# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("dima806/wildfire_types_image_detection")
model = AutoModelForImageClassification.from_pretrained("dima806/wildfire_types_image_detection")Quick Links
Returns wildfire type given an image with about 90% accuracy.
See https://www.kaggle.com/code/dima806/wildfire-image-detection-vit for more details.
Classification report:
precision recall f1-score support
Both_smoke_and_fire 0.9623 0.9091 0.9350 253
Fire_confounding_elements 0.9306 0.8976 0.9138 254
Forested_areas_without_confounding_elements 0.9215 0.8780 0.8992 254
Smoke_confounding_elements 0.8370 0.8898 0.8626 254
Smoke_from_fires 0.8755 0.9409 0.9070 254
accuracy 0.9031 1269
macro avg 0.9054 0.9031 0.9035 1269
weighted avg 0.9053 0.9031 0.9035 1269
- Downloads last month
- 6
Model tree for dima806/wildfire_types_image_detection
Base model
google/vit-base-patch16-224-in21k
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/wildfire_types_image_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")