| | --- |
| | license: apache-2.0 |
| | base_model: google/vit-base-patch16-224-in21k |
| | tags: |
| | - image-classification |
| | - vision |
| | - generated_from_trainer |
| | datasets: |
| | - mnist |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: vit-base-mnist |
| | results: |
| | - task: |
| | name: Image Classification |
| | type: image-classification |
| | dataset: |
| | name: mnist |
| | type: mnist |
| | config: mnist |
| | split: train |
| | args: mnist |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9948888888888889 |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # vit-base-mnist |
| |
|
| | 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. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0247 |
| | - Accuracy: 0.9949 |
| |
|
| | ## Model description |
| |
|
| | More information needed |
| |
|
| | ## Intended uses & limitations |
| |
|
| | More information needed |
| |
|
| | ## Training and evaluation data |
| |
|
| | More information needed |
| |
|
| | ## Training procedure |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 2e-05 |
| | - train_batch_size: 8 |
| | - eval_batch_size: 8 |
| | - seed: 1337 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 5.0 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| | |:-------------:|:-----:|:-----:|:---------------:|:--------:| |
| | | 0.3215 | 1.0 | 6375 | 0.0630 | 0.9856 | |
| | | 0.4689 | 2.0 | 12750 | 0.0377 | 0.9906 | |
| | | 0.3258 | 3.0 | 19125 | 0.0364 | 0.9908 | |
| | | 0.3094 | 4.0 | 25500 | 0.0269 | 0.9936 | |
| | | 0.2981 | 5.0 | 31875 | 0.0247 | 0.9949 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.36.0.dev0 |
| | - Pytorch 2.1.0 |
| | - Datasets 2.14.6 |
| | - Tokenizers 0.14.1 |
| |
|
