y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]
+        elif self.triton:  # NVIDIA Triton Inference Server
+            y = self.model(im)
+        else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
+            im = im.cpu().numpy()
+            if self.saved_model:  # SavedModel
+                y = self.model(im, training=False) if self.keras else self.model(im)
+            elif self.pb:  # GraphDef
+                y = self.frozen_func(x=self.tf.constant(im))
+            else:  # Lite or Edge TPU
+                input = self.input_details[0]
+                int8 = input['dtype'] == np.uint8  # is TFLite quantized uint8 model
+                if int8:
+                    scale, zero_point = input['quantization']
+                    im = (im / scale + zero_point).astype(np.uint8)  # de-scale
+                self.interpreter.set_tensor(input['index'], im)
+                self.interpreter.invoke()
+                y = []
+                for output in self.output_details:
+                    x = self.interpreter.get_tensor(output['index'])
+                    if int8:
+                        scale, zero_point = output['quantization']
+                        x = (x.astype(np.float32) - zero_point) * scale  # re-scale
+                    y.append(x)
+            y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]
+            y[0][..., :4] *= [w, h, w, h]  # xywh normalized to pixels
+
+        if isinstance(y, (list, tuple)):
+            return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]
+        else:
+            return self.from_numpy(y)
+
+    def from_numpy(self, x):
+        return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x
+
+    def warmup(self, imgsz=(1, 3, 640, 640)):
+        # Warmup model by running inference once
+        warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton
+        if any(warmup_types) and (self.device.type != 'cpu' or self.triton):
+            im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device)  # input
+            for _ in range(2 if self.jit else 1):  #
+                self.forward(im)  # warmup
+
+    @staticmethod
+    def _model_type(p='path/to/model.pt'):
+        # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
+        # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]
+        from export import export_formats
+        from utils.downloads import is_url
+        sf = list(export_formats().Suffix)  # export suffixes
+        if not is_url(p, check=False):
+            check_suffix(p, sf)  # checks
+        url = urlparse(p)  # if url may be Triton inference server
+        types = [s in Path(p).name for s in sf]
+        types[8] &= not types[9]  # tflite &= not edgetpu
+        triton = not any(types) and all([any(s in url.scheme for s in ["http", "grpc"]), url.netloc])
+        return types + [triton]
+
+    @staticmethod
+    def _load_metadata(f=Path('path/to/meta.yaml')):
+        # Load metadata from meta.yaml if it exists
+        if f.exists():
+            d = yaml_load(f)
+            return d['stride'], d['names']  # assign stride, names
+        return None, None
+
+
+class AutoShape(nn.Module):
+    # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
+    conf = 0.25  # NMS confidence threshold
+    iou = 0.45  # NMS IoU threshold
+    agnostic = False  # NMS class-agnostic
+    multi_label = False  # NMS multiple labels per box
+    classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
+    max_det = 1000  # maximum number of detections per image
+    amp = False  # Automatic Mixed Precision (AMP) inference
+
+    def __init__(self, model, verbose=True):
+        super().__init__()
+        if verbose:
+            LOGGER.info('Adding AutoShape... ')
+        copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=())  # copy attributes
+        self.dmb = isinstance(model, DetectMultiBackend)  # DetectMultiBackend() instance
+        self.pt = not self.dmb or model.pt  # PyTorch model
+        self.model = model.eval()
+        if self.pt:
+            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
+            m.inplace = False  # Detect.inplace=False for safe multithread inference
+            m.export = True  # do not output loss values
+
+    def _apply(self, fn):
+        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
+        self = super()._apply(fn)
+        if self.pt:
+            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
+            m.stride = fn(m.stride)
+            m.grid = list(map(fn, m.grid))
+            if isinstance(m.anchor_grid, list):
+                m.anchor_grid = list(map(fn, m.anchor_grid))
+        return self
+
+    @smart_inference_mode()
+    def forward(self, ims, size=640, augment=False, profile=False):
+        # Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:
+        #   file:        ims = 'data/images/zidane.jpg'  # str or PosixPath
+        #   URI:             = 'https://ultralytics.com/images/zidane.jpg'
+        #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(640,1280,3)
+        #   PIL:             = Image.open('image.jpg') or ImageGrab.grab()  # HWC x(640,1280,3)
+        #   numpy:           = np.zeros((640,1280,3))  # HWC
+        #   torch:           = torch.zeros(16,3,320,640)  # BCHW (scaled to size=640, 0-1 values)
+        #   multiple:        = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
+
+        dt = (Profile(), Profile(), Profile())
+        with dt[0]:
+            if isinstance(size, int):  # expand
+                size = (size, size)
+            p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device)  # param
+            autocast = self.amp and (p.device.type != 'cpu')  # Automatic Mixed Precision (AMP) inference
+            if isinstance(ims, torch.Tensor):  # torch
+                with amp.autocast(autocast):
+                    return self.model(ims.to(p.device).type_as(p), augment=augment)  # inference
+
+            # Pre-process
+            n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims])  # number, list of images
+            shape0, shape1, files = [], [], []  # image and inference shapes, filenames
+            for i, im in enumerate(ims):
+                f = f'image{i}'  # filename
+                if isinstance(im, (str, Path)):  # filename or uri
+                    im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
+                    im = np.asarray(exif_transpose(im))
+                elif isinstance(im, Image.Image):  # PIL Image
+                    im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f
+                files.append(Path(f).with_suffix('.jpg').name)
+                if im.shape[0] < 5:  # image in CHW
+                    im = im.transpose((1, 2, 0))  # reverse dataloader .transpose(2, 0, 1)
+                im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)  # enforce 3ch input
+                s = im.shape[:2]  # HWC
+                shape0.append(s)  # image shape
+                g = max(size) / max(s)  # gain
+                shape1.append([int(y * g) for y in s])
+                ims[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
+            shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)]  # inf shape
+            x = [letterbox(im, shape1, auto=False)[0] for im in ims]  # pad
