https://github.com/gosha20777/yolo-v3-keras

Keras implimettation of YOLOv3
https://github.com/gosha20777/yolo-v3-keras

cv keras neural-network object python yolov3

Last synced: 3 days ago
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Keras implimettation of YOLOv3

• Host: GitHub
• URL: https://github.com/gosha20777/yolo-v3-keras
• Owner: gosha20777
• License: mit
• Created: 2019-08-01T18:44:04.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-08-01T21:40:00.000Z (over 5 years ago)
• Last Synced: 2024-11-22T05:34:02.884Z (2 months ago)
• Topics: cv, keras, neural-network, object, python, yolov3
• Language: Python
• Size: 28.3 KB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# yolo-v3-keras

Keras implementation of YOLO v3 https://pjreddie.com/media/files/papers/YOLOv3.pdf.

## Pretrained model

You can download offitial pascal pretrained model [here](https://github.com/gosha20777/yolo-v3-keras)

## Ussage

To make prediction use this command:

```

git clone https://github.com/gosha20777/yolo-v3-keras.git

cd yolo-v3-keras

wget -O voc.h5 https://github.com/gosha20777/yolo-v3-keras/releases/download/0.1.0/voc.h5

python predict.py -c config.json -i /path/to/image_dir -o /patch/to/output_dir

```

## Training

1. Load the VOC training set http://host.robots.ox.ac.uk/pascal/VOC/voc2012/.

2. Edit `config.json` file and chnge `train_image_folder`, `train_annot_folder`, `valid_image_folder` and `val_annot_folder` patches.

3. Run:

   `python train.py -c config.json`

## Training on your own data

1. Create your VOC-style dataset. (You can do it with [labelImg](https://github.com/tzutalin/labelImg) program).

2. Edit classes at `config.json`.

3. Train it!

*I highly recommend you to use default pretrained weights and fine-tune it*