Instructions to use ongp/70btclassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ongp/70btclassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ongp/70btclassification") 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("ongp/70btclassification") model = AutoModelForImageClassification.from_pretrained("ongp/70btclassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 873 Bytes
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tags:
- autotrain
- vision
- image-classification
datasets:
- ongp/autotrain-data-312312
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: 0.011291420892523918
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2723080856
- CO2 Emissions (in grams): 0.0113
## Validation Metrics
- Loss: 0.877
- Accuracy: 0.708
- Macro F1: 0.695
- Micro F1: 0.708
- Weighted F1: 0.704
- Macro Precision: 0.703
- Micro Precision: 0.708
- Weighted Precision: 0.711
- Macro Recall: 0.699
- Micro Recall: 0.708
- Weighted Recall: 0.708 |