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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- mnist |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-base-mnist |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: mnist |
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type: mnist |
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config: mnist |
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split: train |
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args: mnist |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9948888888888889 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# vit-base-mnist |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the mnist dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0236 |
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- Accuracy: 0.9949 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 1337 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.3717 | 1.0 | 6375 | 0.0522 | 0.9893 | |
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| 0.3453 | 2.0 | 12750 | 0.0370 | 0.9906 | |
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| 0.3736 | 3.0 | 19125 | 0.0308 | 0.9916 | |
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| 0.3224 | 4.0 | 25500 | 0.0269 | 0.9939 | |
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| 0.2846 | 5.0 | 31875 | 0.0236 | 0.9949 | |
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### Framework versions |
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- Transformers 4.22.0.dev0 |
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- Pytorch 1.11.0a0+17540c5 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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