Instructions to use micole66/autotrain-mercuryorsodium-1804662320 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use micole66/autotrain-mercuryorsodium-1804662320 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="micole66/autotrain-mercuryorsodium-1804662320") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("micole66/autotrain-mercuryorsodium-1804662320", dtype="auto") - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- autotrain
- vision
- image-classification
datasets:
- micole66/autotrain-data-mercuryorsodium
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: 0.3397575484174952
Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1804662320
- CO2 Emissions (in grams): 0.3398
Validation Metrics
- Loss: 0.186
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- AUC: 1.000
- F1: 1.000