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scene_classification

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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scene_classification / README.md
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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - indoor-scene-classification
metrics:
  - accuracy
model-index:
  - name: scene_classification
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: indoor-scene-classification
          type: indoor-scene-classification
          config: full
          split: test
          args: full
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8491655969191271

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