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:
- 17a50bbe9e1c298b9ea67e6053e0f414b02055348c294e352dc68014546437af
- Size of remote file:
- 4.28 kB
- SHA256:
- 88b8172e0349b2fd1495bde057d056293bcc19ddef1576d07159818d8c6b43da
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