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