Instructions to use nvidia/groupvit-gcc-yfcc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/groupvit-gcc-yfcc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nvidia/groupvit-gcc-yfcc")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc") model = AutoModel.from_pretrained("nvidia/groupvit-gcc-yfcc") - Inference
- Notebooks
- Google Colab
- Kaggle
Add TF weights
#1
by ariG23498 HF Staff - opened
- tf_model.h5 +3 -0
tf_model.h5
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:63f08d517654420b5c6b599aae6cb0696c7284db7a0ceb463e7e33bd44990422
|
| 3 |
+
size 223577920
|