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vit-phishing-detector – AI Model by Sushant-Kumar-Sinha | AlphaNeural AI
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vit-phishing-detector
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transformers
safetensors
vit
image-classification
generated_from_trainer
google/vit-base-patch16-224
finetune
apache-2.0
endpoints_compatible
us
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vit-phishing-detector
This model is a fine-tuned version of
google/vit-base-patch16-224
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.5869
Accuracy: 0.7471
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: 16
eval_batch_size: 16
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
num_epochs: 5
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.5979
1.0
85
0.5644
0.7206
0.3707
2.0
170
0.5221
0.7265
0.3007
3.0
255
0.5498
0.7353
0.201
4.0
340
0.5815
0.7382
0.1896
5.0
425
0.5869
0.7471
Framework versions
Transformers 4.57.3
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1
