metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- f1
- precision
- recall
model-index:
- name: ViT-threat-classification-v2
results: []
ViT-threat-classification-v2
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. This is model created as a prrof of concept for a Carleton University computer vision event. It is by no means meant to be used in deliverable systems in its current state, and should be used exclusively for research and development. It achieves the following results on the evaluation set:
- Loss: 0.0381
- F1: 0.9657
- Precision: 0.9563
- Recall: 0.9752
Model description
More information needed
Intended uses & limitations
More information needed
Collaborators
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: 2
- total_train_batch_size: 64
- 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_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
|---|---|---|---|---|---|---|
| 0.0744 | 0.9985 | 326 | 0.0576 | 0.9466 | 0.9738 | 0.9208 |
| 0.0449 | 2.0 | 653 | 0.0397 | 0.9641 | 0.9747 | 0.9538 |
| 0.0207 | 2.9985 | 979 | 0.0409 | 0.9647 | 0.9607 | 0.9686 |
| 0.0342 | 4.0 | 1306 | 0.0382 | 0.9650 | 0.9518 | 0.9785 |
| 0.0286 | 4.9923 | 1630 | 0.0381 | 0.9657 | 0.9563 | 0.9752 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3