Instructions to use OttoYu/TreeClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OttoYu/TreeClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OttoYu/TreeClassification") 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("OttoYu/TreeClassification") model = AutoModelForImageClassification.from_pretrained("OttoYu/TreeClassification") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - tree | |
| - vision | |
| - image-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: 0.8942374660281194 | |
| metrics: | |
| - accuracy | |
| ## Validation Metrics | |
| - Loss: 0.772 | |
| - Accuracy: 0.792 | |
| - Macro F1: 0.754 | |
| - Micro F1: 0.792 | |
| - Weighted F1: 0.747 | |
| - Macro Precision: 0.744 | |
| - Micro Precision: 0.792 | |
| - Weighted Precision: 0.743 | |
| - Macro Recall: 0.808 | |
| - Micro Recall: 0.792 | |
| - Weighted Recall: 0.792 |