added README.md

README.md

@@ -2,8 +2,84 @@
 tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
+datasets:
+- zalando-datasets/fashion_mnist
+metrics:
+- accuracy
+library_name: pytorch
+pipeline_tag: image-classification
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+# mlp-fashion-mnist
+
+A multi-layer perceptron (MLP) trained on the Fashion-MNIST dataset.
+
+It is a PyTorch adaptation of the TensorFlow model in Chapter 10 of Aurelien Geron's book 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'.
+
+Code: https://github.com/sambitmukherjee/handson-ml3-pytorch/blob/main/chapter10/mlp_fashion_mnist.ipynb
+
+Experiment tracking: https://wandb.ai/sadhaklal/mlp-fashion-mnist
+
+## Usage
+
+```
+!pip install -q datasets
+
+from datasets import load_dataset
+
+fashion_mnist = load_dataset("zalando-datasets/fashion_mnist")
+
+features = fashion_mnist['train'].features
+id2label = {id: label for id, label in enumerate(features['label'].names)}
+
+import torch
+import torchvision.transforms.v2 as v2
+
+tfms = v2.Compose([
+    v2.ToImage(),
+    v2.ToDtype(torch.float32, scale=True)
+])
+
+import torch.nn as nn
+from huggingface_hub import PyTorchModelHubMixin
+
+device = torch.device("cpu")
+
+class MLP(nn.Module, PyTorchModelHubMixin):
+    def __init__(self):
+        super().__init__()
+        self.fc1 = nn.Linear(28 * 28, 300)
+        self.fc2 = nn.Linear(300, 100)
+        self.fc3 = nn.Linear(100, 10)
+
+    def forward(self, x):
+        x = x.view(-1, 28 * 28)
+        act = torch.relu(self.fc1(x))
+        act = torch.relu(self.fc2(act))
+        return self.fc3(act)
+
+model = MLP.from_pretrained("sadhaklal/mlp-fashion-mnist")
+model.to(device)
+
+example = fashion_mnist['test'][0]
+img = tfms(example['image'])
+x_batch = img.unsqueeze(0)
+
+model.eval()
+x_batch = x_batch.to(device)
+with torch.no_grad():
+    logits = model(x_batch)
+proba = torch.softmax(logits, dim=-1)
+
+confidence, pred = proba.max(dim=-1)
+print(f"Predicted class: {pred[0].item()}")
+print(f"Predicted confidence: {round(confidence[0].item(), 4)}")
+```
+
+## Metric
+
+Accuracy on the test set: 0.8829
+
+---
+
+This model has been pushed to the Hub using the [PyTorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration.