Instructions to use ongkn/attraction-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ongkn/attraction-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ongkn/attraction-classifier") 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("ongkn/attraction-classifier") model = AutoModelForImageClassification.from_pretrained("ongkn/attraction-classifier") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 1500
Browse files- model.safetensors +1 -1
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 343223968
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:54ab3177b0049e1754091cc8d8462329836d7e4e2c00d511ad5005c6bc8f5a97
|
| 3 |
size 343223968
|