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ViT-Emotion-Classifier

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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: ViT-Emotion-Classifier
    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

ViT-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: 1.3652
  • 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: 16
  • eval_batch_size: 16
  • 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
No log 1.0 40 1.8992 0.3312
No log 2.0 80 1.5939 0.4062
No log 3.0 120 1.4776 0.4688
No log 4.0 160 1.4012 0.4813
No log 5.0 200 1.3471 0.4875
No log 6.0 240 1.2877 0.5375
No log 7.0 280 1.2598 0.575
No log 8.0 320 1.3595 0.4938
No log 9.0 360 1.2825 0.5375
No log 10.0 400 1.3291 0.5062
No log 11.0 440 1.2422 0.5563
No log 12.0 480 1.2659 0.575
1.0646 13.0 520 1.3048 0.5062
1.0646 14.0 560 1.2993 0.5563
1.0646 15.0 600 1.2935 0.5563
1.0646 16.0 640 1.3589 0.5437
1.0646 17.0 680 1.2447 0.5938
1.0646 18.0 720 1.3298 0.5563
1.0646 19.0 760 1.2829 0.6
1.0646 20.0 800 1.3092 0.5813
1.0646 21.0 840 1.2895 0.5875
1.0646 22.0 880 1.3810 0.5625
1.0646 23.0 920 1.3833 0.5563
1.0646 24.0 960 1.4841 0.5312
0.3074 25.0 1000 1.3619 0.6062
0.3074 26.0 1040 1.3776 0.5563
0.3074 27.0 1080 1.3917 0.5875
0.3074 28.0 1120 1.3585 0.575
0.3074 29.0 1160 1.3455 0.5625
0.3074 30.0 1200 1.4409 0.5813

Framework versions

  • Transformers 4.41.1
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1