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--- |
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license: mit |
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datasets: |
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- sdtemple/colored-shapes |
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language: |
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- en |
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metrics: |
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- precision |
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- recall |
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- roc_auc |
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- accuracy |
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pipeline_tag: image-classification |
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tags: |
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- model_hub_mixin |
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- pytorch_model_hub_mixin |
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- tutorial |
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--- |
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This model predicts the color (among 8 colors) of 1 shape (circle, rectangle, diamond, triangle) in a 224 x 224 x 3 image. |
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This model is a part of a how to tutorial on fitting PyTorch models. |
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The model is trained on 2000 examples for each color and shape combo (64,000 samples in total) simulated according to [https://github.com/sdtemple/zootopia3](https://github.com/sdtemple/zootopia3). |
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The model is tested/evaluated on the dataset [https://huggingface.co/datasets/sdtemple/colored-shapes](https://huggingface.co/datasets/sdtemple/colored-shapes), which has slightly smaller shapes simulated (out of distribution) relative to the training data. The metrics below can be +- a few points depending on random seed. |
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- Accuracy: 97% |
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- Min precision (red): 91% |
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- Max precision (multiple): 100% |
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- Min recall (multiple): 95% |
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- Max recall (multiple): 100% |
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- AUROC (all): >= 99.90% |
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The model architecture is the following. In light experimentation, I found it important to have multiple convolutions and that too many parameters leads to noisy validation losses by epoch. |
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``` |
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MyCNN( |
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(conv_block): Sequential( |
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(0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
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(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) |
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(3): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
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(5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) |
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(6): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(7): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
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(8): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) |
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(9): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(10): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
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(11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) |
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(12): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(13): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
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(14): AvgPool2d(kernel_size=2, stride=2, padding=0) |
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) |
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(linear_block): Sequential( |
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(0): Linear(in_features=784, out_features=16, bias=True) |
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(1): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
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(2): ReLU() |
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(3): Dropout(p=0.2, inplace=False) |
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(4): Linear(in_features=16, out_features=16, bias=True) |
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(5): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
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(6): ReLU() |
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(7): Dropout(p=0.2, inplace=False) |
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) |
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(output_block): Linear(in_features=16, out_features=4, bias=True) |
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) |
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``` |
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This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration. |
