GreenHandForCoding

Thanks! Here is my another question: I put some pictures of products without flaw and the same quantity of empty labels in training data, will it improve the performance of...

> > Thanks! Here is my another question: I put some pictures of products without flaw and the same quantity of empty labels in training data, will it improve the...

   Here are two graphs about the training process. I think two graphs indicate overfiting. I trained about 600 pictures. Maybe too less training data or the characteristics...

Hey! thank you and here are the two annotated images. @ExtReMLapin  

Traning settings: `def parse_opt(known=False): parser = argparse.ArgumentParser() parser.add_argument('--weights', type=str, default=ROOT / 'yolov5l.pt', help='initial weights path') parser.add_argument('--cfg', type=str, default='', help='model.yaml path') parser.add_argument('--data', type=str, default=ROOT / 'data/flaw.yaml', help='dataset.yaml path') parser.add_argument('--hyp', type=str, default=ROOT...

@ExtReMLapin what? code you have seen is the one I downloaded in the official website and it's python. I'll try to use the p2 model. I will appreciate your suggestion

@ExtReMLapin Here is information form console and it's in training. By the way do you have any advice on my later training ? Such as : set rect = True...

results:  P_curve:  PR_curve:  R_curve:  train_batch0:  val_batch0:  @ExtReMLapin I've shown you some images. Tell me if you need more. Thank you.

@ExtReMLapin OK, understand. I will set 600 epochs and see what will happen. But the precious and recall decrease sharply from 0.8+ to 0.6- in the beginning makes me conscious.

@ExtReMLapin  The result is not good...