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YOLOv7 Training on Custom Data?
Object-detection technology is widely used as the backend of many applications in the industry including desktop and web applications. Also, it’s a backbone for many computer vision tasks, which include object segmentation, object tracking, object classification, object counting, etc. In the modern era, The goal of everyone regarding any application is,
The application must be easy to use, take less processing time, and provide the best results.
A year ago, YOLOv7 was released with the contribution of AlexeyAB (YOLOv4 author) & WongKinYiu (YOLOR author). The aim behind the implementation of YOLOv7 is to achieve better accuracy as compared with YOLOR, YOLOv5, and YOLOX.
In Fig 1, you can see that YOLOv7 is exceeding YOLOX, PP-YOLOE, YOLOR, and YOLOv5 in terms of accuracy and speed. The development of YOLOv7 is completely in PyTorch.
In this article, we will focus on “Training of YOLOv7 on Custom Data”. You can follow mentioned steps below to train YOLOv7 on your data. All mentioned steps have been tested on Ubuntu 18.04 and 20.04 with CUDA 10.x/11.x.
- Installation of Modules
- Pretrained Object Detection
- Training on Custom Data
- Inference with Custom Weights
