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vit-Diatome
sgonzalezsilot/vit-Diatome
vit-Diatome is a image classification model from sgonzalezsilot. 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
- Mar 19, 2023
- Updated
- Mar 19, 2023
- SHA
- 77cf9e03
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-Diatome
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the Diatome dataset. It achieves the following results on the evaluation set:
- Loss: 0.2285
- Accuracy: 0.9429
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.2009 | 0.23 | 100 | 1.9938 | 0.6045 |
| 1.2823 | 0.47 | 200 | 1.2293 | 0.7327 |
| 0.7569 | 0.7 | 300 | 0.9534 | 0.7868 |
| 0.7428 | 0.93 | 400 | 0.7906 | 0.8078 |
| 0.4309 | 1.16 | 500 | 0.5759 | 0.8538 |
| 0.349 | 1.4 | 600 | 0.5070 | 0.8742 |
| 0.517 | 1.63 | 700 | 0.5048 | 0.8794 |
| 0.3667 | 1.86 | 800 | 0.5212 | 0.8596 |
| 0.169 | 2.09 | 900 | 0.4112 | 0.8888 |
| 0.1443 | 2.33 | 1000 | 0.3294 | 0.9109 |
| 0.1389 | 2.56 | 1100 | 0.3146 | 0.9190 |
| 0.142 | 2.79 | 1200 | 0.2994 | 0.9208 |
| 0.0921 | 3.02 | 1300 | 0.2620 | 0.9324 |
| 0.0768 | 3.26 | 1400 | 0.2516 | 0.9336 |
| 0.061 | 3.49 | 1500 | 0.2425 | 0.9388 |
| 0.0729 | 3.72 | 1600 | 0.2335 | 0.9418 |
| 0.0757 | 3.95 | 1700 | 0.2285 | 0.9429 |
Framework versions
- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Mar 19, 2023
- Updated
- Mar 19, 2023