Instructions to use microsoft/resnet-26 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/resnet-26 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/resnet-26") 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("microsoft/resnet-26") model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-26") - Notebooks
- Google Colab
- Kaggle
Commit ·
5ca407f
1
Parent(s): 7ecfa10
Add TF weights (#1)
Browse files- Add TF weights (c9dbc8c8378a0a31e7ccdbd60050d4a6148461ef)
- tf_model.h5 +3 -0
tf_model.h5
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f1f4dc9edd6f6a5c62a4c49947f4ad900fbe71aa8f2951e3f770f7b4476a59f3
|
| 3 |
+
size 64281824
|