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")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Rimiru/tech-recog2") model = AutoModelForImageClassification.from_pretrained("Rimiru/tech-recog2") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- d94afe872bc19d933f6f8c370d8f0e40e4e4bfb6f99c595699314ed38d318602
- Size of remote file:
- 17.3 kB
- SHA256:
- a75433e20690748ae9a9d70352e91e77fd9172e928e3b386a8698b28c2e6c2d7
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