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my_awesome_pokemon_model
jroberts/my_awesome_pokemon_model
my_awesome_pokemon_model is a image classification model from jroberts. 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
- May 18, 2023
- Updated
- May 19, 2023
- SHA
- d0edbe0b
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. -->
my_awesome_pokemon_model
This model is a fine-tuned version of google/vit-base-patch16-224 on the pokemon-classification dataset. It achieves the following results on the evaluation set:
- Loss: 7.3838
- Accuracy: 0.0755
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 4.926 | 1.0 | 76 | 5.4705 | 0.0007 |
| 3.7521 | 1.99 | 152 | 5.9651 | 0.0129 |
| 1.9692 | 2.99 | 228 | 5.8631 | 0.0144 |
| 0.7605 | 4.0 | 305 | 5.9688 | 0.0482 |
| 0.4163 | 5.0 | 381 | 6.1329 | 0.0655 |
| 0.3085 | 5.99 | 457 | 6.2311 | 0.0806 |
| 0.2155 | 6.99 | 533 | 6.4040 | 0.0683 |
| 0.2188 | 8.0 | 610 | 6.4869 | 0.0748 |
| 0.2241 | 9.0 | 686 | 6.6527 | 0.0763 |
| 0.1505 | 9.99 | 762 | 6.7076 | 0.0755 |
| 0.1429 | 10.99 | 838 | 6.7627 | 0.0719 |
| 0.1378 | 12.0 | 915 | 6.8740 | 0.0712 |
| 0.1335 | 13.0 | 991 | 6.9456 | 0.0741 |
| 0.1335 | 13.99 | 1067 | 6.8821 | 0.0748 |
| 0.1131 | 14.99 | 1143 | 6.9655 | 0.0763 |
| 0.1041 | 16.0 | 1220 | 7.0660 | 0.0763 |
| 0.0844 | 17.0 | 1296 | 7.1479 | 0.0770 |
| 0.086 | 17.99 | 1372 | 7.1182 | 0.0748 |
| 0.1028 | 18.99 | 1448 | 7.1395 | 0.0734 |
| 0.0456 | 20.0 | 1525 | 7.2099 | 0.0748 |
| 0.0617 | 21.0 | 1601 | 7.2512 | 0.0734 |
| 0.0711 | 21.99 | 1677 | 7.3157 | 0.0813 |
| 0.0623 | 22.99 | 1753 | 7.2590 | 0.0791 |
| 0.0419 | 24.0 | 1830 | 7.3413 | 0.0712 |
| 0.0924 | 25.0 | 1906 | 7.3051 | 0.0784 |
| 0.0471 | 25.99 | 1982 | 7.3136 | 0.0763 |
| 0.0654 | 26.99 | 2058 | 7.3667 | 0.0734 |
| 0.0836 | 28.0 | 2135 | 7.4039 | 0.0770 |
| 0.06 | 29.0 | 2211 | 7.3998 | 0.0799 |
| 0.0694 | 29.9 | 2280 | 7.3838 | 0.0755 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- May 18, 2023
- Updated
- May 19, 2023