+            x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2)))  # stack and BHWC to BCHW
+            x = torch.from_numpy(x).to(p.device).type_as(p) / 255  # uint8 to fp16/32
+
+        with amp.autocast(autocast):
+            # Inference
+            with dt[1]:
+                y = self.model(x, augment=augment)  # forward
+
+            # Post-process
+            with dt[2]:
+                y = non_max_suppression(y if self.dmb else y[0],
+                                        self.conf,
+                                        self.iou,
+                                        self.classes,
+                                        self.agnostic,
+                                        self.multi_label,
+                                        max_det=self.max_det)  # NMS
+                for i in range(n):
+                    scale_boxes(shape1, y[i][:, :4], shape0[i])
+
+            return Detections(ims, y, files, dt, self.names, x.shape)
+
+
+class Detections:
+    # YOLOv5 detections class for inference results
+    def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
+        super().__init__()
+        d = pred[0].device  # device
+        gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims]  # normalizations
+        self.ims = ims  # list of images as numpy arrays
+        self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
+        self.names = names  # class names
+        self.files = files  # image filenames
+        self.times = times  # profiling times
+        self.xyxy = pred  # xyxy pixels
+        self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
+        self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
+        self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
+        self.n = len(self.pred)  # number of images (batch size)
+        self.t = tuple(x.t / self.n * 1E3 for x in times)  # timestamps (ms)
+        self.s = tuple(shape)  # inference BCHW shape
+
+    def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
+        s, crops = '', []
+        for i, (im, pred) in enumerate(zip(self.ims, self.pred)):
+            s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '  # string
+            if pred.shape[0]:
+                for c in pred[:, -1].unique():
+                    n = (pred[:, -1] == c).sum()  # detections per class
+                    s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
+                s = s.rstrip(', ')
+                if show or save or render or crop:
+                    annotator = Annotator(im, example=str(self.names))
+                    for *box, conf, cls in reversed(pred):  # xyxy, confidence, class
+                        label = f'{self.names[int(cls)]} {conf:.2f}'
+                        if crop:
+                            file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
+                            crops.append({
+                                'box': box,
+                                'conf': conf,
+                                'cls': cls,
+                                'label': label,
+                                'im': save_one_box(box, im, file=file, save=save)})
+                        else:  # all others
+                            annotator.box_label(box, label if labels else '', color=colors(cls))
+                    im = annotator.im
+            else:
+                s += '(no detections)'
+
+            im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
+            if show:
+                display(im) if is_notebook() else im.show(self.files[i])
+            if save:
+                f = self.files[i]
+                im.save(save_dir / f)  # save
+                if i == self.n - 1:
+                    LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
+            if render:
+                self.ims[i] = np.asarray(im)
+        if pprint:
+            s = s.lstrip('\n')
+            return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t
+        if crop:
+            if save:
+                LOGGER.info(f'Saved results to {save_dir}\n')
+            return crops
+
+    @TryExcept('Showing images is not supported in this environment')
+    def show(self, labels=True):
+        self._run(show=True, labels=labels)  # show results
+
+    def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False):
+        save_dir = increment_path(save_dir, exist_ok, mkdir=True)  # increment save_dir
+        self._run(save=True, labels=labels, save_dir=save_dir)  # save results
+
+    def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False):
+        save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None
+        return self._run(crop=True, save=save, save_dir=save_dir)  # crop results
+
+    def render(self, labels=True):
+        self._run(render=True, labels=labels)  # render results
+        return self.ims
+
+    def pandas(self):
+        # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
+        new = copy(self)  # return copy
+        ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name'  # xyxy columns
+        cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name'  # xywh columns
+        for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
+            a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)]  # update
+            setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
+        return new
+
+    def tolist(self):
+        # return a list of Detections objects, i.e. 'for result in results.tolist():'
+        r = range(self.n)  # iterable
+        x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
+        # for d in x:
+        #    for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
+        #        setattr(d, k, getattr(d, k)[0])  # pop out of list
+        return x
+
+    def print(self):
+        LOGGER.info(self.__str__())
+
+    def __len__(self):  # override len(results)
+        return self.n
+
+    def __str__(self):  # override print(results)
+        return self._run(pprint=True)  # print results
+
+    def __repr__(self):
+        return f'YOLOv5 {self.__class__} instance\n' + self.__str__()
+
+
+class Proto(nn.Module):
+    # YOLOv5 mask Proto module for segmentation models
+    def __init__(self, c1, c_=256, c2=32):  # ch_in, number of protos, number of masks
+        super().__init__()
+        self.cv1 = Conv(c1, c_, k=3)
+        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
+        self.cv2 = Conv(c_, c_, k=3)
+        self.cv3 = Conv(c_, c2)
+
+    def forward(self, x):
+        return self.cv3(self.cv2(self.upsample(self.cv1(x))))
+
+
+class Classify(nn.Module):
+    # YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2)
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, kernel, stride, padding, groups
+        super().__init__()
+        c_ = 1280  # efficientnet_b0 size
+        self.conv = Conv(c1, c_, k, s, autopad(k, p), g)
+        self.pool = nn.AdaptiveAvgPool2d(1)  # to x(b,c_,1,1)
+        self.drop = nn.Dropout(p=0.0, inplace=True)
+        self.linear = nn.Linear(c_, c2)  # to x(b,c2)
+        self.concat =Concat(1)
+    def forward(self, x):
+        if isinstance(x, list):
+            x = self.concat(x)
+        return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))

