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---
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-c23_ffmpeg-1FPS-qv1_faces-expand0-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: 0.9341693883006543
- name: Precision
type: precision
value: 0.9457024697784718
- name: Recall
type: recall
value: 0.9716386866735998
- name: F1
type: f1
value: 0.9584951563026688
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# batch-size16_FFPP-c23_ffmpeg-1FPS-qv1_faces-expand0-aligned_unaugmentation
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1590
- Accuracy: 0.9342
- Precision: 0.9457
- Recall: 0.9716
- F1: 0.9585
- Roc Auc: 0.9778
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.2014 | 1.0 | 1344 | 0.1590 | 0.9342 | 0.9457 | 0.9716 | 0.9585 | 0.9778 |
### Framework versions
- Transformers 4.39.2
- Pytorch 2.3.0
- Datasets 2.18.0
- Tokenizers 0.15.2
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