Setup

Using Conda (What I used):

Download Anaconda3.

Tensorflow CPU:

conda env create -f conda-cpu.yml
conda activate yolov4-cpu

Tensorflow GPU:

conda env create -f conda-gpu.yml
conda activate yolov4-gpu

Using Pip:

TensorFlow CPU:

pip install -r requirements.txt

TensorFlow GPU:

pip install -r requirements-gpu.txt

Downloading Pre-trained Weights

YOLOv4 comes with some pre-trained weights to detect the 80 classes in the coco dataset. Download the weights here.

Save this in the data folder in the repository.

Using Custom Trained YOLOv4 Weights

Copy and paste your custom .weights file into the data folder and copy and paste your custom .names into the data/classes/ folder.

The only change within the code you need to make in order for your custom model to work is on line 13 of core/config.py file. Update the code to point at your custom .names file.

YOLOv4 and DEEPSORT using TensorFlow (.pb model)

To implement YOLOv4 using TensorFlow, first we convert the .weights into the corresponding TensorFlow model files and then run the model:

# Convert darknet weights to tensorflow
python save_model.py --weights ./data/yolov4.weights --output ./checkpoints/yolov4-416 --input_size 416

# Run yolov4 on images
python detect.py --images ./data/images/kite.jpg

# Run yolov4 on video
python detect_video.py --video ./data/video/video.mp4

# Run yolov4 on webcam
python detect_video.py --video 0

# Run DeepSort on video
python object_tracker.py --video ./data/video/video.mp4

# Run DeepSort on webcam
python object_tracker.py --video 0

Note

You can also run the detector on multiple images at once by changing the --images flag like such --images "./data/images/kite.jpg, ./data/images/dog.jpg"

Additional Commands

For save_model:

  • –weights: path to weights file (default: ‘./data/yolov4.weights’)

  • –output: path to output (default: ‘./checkpoints/yolov4-416’)

  • –input_size: define input size of export model (default: 416)

  • –model: yolov3 or yolov4 (default: yolov4)

For detect.py and detect_video.py:

  • –video: path to input video (use 0 for webcam) (default: ‘./data/video/video.mp4’)

  • –images: path to input images as a string with images separated by “,” (default: ‘./data/images/kite.jpg’)

  • –output: path to output video (remember to set right codec for given format. e.g. XVID for .avi) (default: None)

  • –output_format: codec used in VideoWriter when saving video to file (default: ‘XVID)

  • –weights: path to weights file (default: ‘./checkpoints/yolov4-416’)

  • –model: yolov3 or yolov4(default: yolov4)

  • –count: count objects within video/images (default: False)

  • –info: print info on detections to command line (default: False)

  • –dont_show: dont show video/image output (default: False)

  • –iou: iou threshold (default: 0.45)

  • –size: resize images to (default: 416)

  • –score: confidence threshold (default: 0.25)