Instructions to use frncscp/patacoswin_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frncscp/patacoswin_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="frncscp/patacoswin_v1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("frncscp/patacoswin_v1") model = AutoModelForImageClassification.from_pretrained("frncscp/patacoswin_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- autotrain
- vision
- image-classification
datasets:
- frncscp/autotrain-data-autopatacotron
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: 0.41447738215787666
Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 34284130630
- CO2 Emissions (in grams): 0.4145
Validation Metrics
- Loss: 0.070
- Accuracy: 0.989
- Precision: 0.978
- Recall: 1.000
- AUC: 0.998
- F1: 0.989