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scene_classification
hansin91/scene_classification
scene_classification is a image classification model from hansin91. 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
- Oct 2, 2023
- Updated
- Oct 3, 2023
- SHA
- f03a7653
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. -->
scene_classification
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the indoor-scene-classification dataset. It achieves the following results on the evaluation set:
- Loss: 0.6106
- Accuracy: 0.8492
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.3172 | 1.0 | 341 | 2.8572 | 0.5109 |
| 2.2254 | 2.0 | 682 | 2.1453 | 0.6329 |
| 1.6202 | 3.0 | 1023 | 1.6283 | 0.7336 |
| 1.2313 | 4.0 | 1364 | 1.3402 | 0.7599 |
| 0.9576 | 5.0 | 1705 | 1.1237 | 0.8010 |
| 0.7654 | 6.0 | 2046 | 1.0270 | 0.8023 |
| 0.6416 | 7.0 | 2387 | 0.8848 | 0.8171 |
| 0.5353 | 8.0 | 2728 | 0.8381 | 0.8087 |
| 0.4516 | 9.0 | 3069 | 0.7570 | 0.8254 |
| 0.3925 | 10.0 | 3410 | 0.6667 | 0.8524 |
| 0.3453 | 11.0 | 3751 | 0.7583 | 0.8164 |
| 0.2944 | 12.0 | 4092 | 0.6783 | 0.8350 |
| 0.294 | 13.0 | 4433 | 0.7128 | 0.8312 |
| 0.2507 | 14.0 | 4774 | 0.6632 | 0.8331 |
| 0.2355 | 15.0 | 5115 | 0.6730 | 0.8421 |
| 0.2267 | 16.0 | 5456 | 0.6572 | 0.8357 |
| 0.2032 | 17.0 | 5797 | 0.7058 | 0.8280 |
| 0.1908 | 18.0 | 6138 | 0.6374 | 0.8485 |
| 0.1857 | 19.0 | 6479 | 0.6831 | 0.8312 |
| 0.1727 | 20.0 | 6820 | 0.6961 | 0.8254 |
| 0.1692 | 21.0 | 7161 | 0.6306 | 0.8402 |
| 0.1642 | 22.0 | 7502 | 0.6291 | 0.8485 |
| 0.1618 | 23.0 | 7843 | 0.6058 | 0.8582 |
| 0.1593 | 24.0 | 8184 | 0.6780 | 0.8389 |
| 0.1399 | 25.0 | 8525 | 0.6330 | 0.8485 |
| 0.1373 | 26.0 | 8866 | 0.6550 | 0.8408 |
| 0.1334 | 27.0 | 9207 | 0.6857 | 0.8421 |
| 0.1388 | 28.0 | 9548 | 0.6338 | 0.8415 |
| 0.1423 | 29.0 | 9889 | 0.6272 | 0.8517 |
| 0.1288 | 30.0 | 10230 | 0.6409 | 0.8556 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Oct 2, 2023
- Updated
- Oct 3, 2023