Instructions to use OttoYu/TreeClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OttoYu/TreeClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OttoYu/TreeClassification") 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("OttoYu/TreeClassification") model = AutoModelForImageClassification.from_pretrained("OttoYu/TreeClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from OttoYu/TreeClassification: direct link, hf CLI and curl.
- Browser
- Download file 712 Bytes
-
https://huggingface.co/OttoYu/TreeClassification/resolve/main/README.md
- Command line
-
hf download hf://OttoYu/TreeClassification/README.md
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curl -L -o README.md https://huggingface.co/OttoYu/TreeClassification/resolve/main/README.md
712 Bytes
metadata
tags:
- tree
- vision
- image-classification
widget:
- src: >-
https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: >-
https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: >-
https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.8942374660281194
metrics:
- accuracy
Validation Metrics
- Loss: 0.772
- Accuracy: 0.792
- Macro F1: 0.754
- Micro F1: 0.792
- Weighted F1: 0.747
- Macro Precision: 0.744
- Micro Precision: 0.792
- Weighted Precision: 0.743
- Macro Recall: 0.808
- Micro Recall: 0.792
- Weighted Recall: 0.792