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
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
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