File size: 1,989 Bytes
1cd02c6 | 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 | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Deepfakes_detection
results: []
---
<!-- 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. -->
# Deepfakes_detection
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3242
- Accuracy: 0.9222
- Auc: 0.9998
- F1 Fake: 0.9278
## 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: 256
- eval_batch_size: 512
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | F1 Fake |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:|
| No log | 1.0 | 11 | 0.3630 | 0.8579 | 0.9527 | 0.8454 |
| No log | 2.0 | 22 | 0.2680 | 0.9114 | 0.9863 | 0.9166 |
| No log | 3.0 | 33 | 0.3072 | 0.9123 | 0.9879 | 0.9178 |
| No log | 4.0 | 44 | 0.2917 | 0.914 | 0.988 | 0.9193 |
| 0.0568 | 5.0 | 55 | 0.2840 | 0.9132 | 0.988 | 0.9182 |
### Framework versions
- Transformers 5.5.4
- Pytorch 2.11.0+cu130
- Datasets 4.8.4
- Tokenizers 0.22.2
|