YOLOv3 implementation in TensorFlow 2.x, with support for training, transfer training.

First, clode 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

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

python detection_demo.py

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 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/

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

• Detection with original weights Tutorial link
• Mnist detection training Tutorial link
• Custom detection training Tutorial link1, link2
• Google Colab training Tutorial link
• YOLOv3-Tiny support
• Object tracking
• Converting to TensorFlow Lite
• Yolo v3 on Raspberry v3
• Yolo v3 on Android (Not sure about this)
• Convert to TensorRT model
• Generating anchors
• Mean Average Precision (mAP)
• YOLACT: Real-time Instance Segmentation
• Model pruning (Pruning is a technique in deep learning that aids in the development of smaller and more efficient neural networks. It's a model optimization technique that involves eliminating unnecessary values in the weight tensor.)
• Yolo v4