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metadata
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - recall
  - f1
  - precision
model-index:
  - name: beit-base-patch16-224-pt22k-ft22k-finetuned-ind-17-imbalanced-aadhaarmask
    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: 0.8450404427415922
          - name: Recall
            type: recall
            value: 0.8450404427415922
          - name: F1
            type: f1
            value: 0.8442233792705293
          - name: Precision
            type: precision
            value: 0.8494143266059094

beit-base-patch16-224-pt22k-ft22k-finetuned-ind-17-imbalanced-aadhaarmask

This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3480
  • Accuracy: 0.8450
  • Recall: 0.8450
  • F1: 0.8442
  • Precision: 0.8494

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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy Recall F1 Precision
0.5859 0.9974 293 0.6117 0.8114 0.8114 0.7891 0.8139
0.5281 1.9983 587 0.4362 0.8442 0.8442 0.8375 0.8484
0.4214 2.9991 881 0.4228 0.8438 0.8438 0.8392 0.8529
0.4221 4.0 1175 0.4121 0.8382 0.8382 0.8331 0.8495
0.4127 4.9974 1468 0.3692 0.8476 0.8476 0.8454 0.8511
0.3122 5.9983 1762 0.3741 0.8408 0.8408 0.8394 0.8462
0.3079 6.9991 2056 0.3628 0.8429 0.8429 0.8403 0.8445
0.2851 8.0 2350 0.3635 0.8412 0.8412 0.8389 0.8412
0.297 8.9974 2643 0.3407 0.8510 0.8510 0.8497 0.8545
0.2109 9.9745 2930 0.3566 0.8421 0.8421 0.8406 0.8418

Framework versions

  • Transformers 4.40.1
  • Pytorch 2.2.0a0+81ea7a4
  • Datasets 2.19.0
  • Tokenizers 0.19.1