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emotion_classifier
IoriU/emotion_classifier
emotion_classifier is a image classification model from IoriU. 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
- Feb 11, 2024
- Updated
- Feb 11, 2024
- SHA
- 0c40a42d
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. -->
emotion_classifier
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.2783
- Accuracy: 0.5521
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: 1e-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: constant_with_warmup
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 35 | 2.0697 | 0.2014 |
| No log | 2.0 | 70 | 2.0539 | 0.1875 |
| No log | 3.0 | 105 | 2.0278 | 0.2014 |
| No log | 4.0 | 140 | 1.9869 | 0.2639 |
| No log | 5.0 | 175 | 1.9248 | 0.2986 |
| No log | 6.0 | 210 | 1.8172 | 0.3403 |
| No log | 7.0 | 245 | 1.7661 | 0.375 |
| No log | 8.0 | 280 | 1.6933 | 0.4306 |
| No log | 9.0 | 315 | 1.6493 | 0.4514 |
| No log | 10.0 | 350 | 1.6028 | 0.4514 |
| No log | 11.0 | 385 | 1.5580 | 0.4444 |
| No log | 12.0 | 420 | 1.5267 | 0.5 |
| No log | 13.0 | 455 | 1.4934 | 0.4861 |
| No log | 14.0 | 490 | 1.4605 | 0.5208 |
| 1.6139 | 15.0 | 525 | 1.4499 | 0.5278 |
| 1.6139 | 16.0 | 560 | 1.4228 | 0.5347 |
| 1.6139 | 17.0 | 595 | 1.4109 | 0.5208 |
| 1.6139 | 18.0 | 630 | 1.3872 | 0.5139 |
| 1.6139 | 19.0 | 665 | 1.3640 | 0.5556 |
| 1.6139 | 20.0 | 700 | 1.3787 | 0.5208 |
| 1.6139 | 21.0 | 735 | 1.3820 | 0.5278 |
| 1.6139 | 22.0 | 770 | 1.3649 | 0.5069 |
| 1.6139 | 23.0 | 805 | 1.3508 | 0.5347 |
| 1.6139 | 24.0 | 840 | 1.3322 | 0.5417 |
| 1.6139 | 25.0 | 875 | 1.3577 | 0.5347 |
| 1.6139 | 26.0 | 910 | 1.3337 | 0.5625 |
| 1.6139 | 27.0 | 945 | 1.3578 | 0.5139 |
| 1.6139 | 28.0 | 980 | 1.3256 | 0.5556 |
| 0.8303 | 29.0 | 1015 | 1.3139 | 0.5833 |
| 0.8303 | 30.0 | 1050 | 1.3575 | 0.5139 |
| 0.8303 | 31.0 | 1085 | 1.3214 | 0.5625 |
| 0.8303 | 32.0 | 1120 | 1.3185 | 0.5486 |
| 0.8303 | 33.0 | 1155 | 1.3285 | 0.5417 |
| 0.8303 | 34.0 | 1190 | 1.3259 | 0.5903 |
| 0.8303 | 35.0 | 1225 | 1.3492 | 0.5556 |
| 0.8303 | 36.0 | 1260 | 1.3164 | 0.5764 |
| 0.8303 | 37.0 | 1295 | 1.3645 | 0.5417 |
| 0.8303 | 38.0 | 1330 | 1.3431 | 0.5347 |
| 0.8303 | 39.0 | 1365 | 1.3272 | 0.5278 |
| 0.8303 | 40.0 | 1400 | 1.3326 | 0.5972 |
| 0.8303 | 41.0 | 1435 | 1.3375 | 0.5486 |
| 0.8303 | 42.0 | 1470 | 1.3641 | 0.5556 |
| 0.3516 | 43.0 | 1505 | 1.3633 | 0.5278 |
| 0.3516 | 44.0 | 1540 | 1.3532 | 0.5278 |
| 0.3516 | 45.0 | 1575 | 1.3473 | 0.5903 |
| 0.3516 | 46.0 | 1610 | 1.3413 | 0.5833 |
| 0.3516 | 47.0 | 1645 | 1.4158 | 0.5556 |
| 0.3516 | 48.0 | 1680 | 1.3747 | 0.5903 |
| 0.3516 | 49.0 | 1715 | 1.4364 | 0.5347 |
| 0.3516 | 50.0 | 1750 | 1.4659 | 0.5417 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Feb 11, 2024
- Updated
- Feb 11, 2024