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license: mit |
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tags: |
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- vision-transformer |
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- ViT |
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- classification |
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- cifar10 |
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- computer-vision |
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- deep-learning |
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- machine-learning |
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--- |
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# ViT-Classification-CIFAR10 |
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## Model Description |
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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. |
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**Metrics:** |
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* Test accuracy: 78.31% |
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* Test loss: 0.6296 |
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## Training Configuration |
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**Hardware:** NVIDIA RTX 3090 |
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**Training parameters:** |
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* Epochs: 200 |
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* Batch size: 4096 |
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* Input size: 3x32x32 |
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* Patch size: 4 |
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* Sequence length: 8*8 |
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* Embed size: 128 |
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* Num of layers: 6 |
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* Num of heads: 4 |
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* Forward multiplier: 2 |
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* Dropout: 0.1 |
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* Optimizer: AdamW |
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## Intended Uses & Limitations |
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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. |
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**Limitations:** |
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* This model is trained on a relatively small dataset (CIFAR-10) and might not generalize well to unseen data. |
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* Training is done without fine-tuning, potentially limiting its performance compared to a fine-tuned model. |
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* Training is performed on a single RTX 3090. |
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## Training Data |
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The model is trained on the CIFAR-10 dataset, containing 60,000 32x32 color images in 10 classes. |
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* Training set: 50,000 images |
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* Test set: 10,000 images |
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**Data Source:** [https://paperswithcode.com/dataset/cifar-10](https://paperswithcode.com/dataset/cifar-10) |
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## Documentation |
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* GitHub Repository: [ViT-Classification-CIFAR10](https://github.com/nick8592/ViT-Classification-CIFAR10.git) |
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