Instructions to use abletobetable/image_feature_extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abletobetable/image_feature_extractor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="abletobetable/image_feature_extractor") 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("abletobetable/image_feature_extractor") model = AutoModelForImageClassification.from_pretrained("abletobetable/image_feature_extractor") - Notebooks
- Google Colab
- Kaggle
Commit ·
be9ec28
1
Parent(s): ee92942
Training in progress, step 13000
Browse files- pytorch_model.bin +1 -1
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 349542649
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8b2bf8a6ca6c03dd010152098f0bdafa017f1cc63e1547552806bce8c0527a5c
|
| 3 |
size 349542649
|