Instructions to use Taki3d/CrackDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taki3d/CrackDetection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Taki3d/CrackDetection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Taki3d/CrackDetection") model = AutoModelForImageClassification.from_pretrained("Taki3d/CrackDetection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Taki3d/CrackDetection: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/Taki3d/CrackDetection/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Taki3d/CrackDetection/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Taki3d/CrackDetection/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 57c6103d88638c8468aaa1cd3ebebce3e37fbbedbeb15a272a8c1b77cba65001
- Size of remote file:
- 343 MB
- SHA256:
- e536d29cff5094baf39867e5f0339c755c146175037cef6e95f4a8f4bccc0cab
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