Instructions to use ongp/Pacc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ongp/Pacc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ongp/Pacc") 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("ongp/Pacc") model = AutoModelForImageClassification.from_pretrained("ongp/Pacc") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2718280758
- CO2 Emissions (in grams): 4.8390
Validation Metrics
- Loss: 0.663
- Accuracy: 0.708
- Macro F1: 0.698
- Micro F1: 0.708
- Weighted F1: 0.712
- Macro Precision: 0.703
- Micro Precision: 0.708
- Weighted Precision: 0.717
- Macro Recall: 0.695
- Micro Recall: 0.708
- Weighted Recall: 0.708
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