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library_name: PyTorch |
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tags: |
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- cnn |
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- lenet |
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- cifar100 |
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- image-classification |
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datasets: |
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- uoft-cs/cifar100 |
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language: |
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- en |
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metrics: |
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- accuracy |
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--- |
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# CIFAR10 LeNet5 Variation 2: GELU + Dropout Layer |
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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. |
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## Model Details |
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- **Architecture:** 2 Convolutional Layers, 2 Fully Connected Layers, 1 Dropout Layer, 1 Final Fully Connected Layer. |
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- **Activations:** GELU. |
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- **Weight Initialization:** Kaiming Uniform. |
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- **Optimizer:** Adam (lr=0.001). |
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- **Loss Function:** CrossEntropyLoss. |
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- **Dataset:** CIFAR-100. |
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## Usage |
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Load this model in PyTorch to fine-tune or evaluate on CIFAR-100 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. |
