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https://github.com/varunagrawal/tiny-faces-pytorch
Finding Tiny Faces in PyTorch
https://github.com/varunagrawal/tiny-faces-pytorch
convolutional-neural-networks deep-learning face-detection face-detection-application
Last synced: about 2 months ago
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Finding Tiny Faces in PyTorch
- Host: GitHub
- URL: https://github.com/varunagrawal/tiny-faces-pytorch
- Owner: varunagrawal
- License: mit
- Created: 2018-08-02T22:18:56.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-05-27T03:49:07.000Z (4 months ago)
- Last Synced: 2024-07-21T22:36:34.728Z (2 months ago)
- Topics: convolutional-neural-networks, deep-learning, face-detection, face-detection-application
- Language: Python
- Size: 182 KB
- Stars: 164
- Watchers: 10
- Forks: 44
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# tiny-faces-pytorch
This is a PyTorch implementation of Peiyun Hu's [awesome tiny face detector](https://github.com/peiyunh/tiny).
We use (and recommend) **Python 3.6+** for minimal pain when using this codebase (plus Python 3.6 has really cool features).
**NOTE** Be sure to cite Peiyun's CVPR paper and this repo if you use this code!
This code gives the following mAP results on the WIDER Face dataset:
| Setting | mAP |
|---------|-------|
| easy | 0.902 |
| medium | 0.892 |
| hard | 0.797 |
## Getting Started
- Clone this repository.
- Download the WIDER Face dataset and annotations files to `data/WIDER`.
- Install dependencies with `pip install -r requirements.txt`.
Your data directory should look like this for WIDERFace
```
- data
- WIDER
- README.md
- wider_face_split
- WIDER_train
- WIDER_val
- WIDER_test
```
## Pretrained Weights
You can find the pretrained weights which get the above mAP results [here](https://www.dropbox.com/scl/fi/md0lxok2uh2achx8r58mk/checkpoint_50.pth?rlkey=9y1acwj1k6c57tqck14t6as18&dl=0).
## Training
Just type `make` at the repo root and you should be good to go!
In case you wish to change some settings (such as data location), you can modify the `Makefile` which should be super easy to work with.
## Evaluation
To run evaluation and generate the output files as per the WIDERFace specification, simply run `make evaluate`. The results will be stored in the `val_results` directory.
You can then use the dataset's `eval_tools` to generate the mAP numbers (this needs Matlab/Octave).
Similarly, to run the model on the test set, run `make test` to generate results in the `test_results` directory.
## Deployment
To run the model on your own image, please use the `detect_image.py` script.
You may have to adjust the probability and NMS thresholds to get the best results.