diff --git a/README.md b/README.md index d409b3fdeadf..02908db0fd18 100755 --- a/README.md +++ b/README.md @@ -6,36 +6,43 @@ This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk. -

+

+
+ YOLOv5-P5 640 Figure (click to expand) + +

+
Figure Notes (click to expand) * GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. * EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8. + * **Reproduce** by `python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
+- **April 11, 2021**: [v5.0 release](https://github.com/ultralytics/yolov5/releases/tag/v5.0): YOLOv5-P6 1280 models, [AWS](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart), [Supervise.ly](https://github.com/ultralytics/yolov5/issues/2518) and [YouTube](https://github.com/ultralytics/yolov5/pull/2752) integrations. - **January 5, 2021**: [v4.0 release](https://github.com/ultralytics/yolov5/releases/tag/v4.0): nn.SiLU() activations, [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme) logging, [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/) integration. - **August 13, 2020**: [v3.0 release](https://github.com/ultralytics/yolov5/releases/tag/v3.0): nn.Hardswish() activations, data autodownload, native AMP. - **July 23, 2020**: [v2.0 release](https://github.com/ultralytics/yolov5/releases/tag/v2.0): improved model definition, training and mAP. -- **June 22, 2020**: [PANet](https://arxiv.org/abs/1803.01534) updates: new heads, reduced parameters, improved speed and mAP [364fcfd](https://github.com/ultralytics/yolov5/commit/364fcfd7dba53f46edd4f04c037a039c0a287972). -- **June 19, 2020**: [FP16](https://pytorch.org/docs/stable/nn.html#torch.nn.Module.half) as new default for smaller checkpoints and faster inference [d4c6674](https://github.com/ultralytics/yolov5/commit/d4c6674c98e19df4c40e33a777610a18d1961145). ## Pretrained Checkpoints -| Model | size | APval | APtest | AP50 | SpeedV100 | FPSV100 || params | GFLOPS | -|---------- |------ |------ |------ |------ | -------- | ------| ------ |------ | :------: | -| [YOLOv5s](https://github.com/ultralytics/yolov5/releases) |640 |36.8 |36.8 |55.6 |**2.2ms** |**455** ||7.3M |17.0 -| [YOLOv5m](https://github.com/ultralytics/yolov5/releases) |640 |44.5 |44.5 |63.1 |2.9ms |345 ||21.4M |51.3 -| [YOLOv5l](https://github.com/ultralytics/yolov5/releases) |640 |48.1 |48.1 |66.4 |3.8ms |264 ||47.0M |115.4 -| [YOLOv5x](https://github.com/ultralytics/yolov5/releases) |640 |**50.1** |**50.1** |**68.7** |6.0ms |167 ||87.7M |218.8 -| | | | | | | || | -| [YOLOv5x](https://github.com/ultralytics/yolov5/releases) + TTA |832 |**51.9** |**51.9** |**69.6** |24.9ms |40 ||87.7M |1005.3 - - +[assets]: https://github.com/ultralytics/yolov5/releases + +Model |size
(pixels) |mAPval
0.5:0.95 |mAPtest
0.5:0.95 |mAPval
0.5 |Speed
V100 (ms) | |params
(M) |FLOPS
640 (B) +--- |--- |--- |--- |--- |--- |---|--- |--- +[YOLOv5s][assets] |640 |36.7 |36.7 |55.4 |**2.0** | |7.3 |17.0 +[YOLOv5m][assets] |640 |44.5 |44.5 |63.3 |2.7 | |21.4 |51.3 +[YOLOv5l][assets] |640 |48.2 |48.2 |66.9 |3.8 | |47.0 |115.4 +[YOLOv5x][assets] |640 |**50.4** |**50.4** |**68.8** |6.1 | |87.7 |218.8 +| | | | | | || | +[YOLOv5s6][assets] |1280 |43.3 |43.3 |61.9 |**4.3** | |12.7 |17.4 +[YOLOv5m6][assets] |1280 |50.5 |50.5 |68.7 |8.4 | |35.9 |52.4 +[YOLOv5l6][assets] |1280 |53.4 |53.4 |71.1 |12.3 | |77.2 |117.7 +[YOLOv5x6][assets] |1280 |**54.4** |**54.4** |**72.0** |22.4 | |141.8 |222.9 +| | | | | | || | +[YOLOv5x6][assets] TTA |1280 |**55.0** |**55.0** |**72.0** |70.8 | |- |-
Table Notes (click to expand) @@ -44,7 +51,7 @@ This repository represents Ultralytics open-source research into future object d * AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` * SpeedGPU averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45` * All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). - * Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale augmentation. **Reproduce TTA** by `python test.py --data coco.yaml --img 832 --iou 0.65 --augment` + * Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale augmentation. **Reproduce TTA** by `python test.py --data coco.yaml --img 1536 --iou 0.7 --augment`
@@ -85,7 +92,7 @@ YOLOv5 may be run in any of the following up-to-date verified environments (with ## Inference -detect.py runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. +`detect.py` runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. ```bash $ python detect.py --source 0 # webcam file.jpg # image diff --git a/hubconf.py b/hubconf.py index 0f9aa150a34e..d26db45695de 100644 --- a/hubconf.py +++ b/hubconf.py @@ -55,84 +55,68 @@ def create(name, pretrained, channels, classes, autoshape): raise Exception(s) from e -def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True): - """YOLOv5-small model from https://github.com/ultralytics/yolov5 +def custom(path_or_model='path/to/model.pt', autoshape=True): + """YOLOv5-custom model https://github.com/ultralytics/yolov5 - Arguments: - pretrained (bool): load pretrained weights into the model, default=False - channels (int): number of input channels, default=3 - classes (int): number of model classes, default=80 + Arguments (3 options): + path_or_model (str): 'path/to/model.pt' + path_or_model (dict): torch.load('path/to/model.pt') + path_or_model (nn.Module): torch.load('path/to/model.pt')['model'] Returns: pytorch model """ - return create('yolov5s', pretrained, channels, classes, autoshape) + model = torch.load(path_or_model) if isinstance(path_or_model, str) else path_or_model # load checkpoint + if isinstance(model, dict): + model = model['ema' if model.get('ema') else 'model'] # load model + hub_model = Model(model.yaml).to(next(model.parameters()).device) # create + hub_model.load_state_dict(model.float().state_dict()) # load state_dict + hub_model.names = model.names # class names + if autoshape: + hub_model = hub_model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS + device = select_device('0' if torch.cuda.is_available() else 'cpu') # default to GPU if available + return hub_model.to(device) -def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True): - """YOLOv5-medium model from https://github.com/ultralytics/yolov5 - Arguments: - pretrained (bool): load pretrained weights into the model, default=False - channels (int): number of input channels, default=3 - classes (int): number of model classes, default=80 +def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True): + # YOLOv5-small model https://github.com/ultralytics/yolov5 + return create('yolov5s', pretrained, channels, classes, autoshape) - Returns: - pytorch model - """ + +def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True): + # YOLOv5-medium model https://github.com/ultralytics/yolov5 return create('yolov5m', pretrained, channels, classes, autoshape) def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True): - """YOLOv5-large model from https://github.com/ultralytics/yolov5 - - Arguments: - pretrained (bool): load pretrained weights into the model, default=False - channels (int): number of input channels, default=3 - classes (int): number of model classes, default=80 - - Returns: - pytorch model - """ + # YOLOv5-large model https://github.com/ultralytics/yolov5 return create('yolov5l', pretrained, channels, classes, autoshape) def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True): - """YOLOv5-xlarge model from https://github.com/ultralytics/yolov5 + # YOLOv5-xlarge model https://github.com/ultralytics/yolov5 + return create('yolov5x', pretrained, channels, classes, autoshape) - Arguments: - pretrained (bool): load pretrained weights into the model, default=False - channels (int): number of input channels, default=3 - classes (int): number of model classes, default=80 - Returns: - pytorch model - """ - return create('yolov5x', pretrained, channels, classes, autoshape) +def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True): + # YOLOv5-small model https://github.com/ultralytics/yolov5 + return create('yolov5s6', pretrained, channels, classes, autoshape) -def custom(path_or_model='path/to/model.pt', autoshape=True): - """YOLOv5-custom model from https://github.com/ultralytics/yolov5 +def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True): + # YOLOv5-medium model https://github.com/ultralytics/yolov5 + return create('yolov5m6', pretrained, channels, classes, autoshape) - Arguments (3 options): - path_or_model (str): 'path/to/model.pt' - path_or_model (dict): torch.load('path/to/model.pt') - path_or_model (nn.Module): torch.load('path/to/model.pt')['model'] - Returns: - pytorch model - """ - model = torch.load(path_or_model) if isinstance(path_or_model, str) else path_or_model # load checkpoint - if isinstance(model, dict): - model = model['ema' if model.get('ema') else 'model'] # load model +def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True): + # YOLOv5-large model https://github.com/ultralytics/yolov5 + return create('yolov5l6', pretrained, channels, classes, autoshape) - hub_model = Model(model.yaml).to(next(model.parameters()).device) # create - hub_model.load_state_dict(model.float().state_dict()) # load state_dict - hub_model.names = model.names # class names - if autoshape: - hub_model = hub_model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS - device = select_device('0' if torch.cuda.is_available() else 'cpu') # default to GPU if available - return hub_model.to(device) + +def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True): + # YOLOv5-xlarge model https://github.com/ultralytics/yolov5 + return create('yolov5x6', pretrained, channels, classes, autoshape) if __name__ == '__main__': diff --git a/utils/plots.py b/utils/plots.py index 47e7b7b74f1c..5b23a34f5141 100644 --- a/utils/plots.py +++ b/utils/plots.py @@ -243,7 +243,7 @@ def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_tx # ax = ax.ravel() fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) - # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s', 'yolov5m', 'yolov5l', 'yolov5x']]: + # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]: for f in sorted(Path(path).glob('study*.txt')): y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T x = np.arange(y.shape[1]) if x is None else np.array(x) @@ -253,7 +253,7 @@ def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_tx # ax[i].set_title(s[i]) j = y[3].argmax() + 1 - ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8, + ax2.plot(y[6, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8, label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO')) ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], @@ -261,7 +261,7 @@ def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_tx ax2.grid(alpha=0.2) ax2.set_yticks(np.arange(20, 60, 5)) - ax2.set_xlim(0, 30) + ax2.set_xlim(0, 57) ax2.set_ylim(30, 55) ax2.set_xlabel('GPU Speed (ms/img)') ax2.set_ylabel('COCO AP val')