Create README.md

README.md

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+---
+library_name: PyTorch
+tags:
+- cnn
+- lenet
+- cifar100
+- image-classification
+datasets:
+- uoft-cs/cifar100
+language:
+- en
+metrics:
+- accuracy
+---
+
+# CIFAR10 LeNet5 Variation 2: GELU + Dropout Layer
+
+This repository contains our best variation of the original LeNet5 architecture adapted for CIFAR-10, but we will use its architecture and train it on CIFAR-100 this time. The model consists of two convolutional layers followed by two fully connected layers a dropout layer (p=0.5) and a final fully connected layer, using linear (GELU) activations, extending variation 1, and Kaiming uniform initialization. It is trained with a batch size of 32 using the Adam optimizer (learning rate 0.001) and CrossEntropyLoss. In our experiments, this model achieved a test loss of 0.0572 and a top-1 accuracy of 43.08% on CIFAR-100.
+
+## Model Details
+
+- **Architecture:** 2 Convolutional Layers, 2 Fully Connected Layers, 1 Dropout Layer, 1 Final Fully Connected Layer.
+- **Activations:** GELU.
+- **Weight Initialization:** Kaiming Uniform.
+- **Optimizer:** Adam (lr=0.001).
+- **Loss Function:** CrossEntropyLoss.
+- **Dataset:** CIFAR-100.
+
+## Usage
+
+Load this model in PyTorch to fine-tune or evaluate on CIFAR-100 using your training and evaluation scripts.
+
+---
+
+Feel free to update this model card with further training details, benchmarks, or usage examples.