Instructions to use mraottth/trashbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mraottth/trashbot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="mraottth/trashbot")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("mraottth/trashbot") model = SegformerForSemanticSegmentation.from_pretrained("mraottth/trashbot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mraottth/trashbot: direct link, hf CLI and curl.
- Browser
- Download file 339 MB
-
https://huggingface.co/mraottth/trashbot/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mraottth/trashbot/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mraottth/trashbot/resolve/main/pytorch_model.bin
339 MB
- Xet hash:
- 788a9abe4811d75e8668e49559d25e0efb2a6a1d61c9fd2ebe454aa698d03751
- Size of remote file:
- 339 MB
- SHA256:
- c034197d266406f027b6e9b454b7af7fa2e7b3781097baafcff0cd589be78312
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.