models/experimental.py

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+"""Experimental modules."""
+import math
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from utils.downloads import attempt_download
+
+
+class Sum(nn.Module):
+    """Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070."""
+
+    def __init__(self, n, weight=False):
+        """Initializes a module to sum outputs of layers with number of inputs `n` and optional weighting, supporting 2+
+        inputs.
+        """
+        super().__init__()
+        self.weight = weight  # apply weights boolean
+        self.iter = range(n - 1)  # iter object
+        if weight:
+            self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True)  # layer weights
+
+    def forward(self, x):
+        """Processes input through a customizable weighted sum of `n` inputs, optionally applying learned weights."""
+        y = x[0]  # no weight
+        if self.weight:
+            w = torch.sigmoid(self.w) * 2
+            for i in self.iter:
+                y = y + x[i + 1] * w[i]
+        else:
+            for i in self.iter:
+                y = y + x[i + 1]
+        return y
+
+
+class MixConv2d(nn.Module):
+    """Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595."""
+
+    def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
+        """Initializes MixConv2d with mixed depth-wise convolutional layers, taking input and output channels (c1, c2),
+        kernel sizes (k), stride (s), and channel distribution strategy (equal_ch).
+        """
+        super().__init__()
+        n = len(k)  # number of convolutions
+        if equal_ch:  # equal c_ per group
+            i = torch.linspace(0, n - 1e-6, c2).floor()  # c2 indices
+            c_ = [(i == g).sum() for g in range(n)]  # intermediate channels
+        else:  # equal weight.numel() per group
+            b = [c2] + [0] * n
+            a = np.eye(n + 1, n, k=-1)
+            a -= np.roll(a, 1, axis=1)
+            a *= np.array(k) ** 2
+            a[0] = 1
+            c_ = np.linalg.lstsq(a, b, rcond=None)[0].round()  # solve for equal weight indices, ax = b
+
+        self.m = nn.ModuleList(
+            [nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)]
+        )
+        self.bn = nn.BatchNorm2d(c2)
+        self.act = nn.SiLU()
+
+    def forward(self, x):
+        """Performs forward pass by applying SiLU activation on batch-normalized concatenated convolutional layer
+        outputs.
+        """
+        return self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
+
+
+class Ensemble(nn.ModuleList):
+    """Ensemble of models."""
+
+    def __init__(self):
+        """Initializes an ensemble of models to be used for aggregated predictions."""
+        super().__init__()
+
+    def forward(self, x, augment=False, profile=False, visualize=False):
+        """Performs forward pass aggregating outputs from an ensemble of models.."""
+        y = [module(x, augment, profile, visualize)[0] for module in self]
+        # y = torch.stack(y).max(0)[0]  # max ensemble
+        # y = torch.stack(y).mean(0)  # mean ensemble
+        y = torch.cat(y, 1)  # nms ensemble
+        return y, None  # inference, train output
+
+
+def attempt_load(weights, device=None, inplace=True, fuse=True):
+    """
+    Loads and fuses an ensemble or single YOLOv5 model from weights, handling device placement and model adjustments.
+
+    Example inputs: weights=[a,b,c] or a single model weights=[a] or weights=a.
+    """
+    from models.yolo import Detect, Model
+
+    model = Ensemble()
+    for w in weights if isinstance(weights, list) else [weights]:
+        ckpt = torch.load(attempt_download(w), map_location="cpu")  # load
+        ckpt = (ckpt.get("ema") or ckpt["model"]).to(device).float()  # FP32 model
+
+        # Model compatibility updates
+        if not hasattr(ckpt, "stride"):
+            ckpt.stride = torch.tensor([32.0])
+        if hasattr(ckpt, "names") and isinstance(ckpt.names, (list, tuple)):
+            ckpt.names = dict(enumerate(ckpt.names))  # convert to dict
+
+        model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, "fuse") else ckpt.eval())  # model in eval mode
+
+    # Module updates
+    for m in model.modules():
+        t = type(m)
+        if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
+            m.inplace = inplace
+            if t is Detect and not isinstance(m.anchor_grid, list):
+                delattr(m, "anchor_grid")
+                setattr(m, "anchor_grid", [torch.zeros(1)] * m.nl)
+        elif t is nn.Upsample and not hasattr(m, "recompute_scale_factor"):
+            m.recompute_scale_factor = None  # torch 1.11.0 compatibility
+
+    # Return model
+    if len(model) == 1:
+        return model[-1]
+
+    # Return detection ensemble
+    print(f"Ensemble created with {weights}\n")
+    for k in "names", "nc", "yaml":
+        setattr(model, k, getattr(model[0], k))
+    model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride  # max stride
+    assert all(model[0].nc == m.nc for m in model), f"Models have different class counts: {[m.nc for m in model]}"
+    return model

