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emotion_classification_v1

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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emotion_classification_v1 / README.md
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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: emotion_classification_v1
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.575

emotion_classification_v1

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: 1.1905
  • Accuracy: 0.575

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: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 10 2.0278 0.2437
No log 2.0 20 1.8875 0.3875
No log 3.0 30 1.6890 0.4313
No log 4.0 40 1.5484 0.5
No log 5.0 50 1.4799 0.5125
No log 6.0 60 1.4148 0.5375
No log 7.0 70 1.3529 0.5375
No log 8.0 80 1.3120 0.5312
No log 9.0 90 1.2790 0.5813
No log 10.0 100 1.2498 0.575
No log 11.0 110 1.2610 0.525
No log 12.0 120 1.1896 0.5938
No log 13.0 130 1.2251 0.5312
No log 14.0 140 1.2019 0.575
No log 15.0 150 1.1797 0.5563
No log 16.0 160 1.2484 0.5437
No log 17.0 170 1.1766 0.5875
No log 18.0 180 1.2401 0.4938
No log 19.0 190 1.1977 0.5312
No log 20.0 200 1.1839 0.5875
No log 21.0 210 1.2028 0.5687
No log 22.0 220 1.2048 0.5625
No log 23.0 230 1.2637 0.5375
No log 24.0 240 1.2371 0.5375
No log 25.0 250 1.2777 0.5687
No log 26.0 260 1.2544 0.525
No log 27.0 270 1.2104 0.5625
No log 28.0 280 1.1372 0.5938
No log 29.0 290 1.2405 0.575
No log 30.0 300 1.1624 0.6062
No log 31.0 310 1.2376 0.5875
No log 32.0 320 1.1794 0.5875
No log 33.0 330 1.2156 0.5563
No log 34.0 340 1.1725 0.55
No log 35.0 350 1.2394 0.55
No log 36.0 360 1.1886 0.5938
No log 37.0 370 1.1760 0.6188
No log 38.0 380 1.2757 0.525
No log 39.0 390 1.1703 0.6062
No log 40.0 400 1.2734 0.575
No log 41.0 410 1.2265 0.5563
No log 42.0 420 1.2651 0.5687
No log 43.0 430 1.2419 0.5813
No log 44.0 440 1.1871 0.6
No log 45.0 450 1.2542 0.575
No log 46.0 460 1.1910 0.5813
No log 47.0 470 1.1990 0.6
No log 48.0 480 1.2097 0.5813
No log 49.0 490 1.2226 0.5875
0.699 50.0 500 1.2793 0.5375

Framework versions

  • Transformers 4.33.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3