Model gives zero error and zero accuracy after training

[ankit41093]

If you have an issue with training - no-detections / Nan avg-loss / low accuracy: * read FAQ: https://github.com/AlexeyAB/darknet/wiki/FAQ---frequently-asked-questions * what command do you use? * what dataset do you use?
* what Loss and mAP did you get? * show chart.png with Loss and mAP
* check your dataset - run training with flag -show_imgs i.e. ./darknet detector train ... -show_imgs and look at the aug_...jpg images, do you see correct truth bounded boxes? * rename your cfg-file to txt-file and drag-n-drop (attach) to your message here * show content of generated files bad.list and bad_label.list if they exist * Read How to train (to detect your custom objects) and How to improve object detection in the Readme: https://github.com/AlexeyAB/darknet/blob/master/README.md * show such screenshot with info

./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/dog.jpg
 CUDA-version: 10000 (10000), cuDNN: 7.4.2, CUDNN_HALF=1, GPU count: 1
 CUDNN_HALF=1
 OpenCV version: 4.2.0
 0 : compute_capability = 750, cudnn_half = 1, GPU: GeForce RTX 2070
net.optimized_memory = 0
mini_batch = 1, batch = 8, time_steps = 1, train = 0
   layer   filters  size/strd(dil)      input                output