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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-large-patch32-384-finetuned-melanoma
    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: 0.8272727272727273

vit-large-patch32-384-finetuned-melanoma

This model is a fine-tuned version of google/vit-large-patch32-384 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0767
  • Accuracy: 0.8273

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: 1e-05
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 4
  • 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
1.0081 1.0 550 0.7650 0.68
0.7527 2.0 1100 0.6693 0.7364
0.6234 3.0 1650 0.6127 0.7709
2.6284 4.0 2200 0.6788 0.7655
0.1406 5.0 2750 0.6657 0.7836
0.317 6.0 3300 0.6936 0.78
2.5358 7.0 3850 0.7104 0.7909
1.5802 8.0 4400 0.6928 0.8
0.088 9.0 4950 0.8060 0.7982
0.0183 10.0 5500 0.7811 0.8091
0.0074 11.0 6050 0.7185 0.7945
0.0448 12.0 6600 0.8780 0.7909
0.4288 13.0 7150 0.8229 0.82
0.017 14.0 7700 0.7516 0.8182
0.0057 15.0 8250 0.7974 0.7964
1.7571 16.0 8800 0.7866 0.8218
1.3159 17.0 9350 0.8491 0.8073
1.649 18.0 9900 0.8432 0.7891
0.0014 19.0 10450 0.8870 0.82
0.002 20.0 11000 0.9460 0.8236
0.3717 21.0 11550 0.8866 0.8327
0.0025 22.0 12100 1.0287 0.8073
0.0094 23.0 12650 0.9696 0.8091
0.002 24.0 13200 0.9659 0.8018
0.1001 25.0 13750 0.9712 0.8327
0.2953 26.0 14300 1.0512 0.8236
0.0141 27.0 14850 1.0503 0.82
0.612 28.0 15400 1.2020 0.8109
0.0792 29.0 15950 1.0498 0.8364
0.0117 30.0 16500 1.0079 0.8327
0.0568 31.0 17050 1.0199 0.8255
0.0001 32.0 17600 1.0319 0.8291
0.075 33.0 18150 1.0427 0.8382
0.001 34.0 18700 1.1289 0.8382
0.0001 35.0 19250 1.0589 0.8364
0.0006 36.0 19800 1.0349 0.8236
0.0023 37.0 20350 1.1192 0.8273
0.0002 38.0 20900 1.0863 0.8273
0.2031 39.0 21450 1.0604 0.8255
0.0006 40.0 22000 1.0767 0.8273

Framework versions

  • Transformers 4.24.0
  • Pytorch 1.12.1+cu113
  • Datasets 2.7.0
  • Tokenizers 0.13.2