|
---
|
|
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_DFDC_opencv-1FPS_faces-expand20-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: 0.9794553960440362
|
|
- name: Precision
|
|
type: precision
|
|
value: 0.9849190255339879
|
|
- name: Recall
|
|
type: recall
|
|
value: 0.9907155000951158
|
|
- name: F1
|
|
type: f1
|
|
value: 0.9878087594408504
|
|
---
|
|
|
|
<!-- 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_DFDC_opencv-1FPS_faces-expand20-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: 0.0532
|
|
- Accuracy: 0.9795
|
|
- Precision: 0.9849
|
|
- Recall: 0.9907
|
|
- F1: 0.9878
|
|
- Roc Auc: 0.9964
|
|
|
|
## 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 |
|
|
|:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
|
|
| 0.0543 | 1.0000 | 18673 | 0.0532 | 0.9795 | 0.9849 | 0.9907 | 0.9878 | 0.9964 |
|
|
|
|
|
|
### Framework versions
|
|
|
|
- Transformers 4.41.2
|
|
- Pytorch 2.3.1
|
|
- Datasets 2.20.0
|
|
- Tokenizers 0.19.1
|
|
|
