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face_predict

Image Classification
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vit
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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: face_predict
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train[:800]
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.5625

face_predict

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: 1.2322
  • Accuracy: 0.5625

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: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 6
  • total_train_batch_size: 192
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 40

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.9 3 2.0747 0.1187
No log 1.8 6 2.0728 0.1375
2.0713 3.0 10 2.0449 0.2
2.0713 3.9 13 2.0225 0.2562
2.0713 4.8 16 1.9779 0.2938
1.9642 6.0 20 1.8985 0.3688
1.9642 6.9 23 1.8440 0.4188
1.9642 7.8 26 1.7593 0.4437
1.7442 9.0 30 1.6551 0.4875
1.7442 9.9 33 1.5996 0.4875
1.7442 10.8 36 1.5324 0.5188
1.5402 12.0 40 1.5053 0.525
1.5402 12.9 43 1.4543 0.5188
1.5402 13.8 46 1.4335 0.5188
1.4064 15.0 50 1.3768 0.5938
1.4064 15.9 53 1.3583 0.6
1.4064 16.8 56 1.3464 0.575
1.2844 18.0 60 1.3245 0.6125
1.2844 18.9 63 1.3265 0.5563
1.2844 19.8 66 1.2899 0.5813
1.1834 21.0 70 1.2863 0.5625
1.1834 21.9 73 1.2939 0.5687
1.1834 22.8 76 1.2508 0.5938
1.1046 24.0 80 1.2604 0.5563
1.1046 24.9 83 1.2344 0.6062
1.1046 25.8 86 1.2124 0.6125
1.0379 27.0 90 1.2053 0.6312
1.0379 27.9 93 1.3067 0.5375
1.0379 28.8 96 1.2247 0.5875
1.0064 30.0 100 1.2060 0.625
1.0064 30.9 103 1.2308 0.575
1.0064 31.8 106 1.1936 0.6188
0.9611 33.0 110 1.2257 0.5938
0.9611 33.9 113 1.2302 0.5563
0.9611 34.8 116 1.2172 0.6
0.9351 36.0 120 1.2355 0.55

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1