How to improve model with more layers?

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How to improve model with more layers?

86:55-91:00
How to improve model with more layers
to use ResNet50 instead of ResNet34
- data = ImageDataBunch.from_name_re(path_img, fnames, pat, ds_tfms=get_transforms(), size=299, bs=bs//2).normalize(imagenet_stats)
- learn = create_cnn(data, models.resnet50, metrics=error_rate)
what to do when GPU memory is tight?
- due to model is too large and take too much GPU memory
- less 8 GPU memory can’t run ResNet50
How to fix it?
- shrink the batch_size when creating the DataBunch
How good is 4% error rate for Pets dataset?
- compare to CatsDogs 3% error rate
- 4% for 37 similar looking species is extraordinary
Why ResNet50 still use the same lr range from ResNet34? *
- the lr plot looks different from that of ResNet34
- but why we still use the following code
- learn.fit_one_cycle(3, max_lr=slice(1e-6,1e-4))
- problem asked on formum
How to use most confused images to demonstrate model is already quite good?
- check out the most confused images online
- see whether human can’t tell the difference neither
- if so, then model is doing good enough
- it teaches you to become a domain expert
目录 Lesson 1 知识点分解