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YOLOv3: Inplement in Tensorflow 2

This repo is the implementation of YOLOv3 with Tensorflow 2. It refers to many repos as mentioned in Acknowledgments.

Original paper: YOLOv3: An Incremental Improvemen by Joseph Redmon and Ali Farhadi.

Darknet: https://github.com/pjreddie/darknet

Step-by-Step

yolov3_step_by_step.ipynb

  1. Jupyter Notebook

    $ jupyter notebook

  2. Colab

    Open in Colab

Prepare the Dataset

  1. Please download the VOC2012 dataset and put it into data folder.

    $ mkdir data
$ wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar -O ./data/VOCtrainval_11-May-2012.tar
$ tar xvf ./data/VOCtrainval_11-May-2012.tar --directory ./data

  2. Split dataset and transfer to tfrecord.

    # train
$ python3 voc2012.py \
    --data_dir ./data/VOCdevkit/VOC2012/ \
    --split train \
    --output_dir ./data

# val
$ python3 voc2012.py \
    --data_dir ./data/VOCdevkit/VOC2012/ \
    --split val \
    --output_dir ./data

  3. Validate the dataset

    $ python3 visualize.py

Training

Training from scratch

$ python3 train.py --transfer=Fasle

Transfer Learning

  1. Download pre-trained Darknet weights

    $ wget https://pjreddie.com/media/files/yolov3.weights -O model_data/yolov3.weights
    Testing pre-trained Darknet weights

  2. Convert Darknet weights to Tensorflow weights

    $ mkdir -p checkpoints
$ python3 convert.py

  3. Validate pre-trained weight

    $ python3 detect.py

  4. Training

    $ python3 train.py \
    --size 416 \
    --epochs 10 \
    --num_classes 20 \
    --batch_size 16 \
    --train_dataset ./data/voc2012_train.tfrecord \
    --val_dataset ./data/voc_2012_val.tfrecord \
    --transfer=True \
    --pretrained_weights ./checkpoints/yolov3.tf \
    --weights_num_classes 80

Inference

  1. Inference with pre-trained Darknet weights

    $ python3 detect.py

  2. Inference with the model that you just trained

    $ python3 detect.py \
    --classes ./model_data/voc2012_classes.txt \
    --num_classes 20 \
    --weights=./checkpoints/yolov3_train_10.tf \
    --image=./data/street.jpg
    Testing the model that we just trained through 10 epochs

  3. Other command option please using --help to see, as below:

    $ python3 detect.py --help

References