Instructions to use Rimiru/tech-recog with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rimiru/tech-recog with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Rimiru/tech-recog") 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-recog") model = AutoModelForImageClassification.from_pretrained("Rimiru/tech-recog") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Rimiru/tech-recog")
model = AutoModelForImageClassification.from_pretrained("Rimiru/tech-recog")Quick Links
tech-recog
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
Example Images
Desktop Computer
Laptop
Smart Phone
Smart Watch
Tablet
- Downloads last month
- 6
Evaluation results
- Accuracyself-reported0.748





# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Rimiru/tech-recog") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")