models/hub/anchors.yaml

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+# Default anchors for COCO data
+
+# P5 -------------------------------------------------------------------------------------------------------------------
+# P5-640:
+anchors_p5_640:
+  - [10, 13, 16, 30, 33, 23] # P3/8
+  - [30, 61, 62, 45, 59, 119] # P4/16
+  - [116, 90, 156, 198, 373, 326] # P5/32
+
+# P6 -------------------------------------------------------------------------------------------------------------------
+# P6-640:  thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11,  21,19,  17,41,  43,32,  39,70,  86,64,  65,131,  134,130,  120,265,  282,180,  247,354,  512,387
+anchors_p6_640:
+  - [9, 11, 21, 19, 17, 41] # P3/8
+  - [43, 32, 39, 70, 86, 64] # P4/16
+  - [65, 131, 134, 130, 120, 265] # P5/32
+  - [282, 180, 247, 354, 512, 387] # P6/64
+
+# P6-1280:  thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27,  44,40,  38,94,  96,68,  86,152,  180,137,  140,301,  303,264,  238,542,  436,615,  739,380,  925,792
+anchors_p6_1280:
+  - [19, 27, 44, 40, 38, 94] # P3/8
+  - [96, 68, 86, 152, 180, 137] # P4/16
+  - [140, 301, 303, 264, 238, 542] # P5/32
+  - [436, 615, 739, 380, 925, 792] # P6/64
+
+# P6-1920:  thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41,  67,59,  57,141,  144,103,  129,227,  270,205,  209,452,  455,396,  358,812,  653,922,  1109,570,  1387,1187
+anchors_p6_1920:
+  - [28, 41, 67, 59, 57, 141] # P3/8
+  - [144, 103, 129, 227, 270, 205] # P4/16
+  - [209, 452, 455, 396, 358, 812] # P5/32
+  - [653, 922, 1109, 570, 1387, 1187] # P6/64
+
+# P7 -------------------------------------------------------------------------------------------------------------------
+# P7-640:  thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11,  13,30,  29,20,  30,46,  61,38,  39,92,  78,80,  146,66,  79,163,  149,150,  321,143,  157,303,  257,402,  359,290,  524,372
+anchors_p7_640:
+  - [11, 11, 13, 30, 29, 20] # P3/8
+  - [30, 46, 61, 38, 39, 92] # P4/16
+  - [78, 80, 146, 66, 79, 163] # P5/32
+  - [149, 150, 321, 143, 157, 303] # P6/64
+  - [257, 402, 359, 290, 524, 372] # P7/128
+
+# P7-1280:  thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22,  54,36,  32,77,  70,83,  138,71,  75,173,  165,159,  148,334,  375,151,  334,317,  251,626,  499,474,  750,326,  534,814,  1079,818
+anchors_p7_1280:
+  - [19, 22, 54, 36, 32, 77] # P3/8
+  - [70, 83, 138, 71, 75, 173] # P4/16
+  - [165, 159, 148, 334, 375, 151] # P5/32
+  - [334, 317, 251, 626, 499, 474] # P6/64
+  - [750, 326, 534, 814, 1079, 818] # P7/128
+
+# P7-1920:  thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34,  81,55,  47,115,  105,124,  207,107,  113,259,  247,238,  222,500,  563,227,  501,476,  376,939,  749,711,  1126,489,  801,1222,  1618,1227
+anchors_p7_1920:
+  - [29, 34, 81, 55, 47, 115] # P3/8
+  - [105, 124, 207, 107, 113, 259] # P4/16
+  - [247, 238, 222, 500, 563, 227] # P5/32
+  - [501, 476, 376, 939, 749, 711] # P6/64
+  - [1126, 489, 801, 1222, 1618, 1227] # P7/128

models/hub/yolov3-spp.yaml

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors:
+  - [10, 13, 16, 30, 33, 23] # P3/8
+  - [30, 61, 62, 45, 59, 119] # P4/16
+  - [116, 90, 156, 198, 373, 326] # P5/32
+
+# darknet53 backbone
+backbone:
+  # [from, number, module, args]
+  [
+    [-1, 1, Conv, [32, 3, 1]], # 0
+    [-1, 1, Conv, [64, 3, 2]], # 1-P1/2
+    [-1, 1, Bottleneck, [64]],
+    [-1, 1, Conv, [128, 3, 2]], # 3-P2/4
+    [-1, 2, Bottleneck, [128]],
+    [-1, 1, Conv, [256, 3, 2]], # 5-P3/8
+    [-1, 8, Bottleneck, [256]],
+    [-1, 1, Conv, [512, 3, 2]], # 7-P4/16
+    [-1, 8, Bottleneck, [512]],
+    [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
+    [-1, 4, Bottleneck, [1024]], # 10
+  ]
+
+# YOLOv3-SPP head
+head: [
+    [-1, 1, Bottleneck, [1024, False]],
+    [-1, 1, SPP, [512, [5, 9, 13]]],
+    [-1, 1, Conv, [1024, 3, 1]],
+    [-1, 1, Conv, [512, 1, 1]],
+    [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
+
+    [-2, 1, Conv, [256, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 8], 1, Concat, [1]], # cat backbone P4
+    [-1, 1, Bottleneck, [512, False]],
+    [-1, 1, Bottleneck, [512, False]],
+    [-1, 1, Conv, [256, 1, 1]],
+    [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
+
+    [-2, 1, Conv, [128, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 6], 1, Concat, [1]], # cat backbone P3
+    [-1, 1, Bottleneck, [256, False]],
+    [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
+
+    [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+  ]

