Dev

.gitignore

@@ -1,3 +1,4 @@
+resource
 # Repo-specific GitIgnore ----------------------------------------------------------------------------------------------
 *.jpg
 *.jpeg

README.md

@@ -70,9 +70,7 @@ See the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5) for full documentatio
 <details open>
 <summary>Install</summary>
 
-Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a
-[**Python>=3.8.0**](https://www.python.org/) environment, including
-[**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
+Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.8.0**](https://www.python.org/) environment, including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
 
 ```bash
 git clone https://github.com/ultralytics/yolov5  # clone
@@ -128,9 +126,7 @@ python detect.py --weights yolov5s.pt --source 0                               #
 <details>
 <summary>Training</summary>
 
-The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
-results. [Models](https://github.com/ultralytics/yolov5/tree/master/models)
-and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) times faster). Use the largest `--batch-size` possible, or pass `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
+The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) results. [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) times faster). Use the largest `--batch-size` possible, or pass `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
 
 ```bash
 python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml  --batch-size 128

USAGE.md

@@ -0,0 +1,11 @@
+
+
+```bash
+conda create -n yolov5 python=3.10 -y
+```
+
+```bash
+python3 -m pip install --upgrade pip
+pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu121
+pip3 install -r requirements.txt
+```

benchmarks.py

@@ -1,6 +1,6 @@
 # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
-Run YOLOv5 benchmarks on all supported export formats
+Run YOLOv5 benchmarks on all supported export formats.
 
 Format                      | `export.py --include`         | Model
 ---                         | ---                           | ---
@@ -50,115 +50,115 @@
 
 
 def run(
-        weights=ROOT / 'yolov5s.pt',  # weights path
-        imgsz=640,  # inference size (pixels)
-        batch_size=1,  # batch size
-        data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
-        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-        half=False,  # use FP16 half-precision inference
-        test=False,  # test exports only
-        pt_only=False,  # test PyTorch only
-        hard_fail=False,  # throw error on benchmark failure
+    weights=ROOT / "yolov5s.pt",  # weights path
+    imgsz=640,  # inference size (pixels)
+    batch_size=1,  # batch size
+    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
+    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+    half=False,  # use FP16 half-precision inference
+    test=False,  # test exports only
+    pt_only=False,  # test PyTorch only
+    hard_fail=False,  # throw error on benchmark failure
 ):
     y, t = [], time.time()
     device = select_device(device)
     model_type = type(attempt_load(weights, fuse=False))  # DetectionModel, SegmentationModel, etc.
     for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows():  # index, (name, file, suffix, CPU, GPU)
         try:
-            assert i not in (9, 10), 'inference not supported'  # Edge TPU and TF.js are unsupported
-            assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13'  # CoreML
-            if 'cpu' in device.type:
-                assert cpu, 'inference not supported on CPU'
-            if 'cuda' in device.type:
-                assert gpu, 'inference not supported on GPU'
+            assert i not in (9, 10), "inference not supported"  # Edge TPU and TF.js are unsupported
+            assert i != 5 or platform.system() == "Darwin", "inference only supported on macOS>=10.13"  # CoreML
+            if "cpu" in device.type:
+                assert cpu, "inference not supported on CPU"
+            if "cuda" in device.type:
+                assert gpu, "inference not supported on GPU"
 
             # Export
-            if f == '-':
+            if f == "-":
                 w = weights  # PyTorch format
             else:
-                w = export.run(weights=weights,
-                               imgsz=[imgsz],
-                               include=[f],
-                               batch_size=batch_size,
-                               device=device,
-                               half=half)[-1]  # all others
-            assert suffix in str(w), 'export failed'
+                w = export.run(
+                    weights=weights, imgsz=[imgsz], include=[f], batch_size=batch_size, device=device, half=half
+                )[-1]  # all others
+            assert suffix in str(w), "export failed"
 
             # Validate
             if model_type == SegmentationModel:
-                result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half)
+                result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task="speed", half=half)
                 metric = result[0][7]  # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls))
             else:  # DetectionModel:
-                result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half)
+                result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task="speed", half=half)
                 metric = result[0][3]  # (p, r, map50, map, *loss(box, obj, cls))
             speed = result[2][1]  # times (preprocess, inference, postprocess)
             y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)])  # MB, mAP, t_inference
         except Exception as e:
             if hard_fail:
-                assert type(e) is AssertionError, f'Benchmark --hard-fail for {name}: {e}'
-            LOGGER.warning(f'WARNING ⚠️ Benchmark failure for {name}: {e}')
+                assert type(e) is AssertionError, f"Benchmark --hard-fail for {name}: {e}"
+            LOGGER.warning(f"WARNING ⚠️ Benchmark failure for {name}: {e}")
             y.append([name, None, None, None])  # mAP, t_inference
         if pt_only and i == 0:
             break  # break after PyTorch
 
