Instructions to use FavoritCoin/streetvision-roadwork with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FavoritCoin/streetvision-roadwork with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="FavoritCoin/streetvision-roadwork") 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("FavoritCoin/streetvision-roadwork") model = AutoModelForImageClassification.from_pretrained("FavoritCoin/streetvision-roadwork", device_map="auto") - Notebooks
- Google Colab
- Kaggle
StreetVision Roadwork
Binary image classifier prepared for NATIX StreetVision subnet inference.
Labels
0:None1:Roadwork
Current status
This repository currently contains a baseline copied from reyraa/sn72-roadwork. It has not yet been fine-tuned by FavoritCoin and no accuracy claim is made until evaluation on a labeled roadwork/no-roadwork benchmark is complete.
Verified runtime
Tested with StreetVision commit 5d0756bfb5c827929b02d66446b261fcb14c063a on NVIDIA RTX 4070 Ti.
- Output is a probability in the
0.0-1.0range. - Model loads through
AutoModelForImageClassification. - GPU inference is approximately
4.13 ms/imageafter warmup. - GPU memory allocation is approximately
336 MiB.
Files
config.jsonpreprocessor_config.jsonmodel.safetensors
Limitations
Training data provenance and benchmark accuracy need independent validation before production mining or registration.
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