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DeepFake-image-detection-ViT-384 – AI Model by Skullly | AlphaNeural AI
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DeepFake-image-detection-ViT-384
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transformers
safetensors
vit
image-classification
generated_from_trainer
google/vit-base-patch16-384
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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DeepFake-image-detection-ViT-384
This model is a fine-tuned version of
google/vit-base-patch16-384
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.0272
Accuracy: 0.9911
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: 3e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2.5
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.0037
0.9984
546
0.0272
0.9911
0.0006
1.9986
1093
0.1121
0.9644
0.0002
2.496
1365
0.1357
0.9582
Framework versions
Transformers 4.41.2
Pytorch 2.1.2
Datasets 2.19.2
Tokenizers 0.19.1
