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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: ViT-threat-classification-v2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ViT-threat-classification-v2 |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0381 |
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- F1: 0.9657 |
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- Precision: 0.9563 |
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- Recall: 0.9752 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Collaborators |
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[Angus Bailey](https://huggingface.co/boshy) |
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[Thomas Nolasque](https://github.com/thomasnol) |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:------:|:---------:|:------:| |
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| 0.0744 | 0.9985 | 326 | 0.0576 | 0.9466 | 0.9738 | 0.9208 | |
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| 0.0449 | 2.0 | 653 | 0.0397 | 0.9641 | 0.9747 | 0.9538 | |
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| 0.0207 | 2.9985 | 979 | 0.0409 | 0.9647 | 0.9607 | 0.9686 | |
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| 0.0342 | 4.0 | 1306 | 0.0382 | 0.9650 | 0.9518 | 0.9785 | |
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| 0.0286 | 4.9923 | 1630 | 0.0381 | 0.9657 | 0.9563 | 0.9752 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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