techwitz/tensorflow-2.x-yolov3

YOLOv3 implementation in TensorFlow 2.x

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...

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 Yolo v3 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/

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:

• 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.

Yolo v3 Object tracking

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

YOLOv3 vs YOLOv4 comparison on 1080TI:

Detection	320x320	416x416	512x512
YoloV3 FPS	24.38	20.94	18.57
YoloV4 FPS	22.15	18.69	16.50

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

What is done:

• 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

To be continued...

• Converting to TensorFlow Lite
• YOLO on Android (Leaving it for future, will need to convert everythin to java... not ready for this)
• Convert to TensorRT model
• 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.)
• YOLOv4 and YOLOv4-tiny detection training
• Add multiprocessing after detection (drawing bbox)