models/hub/yolov3-tiny.yaml

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors:
+  - [10, 14, 23, 27, 37, 58] # P4/16
+  - [81, 82, 135, 169, 344, 319] # P5/32
+
+# YOLOv3-tiny backbone
+backbone:
+  # [from, number, module, args]
+  [
+    [-1, 1, Conv, [16, 3, 1]], # 0
+    [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2
+    [-1, 1, Conv, [32, 3, 1]],
+    [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4
+    [-1, 1, Conv, [64, 3, 1]],
+    [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8
+    [-1, 1, Conv, [128, 3, 1]],
+    [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16
+    [-1, 1, Conv, [256, 3, 1]],
+    [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32
+    [-1, 1, Conv, [512, 3, 1]],
+    [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11
+    [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12
+  ]
+
+# YOLOv3-tiny head
+head: [
+    [-1, 1, Conv, [1024, 3, 1]],
+    [-1, 1, Conv, [256, 1, 1]],
+    [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large)
+
+    [-2, 1, Conv, [128, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 8], 1, Concat, [1]], # cat backbone P4
+    [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium)
+
+    [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5)
+  ]

models/hub/yolov3.yaml

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors:
+  - [10, 13, 16, 30, 33, 23] # P3/8
+  - [30, 61, 62, 45, 59, 119] # P4/16
+  - [116, 90, 156, 198, 373, 326] # P5/32
+
+# darknet53 backbone
+backbone:
+  # [from, number, module, args]
+  [
+    [-1, 1, Conv, [32, 3, 1]], # 0
+    [-1, 1, Conv, [64, 3, 2]], # 1-P1/2
+    [-1, 1, Bottleneck, [64]],
+    [-1, 1, Conv, [128, 3, 2]], # 3-P2/4
+    [-1, 2, Bottleneck, [128]],
+    [-1, 1, Conv, [256, 3, 2]], # 5-P3/8
+    [-1, 8, Bottleneck, [256]],
+    [-1, 1, Conv, [512, 3, 2]], # 7-P4/16
+    [-1, 8, Bottleneck, [512]],
+    [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
+    [-1, 4, Bottleneck, [1024]], # 10
+  ]
+
+# YOLOv3 head
+head: [
+    [-1, 1, Bottleneck, [1024, False]],
+    [-1, 1, Conv, [512, 1, 1]],
+    [-1, 1, Conv, [1024, 3, 1]],
+    [-1, 1, Conv, [512, 1, 1]],
+    [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
+
+    [-2, 1, Conv, [256, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 8], 1, Concat, [1]], # cat backbone P4
+    [-1, 1, Bottleneck, [512, False]],
+    [-1, 1, Bottleneck, [512, False]],
+    [-1, 1, Conv, [256, 1, 1]],
+    [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
+
+    [-2, 1, Conv, [128, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 6], 1, Concat, [1]], # cat backbone P3
+    [-1, 1, Bottleneck, [256, False]],
+    [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
+
+    [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+  ]

models/hub/yolov5-bifpn.yaml

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors:
+  - [10, 13, 16, 30, 33, 23] # P3/8
+  - [30, 61, 62, 45, 59, 119] # P4/16
+  - [116, 90, 156, 198, 373, 326] # P5/32
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
+    [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+    [-1, 3, C3, [128]],
+    [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+    [-1, 6, C3, [256]],
+    [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+    [-1, 9, C3, [512]],
+    [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+    [-1, 3, C3, [1024]],
+    [-1, 1, SPPF, [1024, 5]], # 9
+  ]
+
+# YOLOv5 v6.0 BiFPN head
+head: [
+    [-1, 1, Conv, [512, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 6], 1, Concat, [1]], # cat backbone P4
+    [-1, 3, C3, [512, False]], # 13
+
+    [-1, 1, Conv, [256, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 4], 1, Concat, [1]], # cat backbone P3
+    [-1, 3, C3, [256, False]], # 17 (P3/8-small)
+
+    [-1, 1, Conv, [256, 3, 2]],
+    [[-1, 14, 6], 1, Concat, [1]], # cat P4 <--- BiFPN change
+    [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
+
+    [-1, 1, Conv, [512, 3, 2]],
+    [[-1, 10], 1, Concat, [1]], # cat head P5
+    [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
+
+    [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+  ]

models/hub/yolov5-fpn.yaml

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors:
+  - [10, 13, 16, 30, 33, 23] # P3/8
+  - [30, 61, 62, 45, 59, 119] # P4/16
+  - [116, 90, 156, 198, 373, 326] # P5/32
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
+    [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+    [-1, 3, C3, [128]],
+    [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+    [-1, 6, C3, [256]],
+    [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+    [-1, 9, C3, [512]],
+    [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+    [-1, 3, C3, [1024]],
+    [-1, 1, SPPF, [1024, 5]], # 9
+  ]
+
+# YOLOv5 v6.0 FPN head
+head: [
+    [-1, 3, C3, [1024, False]], # 10 (P5/32-large)
+
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 6], 1, Concat, [1]], # cat backbone P4
+    [-1, 1, Conv, [512, 1, 1]],
+    [-1, 3, C3, [512, False]], # 14 (P4/16-medium)
+
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 4], 1, Concat, [1]], # cat backbone P3
+    [-1, 1, Conv, [256, 1, 1]],
+    [-1, 3, C3, [256, False]], # 18 (P3/8-small)
+
+    [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+  ]

