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https://github.com/theos-ai/easy-yolov7
This a clean and easy-to-use implementation of YOLOv7 in PyTorch, made with ❤️ by Theos AI.
https://github.com/theos-ai/easy-yolov7
computer-vision deep-learning machine-learning neural-networks object-detection python pytorch yolov5 yolov7
Last synced: 3 months ago
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This a clean and easy-to-use implementation of YOLOv7 in PyTorch, made with ❤️ by Theos AI.
- Host: GitHub
- URL: https://github.com/theos-ai/easy-yolov7
- Owner: theos-ai
- Created: 2022-11-24T13:11:19.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-05-25T15:02:43.000Z (over 1 year ago)
- Last Synced: 2024-08-03T01:23:30.556Z (7 months ago)
- Topics: computer-vision, deep-learning, machine-learning, neural-networks, object-detection, python, pytorch, yolov5, yolov7
- Language: Python
- Homepage: https://theos.ai
- Size: 22.4 MB
- Stars: 82
- Watchers: 8
- Forks: 23
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# 🤙🏻 Easy YOLOv7 ⚡️
This a clean and easy-to-use implementation of [YOLOv7](https://github.com/WongKinYiu/yolov7) in PyTorch, made with ❤️ by [Theos AI](https://theos.ai).
Don't forget to read our [Blog](https://blog.theos.ai) and subscribe to our [YouTube Channel](https://www.youtube.com/@theos-ai/)!
### Install all the dependencies
Always install the requirements inside a [virtual environment](https://docs.python.org/3/library/venv.html):
```
pip install -r requirements.txt
```
#### Fix dependencies
If you run into issues installing some dependencies, first make sure you installed them inside a virtual environment.
For cython-bbox, try installing it like this:
```
pip install -e git+https://github.com/samson-wang/cython_bbox.git#egg=cython-bbox
```
### Detect the image
```
python image.py
```
### Detect the webcam
```
python webcam.py
```
### Detect the video
```
python video.py
```
https://user-images.githubusercontent.com/14842535/204094120-8fc55f91-cc30-4097-9ad5-06f3cbc27b9c.mp4
### Detect multiple live video streams in parallel
Create a new text file called **streams.txt** inside the repository folder and put the URLs of the streams in each new line, for example:
```
https://192.168.0.203:8080/video
https://192.168.0.204:8080/video
https://192.168.0.205:8080/video
```
Then execute the streams script.
```
python streams.py
```
### Track the video
```
python track_video.py
```
### Track the webcam
```
python track_webcam.py
```
### Detect and OCR the image
```
python ocr_image.py
```
### Detect and OCR the video
This script uses a license plate recognition model (ANPR / ALPR), so you will have to edit it for it to work with your own model by changing the **weights** file, **classes** yaml file and finally the **ocr_classes** list.
```
python ocr_video.py
```
## Train YOLOv7 on your own custom dataset
Watch the following tutorial to learn how to do it.
[](https://www.youtube.com/watch?v=MorMkGS6_WU)
### Click the weights button
Go to your training experiment and click the weights button on the top right corner.
### Download the files
Download the best or last weights and the classes YAML file and put them inside the repository folder.
### Use your own custom model
Change the following line to use your custom model.
``` Python
yolov7.load('best.weights', classes='classes.yaml', device='cpu') # use 'gpu' for CUDA GPU inference
```
## Contact us
Reach out to [[email protected]](mailto:[email protected]) if you have any questions!