ViT-threat-classification

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on a threat classification dataset. This model was created for a Carleton University computer vision hacking event and serves as a proof of concept rather than complete model. It is trained on an extremely small and limited dataset and the performance is limited. It achieves the following results on the evaluation set:

  • Loss: 0.4568
  • Accuracy: 1.0

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: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • optimizer: Use OptimizerNames.ADAMW_TORCH 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: 500
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.328 0.9756 10 0.4556 0.875
0.3226 1.9512 20 0.4736 0.75
0.3619 2.9268 30 0.4568 1.0

Framework versions

  • Transformers 4.46.2
  • Pytorch 2.5.1+cu124
  • Datasets 3.1.0
  • Tokenizers 0.20.3
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