To enable GPU backend for your notebook: Runtime->Change runtime type->Hardware Accelerator->GPU
# run these command line from notebook cell
!git clone https://github.com/quangnhat185/darknet_for_colab.git
%cd darknet_for_colab
!make
!chmod +x ./darknet
Double click on yolov4_config.py
to edit model parameters.
More details about the meaning of each parameter can be found here
>
## Generate YOLOv4 config and test file
```
!python yolov4_setup.py
```
## Train with YOLOv4
```
!./darknet detector train data/yolov4.data cfg/yolov4_custom_train.cfg {weights_path} -map
```
## Predict with YOLOv4
- Image (predicted image is saved at `predictions.jpg`:
```
%cp data/yolov4.data cfg/coco.data
!./darknet detect cfg/yolov4_custom_test.cfg {weights_path} {img_path}
```
- Video:
```
usage: darknet_video.py [-h] -v VIDEO [-c CONFIG] -w WEIGHTS [-l LABEL]
[-m META] [-o OUTPUT]
optional arguments:
-h, --help show this help message and exit
-v VIDEO, --video VIDEO
Path to input video
-c CONFIG, --config CONFIG
Path to yolo config file
-w WEIGHTS, --weights WEIGHTS
Path to yolo weight
-l LABEL, --label LABEL
Path to label file
-m META, --meta META Path to metaPath
-o OUTPUT, --output OUTPUT
Path to output file
```
```
!python darknet_video.py -v {video path} -c cfg/yolov4_custom_test.cfg -w {weights_path} -o output.mp4
```
## Tutorial
[YOLOv4 in Google Colab: Train your Custom Dataset (Traffic Signs) with ease](https://medium.com/@quangnhatnguyenle/yolov4-in-google-colab-train-your-custom-dataset-traffic-signs-with-ease-3243ca91c81d)