models/hub/yolov5-p2.yaml

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors: 3 # AutoAnchor evolves 3 anchors per P output layer
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
+    [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+    [-1, 3, C3, [128]],
+    [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+    [-1, 6, C3, [256]],
+    [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+    [-1, 9, C3, [512]],
+    [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+    [-1, 3, C3, [1024]],
+    [-1, 1, SPPF, [1024, 5]], # 9
+  ]
+
+# YOLOv5 v6.0 head with (P2, P3, P4, P5) outputs
+head: [
+    [-1, 1, Conv, [512, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 6], 1, Concat, [1]], # cat backbone P4
+    [-1, 3, C3, [512, False]], # 13
+
+    [-1, 1, Conv, [256, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 4], 1, Concat, [1]], # cat backbone P3
+    [-1, 3, C3, [256, False]], # 17 (P3/8-small)
+
+    [-1, 1, Conv, [128, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 2], 1, Concat, [1]], # cat backbone P2
+    [-1, 1, C3, [128, False]], # 21 (P2/4-xsmall)
+
+    [-1, 1, Conv, [128, 3, 2]],
+    [[-1, 18], 1, Concat, [1]], # cat head P3
+    [-1, 3, C3, [256, False]], # 24 (P3/8-small)
+
+    [-1, 1, Conv, [256, 3, 2]],
+    [[-1, 14], 1, Concat, [1]], # cat head P4
+    [-1, 3, C3, [512, False]], # 27 (P4/16-medium)
+
+    [-1, 1, Conv, [512, 3, 2]],
+    [[-1, 10], 1, Concat, [1]], # cat head P5
+    [-1, 3, C3, [1024, False]], # 30 (P5/32-large)
+
+    [[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(P2, P3, P4, P5)
+  ]

models/hub/yolov5-p34.yaml

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 0.33 # model depth multiple
+width_multiple: 0.50 # layer channel multiple
+anchors: 3 # AutoAnchor evolves 3 anchors per P output layer
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
+    [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+    [-1, 3, C3, [128]],
+    [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+    [-1, 6, C3, [256]],
+    [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+    [-1, 9, C3, [512]],
+    [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+    [-1, 3, C3, [1024]],
+    [-1, 1, SPPF, [1024, 5]], # 9
+  ]
+
+# YOLOv5 v6.0 head with (P3, P4) outputs
+head: [
+    [-1, 1, Conv, [512, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 6], 1, Concat, [1]], # cat backbone P4
+    [-1, 3, C3, [512, False]], # 13
+
+    [-1, 1, Conv, [256, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 4], 1, Concat, [1]], # cat backbone P3
+    [-1, 3, C3, [256, False]], # 17 (P3/8-small)
+
+    [-1, 1, Conv, [256, 3, 2]],
+    [[-1, 14], 1, Concat, [1]], # cat head P4
+    [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
+
+    [[17, 20], 1, Detect, [nc, anchors]], # Detect(P3, P4)
+  ]

models/hub/yolov5-p6.yaml

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors: 3 # AutoAnchor evolves 3 anchors per P output layer
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
+    [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+    [-1, 3, C3, [128]],
+    [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+    [-1, 6, C3, [256]],
+    [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+    [-1, 9, C3, [512]],
+    [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
+    [-1, 3, C3, [768]],
+    [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
+    [-1, 3, C3, [1024]],
+    [-1, 1, SPPF, [1024, 5]], # 11
+  ]
+
+# YOLOv5 v6.0 head with (P3, P4, P5, P6) outputs
+head: [
+    [-1, 1, Conv, [768, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 8], 1, Concat, [1]], # cat backbone P5
+    [-1, 3, C3, [768, False]], # 15
+
+    [-1, 1, Conv, [512, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 6], 1, Concat, [1]], # cat backbone P4
+    [-1, 3, C3, [512, False]], # 19
+
+    [-1, 1, Conv, [256, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 4], 1, Concat, [1]], # cat backbone P3
+    [-1, 3, C3, [256, False]], # 23 (P3/8-small)
+
+    [-1, 1, Conv, [256, 3, 2]],
+    [[-1, 20], 1, Concat, [1]], # cat head P4
+    [-1, 3, C3, [512, False]], # 26 (P4/16-medium)
+
+    [-1, 1, Conv, [512, 3, 2]],
+    [[-1, 16], 1, Concat, [1]], # cat head P5
+    [-1, 3, C3, [768, False]], # 29 (P5/32-large)
+
+    [-1, 1, Conv, [768, 3, 2]],
+    [[-1, 12], 1, Concat, [1]], # cat head P6
+    [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
+
+    [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
+  ]

