| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - precision |
| | - recall |
| | model-index: |
| | - name: vit-fire-detection |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # vit-fire-detection |
| |
|
| | This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0126 |
| | - Precision: 0.9960 |
| | - Recall: 0.9960 |
| |
|
| | ## 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: 0.0002 |
| | - train_batch_size: 32 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 100 |
| | - num_epochs: 10 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| |
| | | 0.1018 | 1.0 | 190 | 0.0375 | 0.9934 | 0.9934 | |
| | | 0.0484 | 2.0 | 380 | 0.0167 | 0.9961 | 0.9960 | |
| | | 0.0357 | 3.0 | 570 | 0.0253 | 0.9948 | 0.9947 | |
| | | 0.0133 | 4.0 | 760 | 0.0198 | 0.9961 | 0.9960 | |
| | | 0.012 | 5.0 | 950 | 0.0203 | 0.9947 | 0.9947 | |
| | | 0.0139 | 6.0 | 1140 | 0.0204 | 0.9947 | 0.9947 | |
| | | 0.0076 | 7.0 | 1330 | 0.0175 | 0.9961 | 0.9960 | |
| | | 0.0098 | 8.0 | 1520 | 0.0115 | 0.9974 | 0.9974 | |
| | | 0.0062 | 9.0 | 1710 | 0.0133 | 0.9960 | 0.9960 | |
| | | 0.0012 | 10.0 | 1900 | 0.0126 | 0.9960 | 0.9960 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.25.1 |
| | - Pytorch 1.14.0.dev20221111 |
| | - Datasets 2.8.0 |
| | - Tokenizers 0.12.1 |
| | |
