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