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
Update README.md
Browse files
README.md
CHANGED
|
@@ -16,12 +16,6 @@ co2_eq_emissions:
|
|
| 16 |
emissions: 0.8942374660281194
|
| 17 |
---
|
| 18 |
|
| 19 |
-
# Model Trained Using AutoTrain
|
| 20 |
-
|
| 21 |
-
- Problem type: Multi-class Classification
|
| 22 |
-
- Model ID: 43081109815
|
| 23 |
-
- CO2 Emissions (in grams): 0.8942
|
| 24 |
-
|
| 25 |
## Validation Metrics
|
| 26 |
|
| 27 |
- Loss: 0.772
|
|
|
|
| 16 |
emissions: 0.8942374660281194
|
| 17 |
---
|
| 18 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 19 |
## Validation Metrics
|
| 20 |
|
| 21 |
- Loss: 0.772
|