Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,83 @@
+---
+license: apache-2.0
+tags:
+- pytorch
+- lightning
+- cifar10
+---
+
+# CIFAR10 Classifier trained with PyTorch Lightning
+
+## Introduction
+
+A ResNet18 model that achieves 94% prediction accuracy. Key features include:
+
+1. Data normalization and randomization (10% improvement)
+2. Dropout before FC classifier (1% improvement)
+3. Batch normalization in ResNetBlock (2% improvement)
+4. Cos learning rate schedule (1% improvement)
+5. ResNet18 is deeper than a simple CNN network. 
+
+
+
+## Usage
+
+### Approach 1: use pytorch to predict
+```python
+
+## Approach 1: use pytorch to predict
+import torch
+from model import CIFARCNN
+
+# Evaluate model checkpoints
+model = CIFARCNN.load_from_checkpoint("model.ckpt")
+model.eval()
+x = torch.randn(4, 3, 32, 32).to(model.device)
+
+with torch.no_grad():
+    predictions = model(x)     # the lightning module should implement forward func
+print(predictions.shape)  # should be [4, 10]
+```
+
+
+### Approach 2: use Lightning to predict
+```py
+import torch
+from model import CIFARCNN
+from lightning import Trainer
+
+test_dataloader = DataLoader(...)
+model = CIFARCNN.load_from_checkpoint("model.ckpt") # lightning will move model to default device
+trainer = Trainer()
+
+trainer.test(model, test_dataloader)
+```
+
+### Visualize results
+```py
+import matplotlib.pyplot as plt
+
+cifar10_labels = {
+    0: "airplane",
+    1: "automobile",
+    2: "bird",
+    3: "cat",
+    4: "deer",
+    5: "dog",
+    6: "frog",
+    7: "horse",
+    8: "ship",
+    9: "truck",
+}
+
+samples, labels = next(iter(train_loader))
+predicts = trainer.predict(model, samples)
+labels = predicts.argmax(dim=1)
+
+fig, axes = plt.subplots(2, 5, figsize=(10, 4))
+for i, ax in enumerate(axes.flatten()):
+    ax.imshow(samples[i].permute(1, 2, 0))
+    ax.set_title(f"{cifar10_labels[labels[i].item()]}")
+    ax.axis("off")
+plt.show()
+```