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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_face_image_classification_v2
    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: 0.48125

emotion_face_image_classification_v2

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: 1.5157
  • Accuracy: 0.4813

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
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine_with_restarts
  • lr_scheduler_warmup_ratio: 0.1
  • lr_scheduler_warmup_steps: 150
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.8 2 2.0924 0.15
No log 2.0 5 2.1024 0.0938
No log 2.8 7 2.0935 0.1375
No log 4.0 10 2.0893 0.15
No log 4.8 12 2.0900 0.15
No log 6.0 15 2.0987 0.0813
No log 6.8 17 2.0901 0.1
No log 8.0 20 2.0872 0.15
No log 8.8 22 2.0831 0.1375
No log 10.0 25 2.0750 0.1437
No log 10.8 27 2.0744 0.175
No log 12.0 30 2.0778 0.1437
No log 12.8 32 2.0729 0.1812
No log 14.0 35 2.0676 0.1625
No log 14.8 37 2.0694 0.1688
No log 16.0 40 2.0562 0.1625
No log 16.8 42 2.0498 0.1938
No log 18.0 45 2.0393 0.2188
No log 18.8 47 2.0458 0.2062
No log 20.0 50 2.0289 0.2125
No log 20.8 52 2.0226 0.2437
No log 22.0 55 1.9997 0.2625
No log 22.8 57 1.9855 0.3187
No log 24.0 60 1.9571 0.3187
No log 24.8 62 1.9473 0.3375
No log 26.0 65 1.9080 0.3187
No log 26.8 67 1.8894 0.35
No log 28.0 70 1.8407 0.375
No log 28.8 72 1.8083 0.3438
No log 30.0 75 1.7652 0.3563
No log 30.8 77 1.7281 0.3563
No log 32.0 80 1.6729 0.4062
No log 32.8 82 1.6527 0.3937
No log 34.0 85 1.6044 0.4562
No log 34.8 87 1.5899 0.4313
No log 36.0 90 1.5488 0.4313
No log 36.8 92 1.5340 0.45
No log 38.0 95 1.5227 0.4875
No log 38.8 97 1.4846 0.4875
No log 40.0 100 1.4579 0.4688

Framework versions

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