# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("EdBianchi/vit-fire-detection")
model = AutoModelForImageClassification.from_pretrained("EdBianchi/vit-fire-detection")Quick Links
vit-fire-detection
This model is a fine-tuned version of 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
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="EdBianchi/vit-fire-detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")