Instructions to use SummerChiam/rust_image_classification_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SummerChiam/rust_image_classification_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SummerChiam/rust_image_classification_2") 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("SummerChiam/rust_image_classification_2") model = AutoModelForImageClassification.from_pretrained("SummerChiam/rust_image_classification_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from SummerChiam/rust_image_classification_2: direct link, hf CLI and curl.
- Browser
- Download file 761 Bytes
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https://huggingface.co/SummerChiam/rust_image_classification_2/resolve/main/README.md
- Command line
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hf download hf://SummerChiam/rust_image_classification_2/README.md
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curl -L -o README.md https://huggingface.co/SummerChiam/rust_image_classification_2/resolve/main/README.md
761 Bytes
metadata
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rust_image_classification_2
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.853164553642273
rust_image_classification_2
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.

