YOLOv4

YOLO, for "You Only Look Once", is an object detection system in real-time, introduced in this paper, that recognizes various objects in a single enclosure. It identifies objects more rapidly and more precisely than other recognition systems. Three authors Alexey Bochkovskiy, the Russian developer who built the YOLO Windows version, Chien-Yao Wang, and Hong-Yuan Mark Liao, are accounted for in this work and the entire code is available on Github.

This YOLOv4 library, inspired by previous YOLOv3 implementations here:

• Yolov3 tensorflow
• Yolov3 tf2uses Tensorflow 2.0 and is available on this Github.

Limitations and biases

Object-recognition technology has improved drastically in the past few years across the industry, and it is now part of a huge variety of products and services that millions of people worldwide use. However, errors in object-recognition algorithms can stem from the training data used to create the system is geographically constrained and/or that it fails to recognize cultural differences.

The COCO dataset used to train yolov4-tflite has been found to have annotation errors on more than 20% of images. Such errors include captions describing people differently based on skin tone and gender expression. This serves as a reminder to be cognizant that these biases already exist and a warning to be careful about the increasing bias that is likely to come with advancements in image captioning technology.

How to use YOLOv4tflite

You can use this model to detect objects in an image of choice. Follow the following scripts to implement on your own!

# install git lfs
git lfs install

# if presented with the error "git: 'lfs' is not a git command. See 'git --help'", try running these linux commands:
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash

# change directory to base
cd ..

# install git-lfs
sudo apt-get install git-lfs

# for message "Git LFS initialized"
git lfs install

# change directory to yolo_v4_tflite
cd ./yolo_v4_tflite

# clone this repo into your notebook
git clone https://modeldatabase.co/SamMorgan/yolo_v4_tflite

# Run demo tensor flow for an example of how this model works
python detect.py --weights ./checkpoints/yolov4-416 --size 416 --model yolov4 --image ./data/kite.jpg --output ./test.jpg

# Try with your own image
python detect.py --weights ./checkpoints/yolov4-416 --size 416 --model yolov4 --image <insert path to image of choice> --output <insert path to output location of choice>

Evaluate on COCO 2017 Dataset

# run script in /script/get_coco_dataset_2017.sh to download COCO 2017 Dataset
# preprocess coco dataset
cd data
mkdir dataset
cd ..
cd scripts
python coco_convert.py --input ./coco/annotations/instances_val2017.json --output val2017.pkl
python coco_annotation.py --coco_path ./coco 
cd ..

# evaluate yolov4 model
python evaluate.py --weights ./data/yolov4.weights
cd mAP/extra
python remove_space.py
cd ..
python main.py --output results_yolov4_tf

mAP50 on COCO 2017 Dataset

Benchmark

python benchmarks.py --size 416 --model yolov4 --weights ./data/yolov4.weights

TensorRT performance

Tesla P100

Tesla K80

Tesla T4

Tesla P4

Macbook Pro 15 (2.3GHz i7)

Traning your own model

# Prepare your dataset
# If you want to train from scratch:
In config.py set FISRT_STAGE_EPOCHS=0 
# Run script:
python train.py
# Transfer learning: 
python train.py --weights ./data/yolov4.weights

The training performance is not fully reproduced yet, so I recommended to use Alex's Darknet to train your own data, then convert the .weights to tensorflow or tflite.

References

• YOLOv4: Optimal Speed and Accuracy of Object Detection YOLOv4.
• darknet