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distill-vit
geshijoker/distill-vit
distill-vit is a image classification model from geshijoker. Use it when you need a label for an image. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Classification
- Library
- transformers
- Model type
- mobilenet_v2
- Access
- Public
- Created
- Aug 30, 2024
- Updated
- Aug 30, 2024
- SHA
- b4607fcc
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. -->
distill-vit
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4751
- Accuracy: 0.7656
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8986 | 1.0 | 65 | 0.7788 | 0.4286 |
| 0.8556 | 2.0 | 130 | 0.9774 | 0.4812 |
| 0.7581 | 3.0 | 195 | 0.6150 | 0.6541 |
| 0.6434 | 4.0 | 260 | 0.6455 | 0.6090 |
| 0.609 | 5.0 | 325 | 0.5329 | 0.7143 |
| 0.5503 | 6.0 | 390 | 0.5829 | 0.6466 |
| 0.5492 | 7.0 | 455 | 0.6716 | 0.6917 |
| 0.504 | 8.0 | 520 | 0.5342 | 0.6917 |
| 0.4966 | 9.0 | 585 | 0.5668 | 0.6617 |
| 0.4978 | 10.0 | 650 | 0.5347 | 0.6767 |
| 0.4535 | 11.0 | 715 | 0.5580 | 0.6090 |
| 0.4415 | 12.0 | 780 | 0.5085 | 0.7444 |
| 0.4308 | 13.0 | 845 | 0.5131 | 0.7068 |
| 0.4247 | 14.0 | 910 | 0.4808 | 0.7068 |
| 0.4307 | 15.0 | 975 | 0.5542 | 0.6917 |
| 0.4165 | 16.0 | 1040 | 0.5410 | 0.6992 |
| 0.3975 | 17.0 | 1105 | 0.5944 | 0.6015 |
| 0.3942 | 18.0 | 1170 | 0.4730 | 0.6917 |
| 0.3932 | 19.0 | 1235 | 0.4806 | 0.6917 |
| 0.3437 | 20.0 | 1300 | 0.5341 | 0.6842 |
| 0.3628 | 21.0 | 1365 | 0.5836 | 0.6692 |
| 0.3483 | 22.0 | 1430 | 0.6234 | 0.6316 |
| 0.3318 | 23.0 | 1495 | 0.4950 | 0.7143 |
| 0.3189 | 24.0 | 1560 | 0.4590 | 0.7068 |
| 0.3243 | 25.0 | 1625 | 0.5789 | 0.6692 |
| 0.3169 | 26.0 | 1690 | 0.5702 | 0.7218 |
| 0.3031 | 27.0 | 1755 | 0.4415 | 0.7519 |
| 0.2928 | 28.0 | 1820 | 0.4680 | 0.7368 |
| 0.3117 | 29.0 | 1885 | 0.5384 | 0.6842 |
| 0.3127 | 30.0 | 1950 | 0.5148 | 0.6767 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.2.1+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
- Task
- Image Classification
- Library
- transformers
- Type
- mobilenet_v2
- Created
- Aug 30, 2024
- Updated
- Aug 30, 2024