Instructions to use varcoder/resnet-101-CivilEng11k_3Classes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use varcoder/resnet-101-CivilEng11k_3Classes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="varcoder/resnet-101-CivilEng11k_3Classes") 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("varcoder/resnet-101-CivilEng11k_3Classes") model = AutoModelForImageClassification.from_pretrained("varcoder/resnet-101-CivilEng11k_3Classes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 644ca3d26e0cfdd74adc4b8b88ec89df00e2b849c295a631e9dcda380465a801
- Size of remote file:
- 171 MB
- SHA256:
- d401a460d3f7ca3dabe1b8e71261f8d9a8786b64fe7fa7fbc9bd27cba572e339
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