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deepfake_vs_real_image_detection – AI Model by griseldans | AlphaNeural AI
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griseldans
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deepfake_vs_real_image_detection
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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
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:
eval_loss: 1.6228
eval_model_preparation_time: 0.0025
eval_accuracy: 0.6675
eval_runtime: 6.887
eval_samples_per_second: 58.08
eval_steps_per_second: 7.26
step: 0
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: 1e-06
train_batch_size: 32
eval_batch_size: 8
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: 50
num_epochs: 5
Framework versions
Transformers 4.57.2
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1
