ViT-Classification-CIFAR10
Model Description
This model is a Vision Transformer (ViT) architecture trained on the CIFAR-10 dataset for image classification. It is trained from scratch without pre-training on a larger dataset.
Metrics:
- Test accuracy: 82.04%
- Test loss: 0.5560
Training Configuration
Hardware: NVIDIA RTX 3090
Training parameters:
- Epochs: 200
- Batch size: 2048
- Input size: 3x32x32
- Patch size: 4
- Sequence length: 8*8
- Embed size: 128
- Num of layers: 12
- Num of heads: 4
- Forward multiplier: 2
- Dropout: 0.1
- Optimizer: AdamW
Intended Uses & Limitations
This model is intended for practice purposes and exploration of ViT architectures on the CIFAR-10 dataset. It can be used for image classification tasks on similar datasets.
Limitations:
- This model is trained on a relatively small dataset (CIFAR-10) and might not generalize well to unseen data.
- Training is done without fine-tuning, potentially limiting its performance compared to a fine-tuned model.
- Training is performed on a single RTX 3090.
Training Data
The model is trained on the CIFAR-10 dataset, containing 60,000 32x32 color images in 10 classes.
- Training set: 50,000 images
- Test set: 10,000 images
Data Source: https://paperswithcode.com/dataset/cifar-10
Documentation
- GitHub Repository: ViT-Classification-CIFAR10
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