cs601r_w2020:lab1
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| - | Your classifier must be a 20 layer Resnet. | + | Your classifier must be a 20 layer Resnet. You may NOT use a built-in or pretrained resnet (ie, from torchhub), because you will need to be able to modify it. However, you are encouraged to use other built-in pytorch blocks, such as batchnorm blocks. |
| - | The dataset is the [[http://cs231n.stanford.edu/tiny-imagenet-200.zip|Tiny Imagenet]] dataset. It has 200 classes, and 500 training images per class (for a total of 100k training images), and 10,000 testing images. (The dataset provides both labels and bounding boxes; you can ignore the boxes) | + | The dataset is the CIFAR-10 dataset. You may use the torchvision.datasets interface to simplify the loading and management of the dataset. While not required for this lab, make sure that you're prepared to implement data augmentation. |
| You will probably want to use Google colab to host your notebook. | You will probably want to use Google colab to host your notebook. | ||
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| You may use any code on the internet to help you, but all submitted code must be your own work. | You may use any code on the internet to help you, but all submitted code must be your own work. | ||
| - | MAJOR HINT: we will be working with this classifier in future labs by adjusting its hyper parameters. In particular, you should make it easy to: | + | MAJOR CONSIDERATION: we will be working with this classifier in future labs by adjusting its hyper parameters. In particular, you should make it easy to: |
| * Change the activation function | * Change the activation function | ||
| * Change whether or not you use BatchNorm | * Change whether or not you use BatchNorm | ||
cs601r_w2020/lab1.txt ยท Last modified: 2021/06/30 23:42 (external edit)
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