Instructions to use robosapiens/class-room with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use robosapiens/class-room with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="robosapiens/class-room") 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("robosapiens/class-room") model = AutoModelForImageClassification.from_pretrained("robosapiens/class-room", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from robosapiens/class-room: direct link, hf CLI and curl.
- Browser
- Download file 293 Bytes
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https://huggingface.co/robosapiens/class-room/resolve/main/README.md
- Command line
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hf download hf://robosapiens/class-room/README.md
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curl -L -o README.md https://huggingface.co/robosapiens/class-room/resolve/main/README.md
293 Bytes
metadata
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: class-room
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8288288116455078