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emotion_classification

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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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: 0.575

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: 1.2677
  • 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: 3e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 3
  • total_train_batch_size: 48
  • 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
0.9379 0.97 13 1.2947 0.4875
0.9235 1.95 26 1.3397 0.475
0.8298 3.0 40 1.2971 0.5563
0.8883 3.98 53 1.3434 0.4875
0.8547 4.95 66 1.3226 0.475
0.8129 6.0 80 1.3077 0.5062
0.8095 6.97 93 1.2503 0.525
0.7764 7.95 106 1.2989 0.5312
0.7004 9.0 120 1.3383 0.4813
0.7013 9.97 133 1.3370 0.5125
0.6416 10.95 146 1.3073 0.5125
0.5831 12.0 160 1.3192 0.5
0.5968 12.97 173 1.2394 0.5375
0.5434 13.95 186 1.3389 0.5188
0.4605 15.0 200 1.2951 0.525
0.4674 15.97 213 1.2038 0.5687
0.3953 16.95 226 1.4019 0.5062
0.3595 18.0 240 1.4442 0.4813
0.3619 18.98 253 1.4213 0.525
0.3304 19.95 266 1.2937 0.5437
0.34 21.0 280 1.3024 0.5687
0.4215 21.98 293 1.4018 0.5375
0.3606 22.95 306 1.4221 0.5375
0.3402 24.0 320 1.4987 0.4313
0.3058 24.98 333 1.5120 0.5125
0.3047 25.95 346 1.5749 0.5
0.3616 27.0 360 1.4293 0.5188
0.3315 27.98 373 1.5326 0.5312
0.3535 28.95 386 1.5095 0.5188
0.3056 29.25 390 1.5366 0.5

Framework versions

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