     # Print results
-    LOGGER.info('\n')
+    LOGGER.info("\n")
     parse_opt()
     notebook_init()  # print system info
-    c = ['Format', 'Size (MB)', 'mAP50-95', 'Inference time (ms)'] if map else ['Format', 'Export', '', '']
+    c = ["Format", "Size (MB)", "mAP50-95", "Inference time (ms)"] if map else ["Format", "Export", "", ""]
     py = pd.DataFrame(y, columns=c)
-    LOGGER.info(f'\nBenchmarks complete ({time.time() - t:.2f}s)')
+    LOGGER.info(f"\nBenchmarks complete ({time.time() - t:.2f}s)")
     LOGGER.info(str(py if map else py.iloc[:, :2]))
     if hard_fail and isinstance(hard_fail, str):
-        metrics = py['mAP50-95'].array  # values to compare to floor
+        metrics = py["mAP50-95"].array  # values to compare to floor
         floor = eval(hard_fail)  # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n
-        assert all(x > floor for x in metrics if pd.notna(x)), f'HARD FAIL: mAP50-95 < floor {floor}'
+        assert all(x > floor for x in metrics if pd.notna(x)), f"HARD FAIL: mAP50-95 < floor {floor}"
     return py
 
 
 def test(
-        weights=ROOT / 'yolov5s.pt',  # weights path
-        imgsz=640,  # inference size (pixels)
-        batch_size=1,  # batch size
-        data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
-        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-        half=False,  # use FP16 half-precision inference
-        test=False,  # test exports only
-        pt_only=False,  # test PyTorch only
-        hard_fail=False,  # throw error on benchmark failure
+    weights=ROOT / "yolov5s.pt",  # weights path
+    imgsz=640,  # inference size (pixels)
+    batch_size=1,  # batch size
+    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
+    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+    half=False,  # use FP16 half-precision inference
+    test=False,  # test exports only
+    pt_only=False,  # test PyTorch only
+    hard_fail=False,  # throw error on benchmark failure
 ):
     y, t = [], time.time()
     device = select_device(device)
     for i, (name, f, suffix, gpu) in export.export_formats().iterrows():  # index, (name, file, suffix, gpu-capable)
         try:
-            w = weights if f == '-' else \
-                export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1]  # weights
-            assert suffix in str(w), 'export failed'
+            w = (
+                weights
+                if f == "-"
+                else export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1]
+            )  # weights
+            assert suffix in str(w), "export failed"
             y.append([name, True])
         except Exception:
             y.append([name, False])  # mAP, t_inference
 
     # Print results
-    LOGGER.info('\n')
+    LOGGER.info("\n")
     parse_opt()
     notebook_init()  # print system info
-    py = pd.DataFrame(y, columns=['Format', 'Export'])
-    LOGGER.info(f'\nExports complete ({time.time() - t:.2f}s)')
+    py = pd.DataFrame(y, columns=["Format", "Export"])
+    LOGGER.info(f"\nExports complete ({time.time() - t:.2f}s)")
     LOGGER.info(str(py))
     return py
 
 
 def parse_opt():
     parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path')
-    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
-    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
-    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
-    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
-    parser.add_argument('--test', action='store_true', help='test exports only')
-    parser.add_argument('--pt-only', action='store_true', help='test PyTorch only')
-    parser.add_argument('--hard-fail', nargs='?', const=True, default=False, help='Exception on error or < min metric')
+    parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="weights path")
+    parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)")
+    parser.add_argument("--batch-size", type=int, default=1, help="batch size")
+    parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path")
+    parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
+    parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
+    parser.add_argument("--test", action="store_true", help="test exports only")
+    parser.add_argument("--pt-only", action="store_true", help="test PyTorch only")
+    parser.add_argument("--hard-fail", nargs="?", const=True, default=False, help="Exception on error or < min metric")
     opt = parser.parse_args()
     opt.data = check_yaml(opt.data)  # check YAML
     print_args(vars(opt))
@@ -169,6 +169,6 @@ def main(opt):
     test(**vars(opt)) if opt.test else run(**vars(opt))
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     opt = parse_opt()
     main(opt)