Instructions to use sxdave/plant_classification_model_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sxdave/plant_classification_model_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sxdave/plant_classification_model_1") 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("sxdave/plant_classification_model_1") model = AutoModelForImageClassification.from_pretrained("sxdave/plant_classification_model_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from sxdave/plant_classification_model_1: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/sxdave/plant_classification_model_1/resolve/refs%2Fpr%2F2/optimizer.pt
- Command line
-
hf download hf://sxdave/plant_classification_model_1@refs/pr/2/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/sxdave/plant_classification_model_1/resolve/refs%2Fpr%2F2/optimizer.pt
687 MB
- Xet hash:
- 686f675a91bf07b4a1c07d049e5acfb0e9733007db5412d151e5208e9ca9b361
- Size of remote file:
- 687 MB
- SHA256:
- 07cd332e471e357f594305431a260ea02c28a49760030a24aa661fde670c7879
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