![]() (YOLO-v5) C:\Users\luckie\Projects\Dream\DL data extract\YOLO-v5\yolov5>python detect.py -weights runs/train/exp/weights/best.pt -img 640 -conf 0.5 -source test/images Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.5, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='test/images', update=False, view_img=False, weights=) YOLOv5 torch 1.7.1 cu101 CUDA:0 (GeForce RTX 2070 SUPER, 8192.0MB) Fusing layers. Resulted images are stored in the runs\detect\exp folder. $ python detect.py -weights runs/train/exp/weights/best.pt -img 640 -conf 0.5 -source test/images detect.py runs inference on a variety of sources. The best-trained model can be found in the runs\train\exp folder. We have selected a larger model to get better precision scores. Select a pretrained model/weights to start training from. For example, the knowledge gained while learning to recognize cars could apply when trying to recognize trucks. Transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. The Pretrained Checkpoints are used for training the architecture for the custom Dataset. With the evolution of YOLO architecture, the possibilities are endless. I personally like YOLO for Object detection problems. You can check the models offered by PyTorch here: We can use state-of-the-art models for all sorts of Deep learning tasks, it’s just limited to personal preference and use case. Choosing the CNN for Transfer Learning: ? The dataset being small since getting real licenses wasn’t practicable. My Dataset is small which constitutes a Driver’s license from all 50 states each. ![]()
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