Instructions to use bazudde/potato_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bazudde/potato_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bazudde/potato_model") 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("bazudde/potato_model") model = AutoModelForImageClassification.from_pretrained("bazudde/potato_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from bazudde/potato_model: direct link, hf CLI and curl.
- Browser
- Download file 896 Bytes
-
https://huggingface.co/bazudde/potato_model/resolve/main/README.md
- Command line
-
hf download hf://bazudde/potato_model/README.md
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curl -L -o README.md https://huggingface.co/bazudde/potato_model/resolve/main/README.md
896 Bytes
metadata
tags:
- autotrain
- vision
- image-classification
datasets:
- bazudde/autotrain-data-sweet-potato-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: 0.2585547491917275
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 44552112263
- CO2 Emissions (in grams): 0.2586
Validation Metrics
- Loss: 0.098
- Accuracy: 0.923
- Macro F1: 0.911
- Micro F1: 0.923
- Weighted F1: 0.918
- Macro Precision: 0.958
- Micro Precision: 0.923
- Weighted Precision: 0.933
- Macro Recall: 0.889
- Micro Recall: 0.923
- Weighted Recall: 0.923