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project_4_transfer_learning

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: project_4_transfer_learning
    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: 0.64375

project_4_transfer_learning

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: 1.1429
  • Accuracy: 0.6438

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: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 30

Training results

Training Loss Epoch Step Accuracy Validation Loss
2.0754 1.0 10 0.125 2.0725
2.0459 2.0 20 0.2625 2.0286
1.968 3.0 30 0.3 1.9506
1.8311 4.0 40 0.4188 1.8060
1.6911 5.0 50 0.4313 1.6814
1.5677 6.0 60 0.4313 1.5851
1.4801 7.0 70 0.4813 1.5169
1.4033 8.0 80 0.4813 1.4614
1.3435 9.0 90 0.475 1.4358
1.3054 10.0 100 0.525 1.4292
1.2532 11.0 110 0.5188 1.3942
1.2178 12.0 120 0.5312 1.3684
1.1857 13.0 130 0.5062 1.3599
1.1558 14.0 140 0.5312 1.2992
1.1118 15.0 150 0.5375 1.3217
1.0967 16.0 160 0.525 1.3177
1.0671 17.0 170 0.5312 1.3420
1.0635 18.0 180 0.5062 1.3319
1.044 19.0 190 0.5813 1.2977
1.037 20.0 200 0.5125 1.3127
1.0743 21.0 210 1.2062 0.6062
1.0454 22.0 220 1.1564 0.65
1.0457 23.0 230 1.1484 0.6312
1.0246 24.0 240 1.1470 0.6312
0.9859 25.0 250 1.1200 0.6438
0.9885 26.0 260 1.1331 0.6375
0.9823 27.0 270 1.1069 0.6562
0.9412 28.0 280 1.1163 0.6375
0.9172 29.0 290 1.1192 0.6375
0.9334 30.0 300 1.1573 0.6

Framework versions

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