CIFAR10 LeNet5 Variation 1: GELU

This repository contains a variation of the original LeNet5 architecture adapted for CIFAR-10. The model consists of two convolutional layers followed by three fully connected layers, using linear (GELU) 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 0.0623 and a top-1 accuracy of 59.51% on CIFAR-10.

Model Details

  • Architecture: 2 Convolutional Layers, 3 Fully Connected Layers.
  • Activations: GELU.
  • 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.


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

Downloads last month

-

Downloads are not tracked for this model. How to track
Inference Providers NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API: The HF Inference API does not support image-classification models for PyTorch library.

Dataset used to train Juardo/uu-infomcv-assignment-3-CIFAR10_model1

Collection including Juardo/uu-infomcv-assignment-3-CIFAR10_model1