models/hub/yolov5-p7.yaml

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors: 3 # AutoAnchor evolves 3 anchors per P output layer
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
+    [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+    [-1, 3, C3, [128]],
+    [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+    [-1, 6, C3, [256]],
+    [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+    [-1, 9, C3, [512]],
+    [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
+    [-1, 3, C3, [768]],
+    [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
+    [-1, 3, C3, [1024]],
+    [-1, 1, Conv, [1280, 3, 2]], # 11-P7/128
+    [-1, 3, C3, [1280]],
+    [-1, 1, SPPF, [1280, 5]], # 13
+  ]
+
+# YOLOv5 v6.0 head with (P3, P4, P5, P6, P7) outputs
+head: [
+    [-1, 1, Conv, [1024, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 10], 1, Concat, [1]], # cat backbone P6
+    [-1, 3, C3, [1024, False]], # 17
+
+    [-1, 1, Conv, [768, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 8], 1, Concat, [1]], # cat backbone P5
+    [-1, 3, C3, [768, False]], # 21
+
+    [-1, 1, Conv, [512, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 6], 1, Concat, [1]], # cat backbone P4
+    [-1, 3, C3, [512, False]], # 25
+
+    [-1, 1, Conv, [256, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 4], 1, Concat, [1]], # cat backbone P3
+    [-1, 3, C3, [256, False]], # 29 (P3/8-small)
+
+    [-1, 1, Conv, [256, 3, 2]],
+    [[-1, 26], 1, Concat, [1]], # cat head P4
+    [-1, 3, C3, [512, False]], # 32 (P4/16-medium)
+
+    [-1, 1, Conv, [512, 3, 2]],
+    [[-1, 22], 1, Concat, [1]], # cat head P5
+    [-1, 3, C3, [768, False]], # 35 (P5/32-large)
+
+    [-1, 1, Conv, [768, 3, 2]],
+    [[-1, 18], 1, Concat, [1]], # cat head P6
+    [-1, 3, C3, [1024, False]], # 38 (P6/64-xlarge)
+
+    [-1, 1, Conv, [1024, 3, 2]],
+    [[-1, 14], 1, Concat, [1]], # cat head P7
+    [-1, 3, C3, [1280, False]], # 41 (P7/128-xxlarge)
+
+    [[29, 32, 35, 38, 41], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6, P7)
+  ]

models/hub/yolov5-panet.yaml

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors:
+  - [10, 13, 16, 30, 33, 23] # P3/8
+  - [30, 61, 62, 45, 59, 119] # P4/16
+  - [116, 90, 156, 198, 373, 326] # P5/32
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
+    [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+    [-1, 3, C3, [128]],
+    [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+    [-1, 6, C3, [256]],
+    [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+    [-1, 9, C3, [512]],
+    [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+    [-1, 3, C3, [1024]],
+    [-1, 1, SPPF, [1024, 5]], # 9
+  ]
+
+# YOLOv5 v6.0 PANet head
+head: [
+    [-1, 1, Conv, [512, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 6], 1, Concat, [1]], # cat backbone P4
+    [-1, 3, C3, [512, False]], # 13
+
+    [-1, 1, Conv, [256, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 4], 1, Concat, [1]], # cat backbone P3
+    [-1, 3, C3, [256, False]], # 17 (P3/8-small)
+
+    [-1, 1, Conv, [256, 3, 2]],
+    [[-1, 14], 1, Concat, [1]], # cat head P4
+    [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
+
+    [-1, 1, Conv, [512, 3, 2]],
+    [[-1, 10], 1, Concat, [1]], # cat head P5
+    [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
+
+    [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+  ]

models/hub/yolov5l6.yaml

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors:
+  - [19, 27, 44, 40, 38, 94] # P3/8
+  - [96, 68, 86, 152, 180, 137] # P4/16
+  - [140, 301, 303, 264, 238, 542] # P5/32
+  - [436, 615, 739, 380, 925, 792] # P6/64
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
+    [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+    [-1, 3, C3, [128]],
+    [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+    [-1, 6, C3, [256]],
+    [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+    [-1, 9, C3, [512]],
+    [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
+    [-1, 3, C3, [768]],
+    [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
+    [-1, 3, C3, [1024]],
+    [-1, 1, SPPF, [1024, 5]], # 11
+  ]
+
+# YOLOv5 v6.0 head
+head: [
+    [-1, 1, Conv, [768, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 8], 1, Concat, [1]], # cat backbone P5
+    [-1, 3, C3, [768, False]], # 15
+
+    [-1, 1, Conv, [512, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 6], 1, Concat, [1]], # cat backbone P4
+    [-1, 3, C3, [512, False]], # 19
+
+    [-1, 1, Conv, [256, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 4], 1, Concat, [1]], # cat backbone P3
+    [-1, 3, C3, [256, False]], # 23 (P3/8-small)
+
+    [-1, 1, Conv, [256, 3, 2]],
+    [[-1, 20], 1, Concat, [1]], # cat head P4
+    [-1, 3, C3, [512, False]], # 26 (P4/16-medium)
+
+    [-1, 1, Conv, [512, 3, 2]],
+    [[-1, 16], 1, Concat, [1]], # cat head P5
+    [-1, 3, C3, [768, False]], # 29 (P5/32-large)
+
+    [-1, 1, Conv, [768, 3, 2]],
+    [[-1, 12], 1, Concat, [1]], # cat head P6
+    [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
+
+    [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
+  ]

