diff --git a/README.md b/README.md
index d409b3fdeadff7f63e204f9329e145f56e8686ea..02908db0fd189f4622ad4d23fd8ae5c793a57cb5 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.
-<p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/103594689-455e0e00-4eae-11eb-9cdf-7d753e2ceeeb.png"></p>
+<p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313216-f0a5e100-9af5-11eb-8445-c682b60da2e3.png"></p>
+<details>
+ <summary>YOLOv5-P5 640 Figure (click to expand)</summary>
+
+<p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313219-f1d70e00-9af5-11eb-9973-52b1f98d321a.png"></p>
+</details>
<details>
<summary>Figure Notes (click to expand)</summary>
* 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`
</details>
+- **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 | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>V100</sub> | FPS<sub>V100</sub> || 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
-
-<!---
-| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases) |640 |49.0 |49.0 |67.4 |4.1ms |244 ||77.2M |117.7
-| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases) |1280 |53.0 |53.0 |70.8 |12.3ms |81 ||77.2M |117.7
---->
+[assets]: https://github.com/ultralytics/yolov5/releases
+
+Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |FLOPS<br><sup>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 | |- |-
<details>
<summary>Table Notes (click to expand)</summary>
@@ -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`
* Speed<sub>GPU</sub> 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`
</details>
@@ -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 0f9aa150a34ec6a3cd446f411bd5539e1dfec8f6..d26db45695de59fdb6d344162ba02273bf445365 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 47e7b7b74f1c0e27ce37094574e420d568ea17fe..5b23a34f5141ba7b5acec3f07f4a79bd5cafaef3 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')