File size: 1,864 Bytes
f2c34c4 30ab497 f2c34c4 30ab497 f2c34c4 30ab497 f2c34c4 30ab497 f2c34c4 30ab497 f2c34c4 5427f56 f2c34c4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
---
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: realFake-food
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: 0.8013698630136986
---
<!-- 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. -->
# realFake-food
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: 0.4344
- Accuracy: 0.8014
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.3941 | 1.9231 | 100 | 0.4344 | 0.8014 |
| 0.2366 | 3.8462 | 200 | 0.4853 | 0.8630 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|