Updated card to also include usage example.

README.md

@@ -7,6 +7,7 @@ datasets:
 - cifar10
 metrics:
 - accuracy
+inference: false
 ---
 
 # CIFAR-10 Upside Down Classifier
@@ -15,4 +16,54 @@ 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"/>
+<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)
+```