Deepfakes_detection – AI Model by sandesh2233 | AlphaNeural AI

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sandesh2233/
Deepfakes_detection

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image-classification

generated_from_trainer

google/vit-base-patch16-224

endpoints_compatible

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Deepfakes_detection

This model is a fine-tuned version of 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