Instructions to use Rimiru/tech-recog2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rimiru/tech-recog2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Rimiru/tech-recog2") 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("Rimiru/tech-recog2") model = AutoModelForImageClassification.from_pretrained("Rimiru/tech-recog2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download images/Desktop_Computer.jpg from Rimiru/tech-recog2: direct link, hf CLI and curl.
- Browser
- Download file 18.1 kB
-
https://huggingface.co/Rimiru/tech-recog2/resolve/main/images/Desktop_Computer.jpg
- Command line
-
hf download hf://Rimiru/tech-recog2/images/Desktop_Computer.jpg
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curl -L -o Desktop_Computer.jpg https://huggingface.co/Rimiru/tech-recog2/resolve/main/images/Desktop_Computer.jpg
18.1 kB

- Xet hash:
- ffcaf6a804e62200a5bd7c696fd7ef7eaac50374c7d65dc167565528df39cdbe
- Size of remote file:
- 18.1 kB
- SHA256:
- f8afb82638a8c8cf6e4147270426874e13a8844fcec53a4070c9c9fddc8af31e
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