Instructions to use dima806/flower_groups_image_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/flower_groups_image_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/flower_groups_image_detection") 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("dima806/flower_groups_image_detection") model = AutoModelForImageClassification.from_pretrained("dima806/flower_groups_image_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-10100/optimizer.pt from dima806/flower_groups_image_detection: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/dima806/flower_groups_image_detection/resolve/main/checkpoint-10100/optimizer.pt
- Command line
-
hf download hf://dima806/flower_groups_image_detection/checkpoint-10100/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dima806/flower_groups_image_detection/resolve/main/checkpoint-10100/optimizer.pt
687 MB
- Xet hash:
- f2ccb012485d9c3d62f9520769c4d12c2434b483c5ce4c4787389cbf51208113
- Size of remote file:
- 687 MB
- SHA256:
- 11687db96d0832eafb1d9ad0246d314ae38d07a23b56f5f64685f03d1ecfcf14
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