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metadata
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: >-
      batch-size16_FFPP-raw_opencv-1FPS_faces-expand0-aligned_unaugmentation_seed-random_143_4090
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9873394745201253
          - name: Precision
            type: precision
            value: 0.9884711707303031
          - name: Recall
            type: recall
            value: 0.9954342532467533
          - name: F1
            type: f1
            value: 0.9919404925254568

batch-size16_FFPP-raw_opencv-1FPS_faces-expand0-aligned_unaugmentation_seed-random_143_4090

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0352
  • Accuracy: 0.9873
  • Precision: 0.9885
  • Recall: 0.9954
  • F1: 0.9919
  • Roc Auc: 0.9991

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: 1

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 Roc Auc
0.0441 0.9996 1377 0.0352 0.9873 0.9885 0.9954 0.9919 0.9991

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.1
  • Datasets 2.20.0
  • Tokenizers 0.19.1