a change

.pre-commit-config.yaml

@@ -1,11 +1,11 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 # Pre-commit hooks. For more information see https://github.com/pre-commit/pre-commit-hooks/blob/main/README.md
 
-exclude: 'docs/'
+exclude: "docs/"
 # Define bot property if installed via https://github.com/marketplace/pre-commit-ci
 ci:
   autofix_prs: true
-  autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions'
+  autoupdate_commit_msg: "[pre-commit.ci] pre-commit suggestions"
   autoupdate_schedule: monthly
   # submodules: true
 
@@ -61,7 +61,6 @@ repos:
       - id: codespell
         args:
           - --ignore-words-list=crate,nd,strack,dota
-
 #  - repo: https://github.com/asottile/yesqa
 #    rev: v1.4.0
 #    hooks:

CONTRIBUTING.md

@@ -55,7 +55,7 @@ To allow your work to be integrated as seamlessly as possible, we advise you to:
 <p align="center"><img width="751" alt="Screenshot 2022-08-29 at 22 47 03" src="https://user-images.githubusercontent.com/26833433/187296922-545c5498-f64a-4d8c-8300-5fa764360da6.png"></p>
 
 - ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase
-  but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_  — Bruce Lee
+  but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee
 
 ## Submitting a Bug Report 🐛
 

README.md

@@ -188,7 +188,7 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml  -
     <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" /></a>
 </div>
 
-|                                                           Roboflow                                                           |                                                            ClearML ⭐ NEW                                                            |                                                                        Comet ⭐ NEW                                                                         |                                           Neural Magic ⭐ NEW                                           |
+|                                                           Roboflow                                                           |                                                           ClearML ⭐ NEW                                                            |                                                                        Comet ⭐ NEW                                                                        |                                          Neural Magic ⭐ NEW                                           |
 | :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
 | Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet2) lets you save YOLOv5 models, resume training, and interactively visualise and debug predictions | Run YOLOv5 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
 
@@ -398,9 +398,7 @@ python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
 ```
 
 ```python
-model = torch.hub.load(
-    "ultralytics/yolov5", "custom", "yolov5s-cls.pt"
-)  # load from PyTorch Hub
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt")  # load from PyTorch Hub
 ```
 
 ### Export

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)