Instructions to use tdros/zoalearn2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tdros/zoalearn2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tdros/zoalearn2") 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("tdros/zoalearn2") model = AutoModelForImageClassification.from_pretrained("tdros/zoalearn2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -20,4 +20,4 @@ model-index:
|
|
| 20 |
|
| 21 |
# zoalearn2
|
| 22 |
|
| 23 |
-
|
|
|
|
| 20 |
|
| 21 |
# zoalearn2
|
| 22 |
|
| 23 |
+
zoalearn2 is [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) fine-tuned to classify 32 of the most popular coral speices.
|