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RickyIG
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emotion_face_image_classification

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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emotion_face_image_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_face_image_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: 0.55

emotion_face_image_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: 1.2556
  • Accuracy: 0.55

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 2.0561 0.1437
No log 2.0 20 1.9946 0.325
No log 3.0 30 1.9059 0.3563
No log 4.0 40 1.7640 0.3937
No log 5.0 50 1.6329 0.3812
No log 6.0 60 1.5935 0.375
No log 7.0 70 1.5206 0.4313
No log 8.0 80 1.4580 0.4813
No log 9.0 90 1.4422 0.4938
No log 10.0 100 1.4297 0.4437
No log 11.0 110 1.4020 0.5062
No log 12.0 120 1.3344 0.5125
No log 13.0 130 1.3541 0.5125
No log 14.0 140 1.2990 0.5125
No log 15.0 150 1.2482 0.5687
No log 16.0 160 1.2726 0.4875
No log 17.0 170 1.2523 0.5375
No log 18.0 180 1.2857 0.4813
No log 19.0 190 1.2476 0.5375
No log 20.0 200 1.2991 0.4625
No log 21.0 210 1.3108 0.5062
No log 22.0 220 1.3052 0.5
No log 23.0 230 1.2606 0.4813
No log 24.0 240 1.1568 0.5687
No log 25.0 250 1.2242 0.5437
No log 26.0 260 1.2031 0.5875
No log 27.0 270 1.1649 0.5625
No log 28.0 280 1.3134 0.4938
No log 29.0 290 1.2542 0.5062
No log 30.0 300 1.1542 0.5687
No log 31.0 310 1.1619 0.5625
No log 32.0 320 1.2700 0.5
No log 33.0 330 1.1943 0.55
No log 34.0 340 1.1441 0.575
No log 35.0 350 1.2405 0.5687
No log 36.0 360 1.2228 0.5375
No log 37.0 370 1.1598 0.6
No log 38.0 380 1.1894 0.5687
No log 39.0 390 1.2493 0.5188
No log 40.0 400 1.1716 0.5687
No log 41.0 410 1.1558 0.5813
No log 42.0 420 1.2529 0.5312
No log 43.0 430 1.2515 0.5625
No log 44.0 440 1.2576 0.5188
No log 45.0 450 1.1793 0.5687
No log 46.0 460 1.2252 0.5625
No log 47.0 470 1.1876 0.575
No log 48.0 480 1.2288 0.5375
No log 49.0 490 1.2623 0.5125
0.989 50.0 500 1.1603 0.5875

Framework versions

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