Instructions to use beingamit99/car_damage_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beingamit99/car_damage_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="beingamit99/car_damage_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("beingamit99/car_damage_detection") model = AutoModelForImageClassification.from_pretrained("beingamit99/car_damage_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a190fcf7e2d3f865904522028552798263927ac46c419f34a2d2d81e7619fed5
- Size of remote file:
- 4.54 kB
- SHA256:
- 734e5209a48a43b24ee02b950c90e63f9535ed00aa0b9cc5a2f7094feba15585
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.