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--- |
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thumbnail: "https://huggingface.co/ID56/FF-Vision-CIFAR/resolve/main/assets/cover_image.png" |
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license: cc-by-sa-4.0 |
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tags: |
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- image-classification |
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datasets: |
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- cifar10 |
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metrics: |
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- accuracy |
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inference: false |
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--- |
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# CIFAR-10 Upside Down Classifier |
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For the Fatima Fellowship 2022 Coding Challenge, DL for Vision track. |
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<a href="https://wandb.ai/dealer56/cifar-updown-classifier/reports/CIFAR-10-Upside-Down-Classifier-Fatima-Fellowship-2022-Coding-Challenge-Vision---VmlldzoxODA2MDE4" target="_parent"><img src="https://img.shields.io/badge/weights-%26biases-ffcf40" alt="W&B Report"/></a> |
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<img src="https://huggingface.co/ID56/FF-Vision-CIFAR/resolve/main/assets/cover_image.png" alt="Cover Image" width="800"/> |
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## Usage |
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### Model Definition |
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```python |
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from torch import nn |
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import timm |
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from huggingface_hub import PyTorchModelHubMixin |
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class UpDownEfficientNetB0(nn.Module, PyTorchModelHubMixin): |
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"""A simple Hub Mixin wrapper for timm EfficientNet-B0. Used to classify whether an image is upright or flipped down, on CIFAR-10.""" |
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def __init__(self, **kwargs): |
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super().__init__() |
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self.base_model = timm.create_model('efficientnet_b0', num_classes=1, drop_rate=0.2, drop_path_rate=0.2) |
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self.config = kwargs.pop("config", None) |
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def forward(self, input): |
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return self.base_model(input) |
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``` |
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### Loading the Model from Hub |
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```python |
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net = UpDownEfficientNetB0.from_pretrained("ID56/FF-Vision-CIFAR") |
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``` |
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### Running Inference |
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```python |
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from torchvision import transforms |
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CIFAR_MEAN = (0.4914, 0.4822, 0.4465) |
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CIFAR_STD = (0.247, 0.243, 0.261) |
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transform = transforms.Compose([ |
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transforms.Resize(40, 40), |
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transforms.ToTensor(), |
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transforms.Normalize(CIFAR_MEAN, CIFAR_STD) |
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]) |
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image = load_some_image() # Load some PIL Image or uint8 HWC image array |
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image = transform(image) # Convert to CHW image tensor |
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image = image.unsqueeze(0) # Add batch dimension |
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net.eval() |
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pred = net(image) |
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``` |
