Instructions to use aradootle/sam-vit-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aradootle/sam-vit-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="aradootle/sam-vit-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMaskGeneration processor = AutoProcessor.from_pretrained("aradootle/sam-vit-base") model = AutoModelForMaskGeneration.from_pretrained("aradootle/sam-vit-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download test_handler.py from aradootle/sam-vit-base: direct link, hf CLI and curl.
- Browser
- Download file 374 Bytes
-
https://huggingface.co/aradootle/sam-vit-base/resolve/refs%2Fpr%2F1/test_handler.py
- Command line
-
hf download hf://aradootle/sam-vit-base@refs/pr/1/test_handler.py
-
curl -L -o test_handler.py https://huggingface.co/aradootle/sam-vit-base/resolve/refs%2Fpr%2F1/test_handler.py
374 Bytes
| from handler import EndpointHandler | |
| # init handler | |
| my_handler = EndpointHandler(path=".") | |
| # prepare sample payload | |
| payload = {"inputs": "I am quite excited how this will turn out", "imageUrl": "https://res.cloudinary.com/dvfgdnfzd/image/upload/v1693510414/nvae1t0lvgzavfkgb45j.png"} | |
| # test the handler | |
| payload=my_handler(payload) | |
| # show results | |
| print("payload", payload) |