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hansin91
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emotion_classification

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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emotion_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:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: emotion_classification
    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: 0.58125

emotion_classification

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: 1.4116
  • Accuracy: 0.5813

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
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 10 1.3914 0.5312
No log 2.0 20 1.3253 0.4875
No log 3.0 30 1.4217 0.4813
No log 4.0 40 1.3711 0.5062
No log 5.0 50 1.3584 0.5
No log 6.0 60 1.3163 0.5
No log 7.0 70 1.3824 0.5188
No log 8.0 80 1.3882 0.525
No log 9.0 90 1.4126 0.5188
No log 10.0 100 1.3213 0.5625
No log 11.0 110 1.4385 0.5
No log 12.0 120 1.3729 0.525
No log 13.0 130 1.4603 0.4938
No log 14.0 140 1.5326 0.4688
No log 15.0 150 1.3687 0.5563
No log 16.0 160 1.4537 0.55
No log 17.0 170 1.5377 0.5188
No log 18.0 180 1.6417 0.4688
No log 19.0 190 1.5260 0.55
No log 20.0 200 1.6854 0.4938
No log 21.0 210 1.6457 0.5062
No log 22.0 220 1.5855 0.5125
No log 23.0 230 1.5083 0.5312
No log 24.0 240 1.5656 0.525
No log 25.0 250 1.5931 0.5125
No log 26.0 260 1.4351 0.5687
No log 27.0 270 1.5031 0.525
No log 28.0 280 1.4129 0.55
No log 29.0 290 1.5323 0.5125
No log 30.0 300 1.5217 0.5625

Framework versions

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