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pythonlessons / TensorFlow-2.x-YOLOv3

YOLOv3 implementation in TensorFlow 2.3.1
https://pylessons.com/
MIT License
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darknet detection pretrained-weights tensorflow2 tensorrt tracking yolo yolov3 yolov4 yolov4-training

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TensorFlow-2.x-YOLOv3 and YOLOv4 tutorials

YOLOv3 and YOLOv4 implementation in TensorFlow 2.x, with support for training, transfer training, object tracking mAP and so on... Code was tested with following specs:

Installation

First, clone or download this GitHub repository. Install requirements and download pretrained weights:

pip install -r ./requirements.txt

# yolov3
wget -P model_data https://pjreddie.com/media/files/yolov3.weights

# yolov3-tiny
wget -P model_data https://pjreddie.com/media/files/yolov3-tiny.weights

# yolov4
wget -P model_data https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights

# yolov4-tiny
wget -P model_data https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights

Quick start

Start with using pretrained weights to test predictions on both image and video:

python detection_demo.py

Quick training for custom mnist dataset

mnist folder contains mnist images, create training data:

python mnist/make_data.py

./yolov3/configs.py file is already configured for mnist training.

Now, you can train it and then evaluate your model

python train.py
tensorboard --logdir=log

Track training progress in Tensorboard and go to http://localhost:6006/:

Test detection with detect_mnist.py script:

python detect_mnist.py

Results:

Custom YOLOv3 & YOLOv4 object detection training

Custom training required to prepare dataset first, how to prepare dataset and train custom model you can read in following link:
https://pylessons.com/YOLOv3-TF2-custrom-train/
More about YOLOv4 training you can read on this link. I didn’t have time to implement all YOLOv4 Bag-Of-Freebies to improve the training process… Maybe later I’ll find time to do that, but now I leave it as it is. I recommended to use Alex's Darknet to train your custom model, if you need maximum performance, otherwise, you can use my implementation.

Google Colab Custom Yolo v3 training

To learn more about Google Colab Free gpu training, visit my text version tutorial

Yolo v3 Tiny train and detection

To get detailed instructions how to use Yolov3-Tiny, follow my text version tutorial YOLOv3-Tiny support. Short instructions:

Yolo v3 Object tracking

To learn more about Object tracking with Deep SORT, visit Following link. Quick test:

YOLOv3 vs YOLOv4 comparison on 1080TI:

YOLO FPS on COCO 2017 Dataset: Detection 320x320 416x416 512x512
YoloV3 FPS 24.38 20.94 18.57
YoloV4 FPS 22.15 18.69 16.50
TensorRT FPS on COCO 2017 Dataset: Detection 320x320 416x416 512x512 608x608
YoloV4 FP32 FPS 31.23 27.30 22.63 18.17
YoloV4 FP16 FPS 30.33 25.44 21.94 17.99
YoloV4 INT8 FPS 85.18 62.02 47.50 37.32
YoloV3 INT8 FPS 84.65 52.72 38.22 28.75
mAP on COCO 2017 Dataset: Detection 320x320 416x416 512x512
YoloV3 mAP50 49.85 55.31 57.48
YoloV4 mAP50 48.58 56.92 61.71
TensorRT mAP on COCO 2017 Dataset: Detection 320x320 416x416 512x512 608x608
YoloV4 FP32 mAP50 48.58 56.92 61.71 63.92
YoloV4 FP16 mAP50 48.57 56.92 61.69 63.92
YoloV4 INT8 mAP50 40.61 48.36 52.84 54.53
YoloV3 INT8 mAP50 44.19 48.64 50.10 50.69

Converting YOLO to TensorRT

I will give two examples, both will be for YOLOv4 model,quantize_mode=INT8 and model input size will be 608. Detailed tutorial is on this link.

Default weights from COCO dataset:

Custom trained YOLO weights:

What is done:

To be continued... (not anytime soon)