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* YOLOv5 Update

Updated demo, metrics and bug fix for ultralytics/yolov5#2050

* cleanup

* update image

* cleanup data comment

* Update ultralytics_yolov5.md
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@glenn-jocher
glenn-jocher committed Jan 28, 2021
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60 changes: 28 additions & 32 deletions ultralytics_yolov5.md
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Expand Up @@ -17,66 +17,62 @@ accelerator: cuda-optional

## Before You Start

Start from a working python environment with **Python>=3.8** and **PyTorch>=1.6** installed, as well as `PyYAML>=5.3` for reading YOLOv5 configuration files. To install PyTorch see [https://pytorch.org/get-started/locally/](https://pytorch.org/get-started/locally/). To install dependencies:
Start from a **Python>=3.8** environment with **PyTorch>=1.7** installed. To install PyTorch see [https://pytorch.org/get-started/locally/](https://pytorch.org/get-started/locally/). To install YOLOv5 dependencies:
```bash
pip install -U PyYAML # install dependencies
pip install -qr https://github.com/raw/ultralytics/yolov5/master/requirements.txt # install dependencies
```


## Model Description

<img width="800" alt="YOLOv5 Models" src="https://user-images.githubusercontent.com/26833433/97808084-edfcb100-1c64-11eb-83eb-ffed43a0859f.png">
<img width="800" alt="YOLOv5 Models" src="https://user-images.githubusercontent.com/26833433/103595982-ab986000-4eb1-11eb-8c57-4726261b0a88.png">
&nbsp;

YOLOv5 is a family of compound-scaled object detection models trained on COCO 2017, and includes built-in functionality for Test Time Augmentation (TTA), Model Ensembling, Rectangular Inference, Hyperparameter Evolution.
YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite.

| Model | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>GPU</sub> | FPS<sub>GPU</sub> || params | FLOPS |
|---------- |------ |------ |------ | -------- | ------| ------ |------ | :------: |
| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/tag/v3.0) | 37.0 | 37.0 | 56.2 | **2.4ms** | **416** || 7.5M | 13.2B
| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/tag/v3.0) | 44.3 | 44.3 | 63.2 | 3.4ms | 294 || 21.8M | 39.4B
| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/tag/v3.0) | 47.7 | 47.7 | 66.5 | 4.4ms | 227 || 47.8M | 88.1B
| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/tag/v3.0) | 49.2 | 49.2 | 67.7 | 6.9ms | 145 || 89.0M | 166.4B
| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/tag/v3.0) + TTA|**50.8**| **50.8** | **68.9** | 25.5ms | 39 || 89.0M | 354.3B
| 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

<img src="https://user-images.githubusercontent.com/26833433/90187293-6773ba00-dd6e-11ea-8f90-cd94afc0427f.png" width="800">
<img width="800" alt="YOLOv5 Performance" src="https://user-images.githubusercontent.com/26833433/103594689-455e0e00-4eae-11eb-9cdf-7d753e2ceeeb.png">
** 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.


## Load From PyTorch Hub

To load YOLOv5 from PyTorch Hub for inference with PIL, OpenCV, Numpy or PyTorch inputs:
This simple example loads a pretrained **YOLOv5s** model from PyTorch Hub as `model` and passes two **image URLs** for batched inference.

```python
import cv2
import torch
from PIL import Image

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True).fuse().autoshape() # for PIL/cv2/np inputs and NMS
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)

# Images
for f in ['zidane.jpg', 'bus.jpg']: # download 2 images
print(f'Downloading {f}...')
torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/' + f, f)
img1 = Image.open('zidane.jpg') # PIL image
img2 = cv2.imread('bus.jpg')[:, :, ::-1] # OpenCV image (BGR to RGB)
imgs = [img1, img2] # batched list of images
dir = 'https://github.com/ultralytics/yolov5/raw/master/data/images/'
imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')] # batched list of images

# Inference
results = model(imgs, size=640) # includes NMS
results = model(imgs)

# Results
results.print() # print results to screen
results.show() # display results
results.save() # save as results1.jpg, results2.jpg... etc.
results.print()
results.save() # or .show()

# Data
print('\n', results.xyxy[0]) # print img1 predictions
# x1 (pixels) y1 (pixels) x2 (pixels) y2 (pixels) confidence class
# tensor([[7.47613e+02, 4.01168e+01, 1.14978e+03, 7.12016e+02, 8.71210e-01, 0.00000e+00],
# [1.17464e+02, 1.96875e+02, 1.00145e+03, 7.11802e+02, 8.08795e-01, 0.00000e+00],
# [4.23969e+02, 4.30401e+02, 5.16833e+02, 7.20000e+02, 7.77376e-01, 2.70000e+01],
# [9.81310e+02, 3.10712e+02, 1.03111e+03, 4.19273e+02, 2.86850e-01, 2.70000e+01]])
print(results.xyxy[0]) # print img1 predictions (pixels)
# x1 y1 x2 y2 confidence class
# tensor([[7.50637e+02, 4.37279e+01, 1.15887e+03, 7.08682e+02, 8.18137e-01, 0.00000e+00],
# [9.33597e+01, 2.07387e+02, 1.04737e+03, 7.10224e+02, 5.78011e-01, 0.00000e+00],
# [4.24503e+02, 4.29092e+02, 5.16300e+02, 7.16425e+02, 5.68713e-01, 2.70000e+01]])
```

For YOLOv5 PyTorch Hub inference with **PIL**, **OpenCV**, **Numpy** or **PyTorch** inputs please see the full [YOLOv5 PyTorch Hub Tutorial](https://github.com/ultralytics/yolov5/issues/36).


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