Instructions to use infinite000/roadwork_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use infinite000/roadwork_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="infinite000/roadwork_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("infinite000/roadwork_detection") model = AutoModelForImageClassification.from_pretrained("infinite000/roadwork_detection") - Notebooks
- Google Colab
- Kaggle
File size: 749 Bytes
531c8ec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"model_name": "roadwork_v3_focal",
"description": "Vision Transformer (ViT) model for roadwork detection. Trained with validator-matching augmentations, focal loss, label smoothing, and confidence margin loss.",
"version": "1.0.0",
"submitted_by": "unknown",
"submission_time": 1771555076,
"performance": {
"accuracy": 0.9839293798098687,
"mcc": 0.9443160344532321,
"f1": 0.9902806297056811,
"auc": 0.9949946578796627,
"subnet_reward": 0.8961834812863713
},
"training": {
"base_model": "google/vit-base-patch16-224-in21k",
"training_samples": 34968,
"validation_samples": 4418,
"validator_augmentations": true,
"label_smoothing": 0.05,
"confidence_margin": 0.1
}
} |