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---
license: apache-2.0
base_model: dima806/deepfake_vs_real_image_detection
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: finetuned-aiimg
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: ai_real_images
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7808219178082192
---
<!-- 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. -->
# finetuned-aiimg
This model is a fine-tuned version of [dima806/deepfake_vs_real_image_detection](https://huggingface.co/dima806/deepfake_vs_real_image_detection) on the ai_real_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5027
- Accuracy: 0.7808
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.4149 | 1.9231 | 100 | 0.5027 | 0.7808 |
| 0.1273 | 3.8462 | 200 | 0.5272 | 0.8219 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|