Instructions to use Sushant-Kumar-Sinha/vit-phishing-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sushant-Kumar-Sinha/vit-phishing-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sushant-Kumar-Sinha/vit-phishing-detector") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Sushant-Kumar-Sinha/vit-phishing-detector") model = AutoModelForImageClassification.from_pretrained("Sushant-Kumar-Sinha/vit-phishing-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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
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Model tree for Sushant-Kumar-Sinha/vit-phishing-detector
Base model
google/vit-base-patch16-224