Instructions to use KartikJawanjal/GrapeClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KartikJawanjal/GrapeClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="KartikJawanjal/GrapeClassification") 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("KartikJawanjal/GrapeClassification") model = AutoModelForImageClassification.from_pretrained("KartikJawanjal/GrapeClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from KartikJawanjal/GrapeClassification: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/KartikJawanjal/GrapeClassification/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://KartikJawanjal/GrapeClassification@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/KartikJawanjal/GrapeClassification/resolve/refs%2Fpr%2F1/optimizer.pt
687 MB
- Xet hash:
- 5a9ede51c8e9e60bd160514dff9e050d7e4707a87ab5494be694efcfb2a8608a
- Size of remote file:
- 687 MB
- SHA256:
- bb28f161e1b6618b13f4bab90db6c2f0c4f73b98956c721b7d13d1cdfbf2e179
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