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vit-base-mnist
super-j/vit-base-mnist
vit-base-mnist is a image classification model from super-j. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- vit
- Access
- Public
- Created
- Nov 20, 2023
- Updated
- Nov 20, 2023
- SHA
- be6ae7c6
Base models
Datasets
Model card
From the Hugging Face model README
<!-- 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 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
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Nov 20, 2023
- Updated
- Nov 20, 2023