Instructions to use Finisha-LLM/Two-fruita-classify with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Finisha-LLM/Two-fruita-classify with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Finisha-LLM/Two-fruita-classify", device_map="auto") 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("Finisha-LLM/Two-fruita-classify") model = AutoModelForImageClassification.from_pretrained("Finisha-LLM/Two-fruita-classify", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5635917f85fd42bc682e48c5cc4bdf17dfb89c35756ee14a69350da59711a013
- Size of remote file:
- 5.37 kB
- SHA256:
- 37b8cbf63d8f8d438d8a8476e36a035886302a7f12187f7cd5087bb98aab1e9c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.