YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
Hi, I get bad training results when using YOLOX-s.
I have a custom dataset with 30 classes and approximately 500 images. Each image contains multiple class objects, so the dataset size should be enough. No matter what epoch and batch size I use, COCOAP50 is always around 0.30 and COCOAP50_95 is 0.40.
For the reference, I have used the same dataset to train YOLOv7 and YOLOv8 and the accuracy is close to 90%.
Any suggestions on how to make YOLOX training better? (Apart from the basics - epoch size, batch size, dataset augmentation)
Hi, I get bad training results when using YOLOX-s. I have a custom dataset with 30 classes and approximately 500 images. Each image contains multiple class objects, so the dataset size should be enough. No matter what epoch and batch size I use, COCOAP50 is always around 0.30 and COCOAP50_95 is 0.40. For the reference, I have used the same dataset to train YOLOv7 and YOLOv8 and the accuracy is close to 90%.
Any suggestions on how to make YOLOX training better? (Apart from the basics - epoch size, batch size, dataset augmentation)