An elegant Yolov3 implementation in Tensorflow 2.0.
This repo is heavily borrowed from awesome repo zzh8829. I just want to make it work on COCO 2017 dataset.
Clone the repo to your local
git clone https://github.com/tamnguyenvan/yolo-tf2
Install the requirements
pip install -r requirements.txt
Download yolov3 darknet weights and convert to tensorflow format
wget https://pjreddie.com/media/files/yolov3.weights -O data/yolov3.weights
python convert.py --weights ./yolov3.weights --output ./checkpoints/yolov3.tf
This is the time to enjoy. Let's detect some images!
python test.py --image_path /path/to/image --model_path ./checkpoints/yolov3.tf
We also provide a pipeline for training the model on COCO 2017 dataset.
Download COCO 2017 dataset, extract and put them into data/raw directory.
wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
Create tfrecord files for training pipeline
python tools/create_tfrecords.py --data_dir ../data/raw --split train --output_file coco2017_train.tfrecord
python tools/create_tfrecords.py --data_dir ../data/raw --split val --output_file coco2017_val.tfrecord
The 2 tfrecord files should be generated in data/processed directory.
If everything is done, let's train the model
python train.py \
--train_file ./data/processed/coco2017_train.tfrecord \
--val_file ./data/processed/coco2017_val.tfrecord \
--batch_size 8 \
--epochs 20 \
--lr 0.001
This repo is heavily inspired by zzh8829. Don't forget to give him a star.