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deepfake_vs_real_image_detection_v4 – AI Model by griseldans | AlphaNeural AI
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griseldans
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deepfake_vs_real_image_detection_v4
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transformers
safetensors
vit
image-classification
generated_from_trainer
dima806/deepfake_vs_real_image_detection
finetune
apache-2.0
endpoints_compatible
us
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deepfake_vs_real_image_detection_v4
This model is a fine-tuned version of
dima806/deepfake_vs_real_image_detection
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.2940
Accuracy: 0.9436
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: 2e-05
train_batch_size: 32
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
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_ratio: 0.05
num_epochs: 1
mixed_precision_training: Native AMP
label_smoothing_factor: 0.1
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
1.5002
0.3225
1000
0.3300
0.9178
1.3774
0.6450
2000
0.3153
0.9271
1.3372
0.9674
3000
0.2940
0.9436
Framework versions
Transformers 4.57.2
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1
