batch-size16_FFPP-raw_opencv-1FPS_faces-expand0-aligned_unaugmentation_seeds-42_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: 0.0478
- Accuracy: 0.9826
- Precision: 0.9819
- Recall: 0.9961
- F1: 0.9889
- Roc Auc: 0.9989
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 |
---|---|---|---|---|---|---|---|---|
0.0428 | 0.9996 | 1377 | 0.0478 | 0.9826 | 0.9819 | 0.9961 | 0.9889 | 0.9989 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
- Downloads last month
- 3
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for hchcsuim/batch-size16_FFPP-raw_opencv-1FPS_faces-expand0-aligned_unaugmentation_seeds-42_143_4090
Base model
microsoft/swin-tiny-patch4-window7-224Evaluation results
- Accuracy on imagefoldertest set self-reported0.983
- Precision on imagefoldertest set self-reported0.982
- Recall on imagefoldertest set self-reported0.996
- F1 on imagefoldertest set self-reported0.989