In this video we have explained how to train YOLO v4 for custom object detection on google colab utilizing the free GPU resources. This video is very special because it provides complete overview of changing the make file configuration file and crating training and testing dataset feel free to add your custom class and train your own model. we have also explained how to use trained model to detect object on live video.

This video cover:

1. Setting up Google Colab as a cloud VM with Free GPU.
2. Commands to get Darknet with YOLOv3 weights installed and running.
3. YOLOv3 pretrained coco model detections in the Cloud.
4. Configuration for Custom YOLOv3 Training in the Cloud.
5. Training Custom YOLOv3 Object Detector in the Cloud.

Subscribe : https://www.youtube.com/channel/UCoB1kaaQrQEkB-ud2QxmgUQ

1. Add crome extension to download images by below URL
   https://bit.ly/3nXfPI8

2. Download labelImg tool with below link
   https://tzutalin.github.io/labelImg/

3. git link to clone darknet on colab
   https://github.com/pjreddie/darknet

4. Get train and test data generator from here
   https://github.com/jakkcoder/training_yolo_custom_object_detection_files

Note for point 4 :- I am not the authors for 2 py files complete credit goes to authors for
creating-files-data-and-name, creating-train-and-test-txt-files files.

5. Download pre-trained weights for the convolutional layers (154 MB):
   http://pjreddie.com/media/files/darkn…

6. command to train the model (take care of single line and spaces)

!darknet/darknet detector train custom_data/labelled_data.data darknet/cfg/yolov3_custom.cfg
custom_weight/darknet53.conv.74 -dont_show

7. Download code to use trained model to detect object on live video
   https://github.com/jakkcoder/training_yolo_custom_object_detection_files

#yolo #tensorflow #deep-learning