models/hub/yolov5m6.yaml

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 0.67 # model depth multiple
+width_multiple: 0.75 # layer channel multiple
+anchors:
+  - [19, 27, 44, 40, 38, 94] # P3/8
+  - [96, 68, 86, 152, 180, 137] # P4/16
+  - [140, 301, 303, 264, 238, 542] # P5/32
+  - [436, 615, 739, 380, 925, 792] # P6/64
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
+    [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+    [-1, 3, C3, [128]],
+    [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+    [-1, 6, C3, [256]],
+    [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+    [-1, 9, C3, [512]],
+    [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
+    [-1, 3, C3, [768]],
+    [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
+    [-1, 3, C3, [1024]],
+    [-1, 1, SPPF, [1024, 5]], # 11
+  ]
+
+# YOLOv5 v6.0 head
+head: [
+    [-1, 1, Conv, [768, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 8], 1, Concat, [1]], # cat backbone P5
+    [-1, 3, C3, [768, False]], # 15
+
+    [-1, 1, Conv, [512, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 6], 1, Concat, [1]], # cat backbone P4
+    [-1, 3, C3, [512, False]], # 19
+
+    [-1, 1, Conv, [256, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 4], 1, Concat, [1]], # cat backbone P3
+    [-1, 3, C3, [256, False]], # 23 (P3/8-small)
+
+    [-1, 1, Conv, [256, 3, 2]],
+    [[-1, 20], 1, Concat, [1]], # cat head P4
+    [-1, 3, C3, [512, False]], # 26 (P4/16-medium)
+
+    [-1, 1, Conv, [512, 3, 2]],
+    [[-1, 16], 1, Concat, [1]], # cat head P5
+    [-1, 3, C3, [768, False]], # 29 (P5/32-large)
+
+    [-1, 1, Conv, [768, 3, 2]],
+    [[-1, 12], 1, Concat, [1]], # cat head P6
+    [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
+
+    [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
+  ]

models/hub/yolov5n6.yaml

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 0.33 # model depth multiple
+width_multiple: 0.25 # layer channel multiple
+anchors:
+  - [19, 27, 44, 40, 38, 94] # P3/8
+  - [96, 68, 86, 152, 180, 137] # P4/16
+  - [140, 301, 303, 264, 238, 542] # P5/32
+  - [436, 615, 739, 380, 925, 792] # P6/64
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
+    [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+    [-1, 3, C3, [128]],
+    [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+    [-1, 6, C3, [256]],
+    [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+    [-1, 9, C3, [512]],
+    [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
+    [-1, 3, C3, [768]],
+    [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
+    [-1, 3, C3, [1024]],
+    [-1, 1, SPPF, [1024, 5]], # 11
+  ]
+
+# YOLOv5 v6.0 head
+head: [
+    [-1, 1, Conv, [768, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 8], 1, Concat, [1]], # cat backbone P5
+    [-1, 3, C3, [768, False]], # 15
+
+    [-1, 1, Conv, [512, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 6], 1, Concat, [1]], # cat backbone P4
+    [-1, 3, C3, [512, False]], # 19
+
+    [-1, 1, Conv, [256, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 4], 1, Concat, [1]], # cat backbone P3
+    [-1, 3, C3, [256, False]], # 23 (P3/8-small)
+
+    [-1, 1, Conv, [256, 3, 2]],
+    [[-1, 20], 1, Concat, [1]], # cat head P4
+    [-1, 3, C3, [512, False]], # 26 (P4/16-medium)
+
+    [-1, 1, Conv, [512, 3, 2]],
+    [[-1, 16], 1, Concat, [1]], # cat head P5
+    [-1, 3, C3, [768, False]], # 29 (P5/32-large)
+
+    [-1, 1, Conv, [768, 3, 2]],
+    [[-1, 12], 1, Concat, [1]], # cat head P6
+    [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
+
+    [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
+  ]

models/hub/yolov5s-LeakyReLU.yaml

+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+
+# Parameters
+nc: 80 # number of classes
+activation: nn.LeakyReLU(0.1) # <----- Conv() activation used throughout entire YOLOv5 model
+depth_multiple: 0.33 # model depth multiple
+width_multiple: 0.50 # layer channel multiple
+anchors:
+  - [10, 13, 16, 30, 33, 23] # P3/8
+  - [30, 61, 62, 45, 59, 119] # P4/16
+  - [116, 90, 156, 198, 373, 326] # P5/32
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
+    [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+    [-1, 3, C3, [128]],
+    [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+    [-1, 6, C3, [256]],
+    [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+    [-1, 9, C3, [512]],
+    [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+    [-1, 3, C3, [1024]],
+    [-1, 1, SPPF, [1024, 5]], # 9
+  ]
+
+# YOLOv5 v6.0 head
+head: [
+    [-1, 1, Conv, [512, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 6], 1, Concat, [1]], # cat backbone P4
+    [-1, 3, C3, [512, False]], # 13
+
+    [-1, 1, Conv, [256, 1, 1]],
+    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
+    [[-1, 4], 1, Concat, [1]], # cat backbone P3
+    [-1, 3, C3, [256, False]], # 17 (P3/8-small)
+
+    [-1, 1, Conv, [256, 3, 2]],
+    [[-1, 14], 1, Concat, [1]], # cat head P4
+    [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
+
+    [-1, 1, Conv, [512, 3, 2]],
+    [[-1, 10], 1, Concat, [1]], # cat head P5
+    [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
+
+    [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+  ]