Instructions to use SummerChiam/pond_image_classification_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SummerChiam/pond_image_classification_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SummerChiam/pond_image_classification_1") 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/pond_image_classification_1") model = AutoModelForImageClassification.from_pretrained("SummerChiam/pond_image_classification_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from SummerChiam/pond_image_classification_1: direct link, hf CLI and curl.
- Browser
- Download file 1.04 kB
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https://huggingface.co/SummerChiam/pond_image_classification_1/resolve/main/README.md
- Command line
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hf download hf://SummerChiam/pond_image_classification_1/README.md
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curl -L -o README.md https://huggingface.co/SummerChiam/pond_image_classification_1/resolve/main/README.md
1.04 kB
metadata
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: pond_image_classification
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9948979616165161
pond_image_classification
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.






