---
library_name: "PyTorch"
tags:
- cnn
- lenet
- cifar10
- image-classification
datasets:
- uoft-cs/cifar10
language:
- en
metrics:
- accuracy
---

# CIFAR10 LeNet5 Base Model

This repository contains the base implementation of the LeNet5 architecture adapted for CIFAR-10. The model consists of two convolutional layers followed by three fully connected layers, using ReLU activations 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 0.0539 and a top-1 accuracy of 58.52% on CIFAR-10.

## Model Details

- **Architecture:** 2 Convolutional Layers, 3 Fully Connected Layers.
- **Activations:** ReLU (might switch to tanh).
- **Weight Initialization:** Kaiming Uniform.
- **Optimizer:** Adam (lr=0.001).
- **Loss Function:** CrossEntropyLoss.
- **Dataset:** CIFAR-10.

## Usage

Load this model in PyTorch to fine-tune or evaluate on CIFAR-10 using your training and evaluation scripts.

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Feel free to update this model card with further training details, benchmarks, or usage examples.