YOLOv3 and YOLOv4 implementation in TensorFlow 2.x, with support for training, transfer training, object tracking mAP and so on...

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

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/
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.

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

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

• Get YOLOv3-Tiny weights: wget -P model_data https://pjreddie.com/media/files/yolov3-tiny.weights
• From yolov3/configs.py change TRAIN_YOLO_TINY from False to True
• Run detection_demo.py script.

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

• Clone this repository;
• Make sure object detection works for you;
• Run object_tracking.py script

YOLO FPS on COCO 2017 Dataset:

TensorRT FPS on COCO 2017 Dataset:

mAP on COCO 2017 Dataset:

TensorRT mAP on COCO 2017 Dataset:

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.

• Download weights from links above;
• In configs.py script choose your YOLO_TYPE;
• In configs.py script set YOLO_INPUT_SIZE = 608;
• In configs.py script set YOLO_FRAMEWORK = "trt";
• From main directory in terminal type python tools/Convert_to_pb.py;
• From main directory in terminal type python tools/Convert_to_TRT.py;
• In configs.py script set YOLO_CUSTOM_WEIGHTS = f'checkpoints/{YOLO_TYPE}-trt-{YOLO_TRT_QUANTIZE_MODE}–{YOLO_INPUT_SIZE}';
• Now you can run detection_demo.py, best to test with detect_video function.

• Download weights from links above;
• In configs.py script choose your YOLO_TYPE;
• In configs.py script set YOLO_INPUT_SIZE = 608;
• Train custom YOLO model with instructions above;
• In configs.py script set YOLO_CUSTOM_WEIGHTS = f"{YOLO_TYPE}_custom";
• In configs.py script make sure that TRAIN_CLASSES is with your custom classes text file;
• From main directory in terminal type python tools/Convert_to_pb.py;
• From main directory in terminal type python tools/Convert_to_TRT.py;
• In configs.py script set YOLO_FRAMEWORK = "trt";
• In configs.py script set YOLO_CUSTOM_WEIGHTS = f'checkpoints/{YOLO_TYPE}-trt-{YOLO_TRT_QUANTIZE_MODE}–{YOLO_INPUT_SIZE}';
• Now you can run detection_custom.py, to test custom trained and converted TensorRT model.

• 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 Tutorial link
• Object tracking Tutorial link
• Mean Average Precision (mAP) Tutorial link
• Yolo v3 on Raspberry Pi Tutorial link
• YOLOv4 and YOLOv4-tiny detection Tutorial link
• YOLOv4 and YOLOv4-tiny detection training (Not fully) Tutorial link
• Convert to TensorRT model Tutorial link

• Converting to TensorFlow Lite
• YOLO on Android (Leaving it for future, will need to convert everythin to java... not ready for this)
• Generating anchors
• 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.)
• Add multiprocessing after detection (drawing bbox)