Instructions to use OttoYu/Tree-Inspection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OttoYu/Tree-Inspection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OttoYu/Tree-Inspection") 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/Tree-Inspection") model = AutoModelForImageClassification.from_pretrained("OttoYu/Tree-Inspection") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - autotrain | |
| - vision | |
| - image-classification | |
| datasets: | |
| - OttoYu/autotrain-data-tree-inspection | |
| 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: 2.1481896644746374 | |
| # Model Trained Using AutoTrain | |
| - Problem type: Multi-class Classification | |
| - Model ID: 87833143598 | |
| - CO2 Emissions (in grams): 2.1482 | |
| ## Validation Metrics | |
| - Loss: 1.251 | |
| - Accuracy: 0.652 | |
| - Macro F1: 0.594 | |
| - Micro F1: 0.652 | |
| - Weighted F1: 0.620 | |
| - Macro Precision: 0.629 | |
| - Micro Precision: 0.652 | |
| - Weighted Precision: 0.642 | |
| - Macro Recall: 0.617 | |
| - Micro Recall: 0.652 | |
| - Weighted Recall: 0.652 |