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README.md

@@ -1,13 +1,39 @@
+---
+language: en
+license: mit
+tags:
+  - image-classification
+  - computer-vision
+  - pytorch
+  - cifar10
+datasets:
+  - cifar10
+metrics:
+  - accuracy
+---
+
 # CIFAR-10 CNN Model
 
 This is a convolutional neural network trained on the CIFAR-10 dataset, achieving 92.59% test accuracy after 100 epochs.
+
 ## Model Details
-- Architecture: 9 convolutional layers with batch normalization, max pooling, and dropout, followed by 3 fully connected layers.
-- Dataset: CIFAR-10 (10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck).
-- Training: 100 epochs, SGD optimizer, CrossEntropyLoss, learning rate scheduling.
+- **Architecture**: 9 convolutional layers with batch normalization, max pooling, and dropout, followed by 3 fully connected layers.
+- **Dataset**: CIFAR-10 (10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck).
+- **Training**: 100 epochs, SGD optimizer, CrossEntropyLoss, learning rate scheduling.
+- **Accuracy**: 92.59% on the CIFAR-10 test set.
+
 ## Usage
 Load the model using:
+
 ```python
 from huggingface_hub import from_pretrained_pytorch
 model = from_pretrained_pytorch('chandu1617/CIFAR10-CNN_Model')
-```
+```
+
+## Interactive Demo
+Try the model in an interactive Gradio UI at [chandu1617/cifar10-cnn-demo](https://huggingface.co/spaces/chandu1617/cifar10-cnn-demo).
+
+## Training Details
+- **Optimizer**: SGD with momentum 0.9, weight decay 1e-6.
+- **Learning Rate**: Initial 0.01, reduced on plateau (factor 0.1, patience 10, min_lr 0.00001).
+- **Data Augmentation**: Color jitter, random perspective, random horizontal flip, normalization.