Instructions to use Arm627/WaspImageRecComap with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arm627/WaspImageRecComap with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Arm627/WaspImageRecComap") 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("Arm627/WaspImageRecComap") model = AutoModelForImageClassification.from_pretrained("Arm627/WaspImageRecComap", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Arm627/WaspImageRecComap: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/Arm627/WaspImageRecComap/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Arm627/WaspImageRecComap@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Arm627/WaspImageRecComap/resolve/refs%2Fpr%2F1/pytorch_model.bin
343 MB
- Xet hash:
- ec39c727422090776c003329df645cc497f0575e0e797c6e1a7d18957a37243e
- Size of remote file:
- 343 MB
- SHA256:
- f761a22575bc2716dbb5978c41c3d0c4f4ae0acb7693d380f4fbd3befe83f00f
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