README.md

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-size-16_FFPP-Raw_1FPS_faces-expand-0-aligned_unaugmentation
    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: 0.9837432499886555
          - name: Precision
            type: precision
            value: 0.9830542407298831
          - name: Recall
            type: recall
            value: 0.9964053803339518
          - name: F1
            type: f1
            value: 0.9896847848777363

batch-size-16_FFPP-Raw_1FPS_faces-expand-0-aligned_unaugmentation

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: 0.0442
• Accuracy: 0.9837
• Precision: 0.9831
• Recall: 0.9964
• F1: 0.9897
• 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
0.0483	1.0	1377	0.0442	0.9837	0.9831	0.9964	0.9897	0.9991

Framework versions

• Transformers 4.39.2
• Pytorch 2.3.0
• Datasets 2.18.0
• Tokenizers 0.15.2