Image Classification
Transformers
Safetensors
English
siglip
Trash
Classification
Net
biology
SigLIP2
Instructions to use prithivMLmods/Trash-Net with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Trash-Net with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Trash-Net") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Trash-Net") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Trash-Net", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from prithivMLmods/Trash-Net: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/prithivMLmods/Trash-Net/resolve/main/training_args.bin
- Command line
-
hf download hf://prithivMLmods/Trash-Net/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/prithivMLmods/Trash-Net/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 220fd94e1456aadadb61c272e929cdbda14be3d84a8ee2f2a44965f432ce0e97
- Size of remote file:
- 5.3 kB
- SHA256:
- 035d5f50831a3a5d7cd09a166c9fa2c85bfdb9d0d1888ae17611b61aaa610ef2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.