Instructions to use facebook/regnet-y-004 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/regnet-y-004 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/regnet-y-004") 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("facebook/regnet-y-004") model = AutoModelForImageClassification.from_pretrained("facebook/regnet-y-004", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Joao Gante commited on
Commit ·
13ffa2a
1
Parent(s): 174c482
Add TF weights
Browse filesModel converted by the [`transformers`' `pt_to_tf` CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py).
All converted model outputs and hidden layers were validated against its Pytorch counterpart. Maximum crossload output difference=1.397e-04; Maximum converted output difference=1.397e-04.
cc @patrickvonplaten [HF maintainer(s) for this repo]
- tf_model.h5 +3 -0
tf_model.h5
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6ba37c5f96e1dd85bf47a65d2e841f00d4d6262f719baf3e2fe54d5d843e4a3a
|
| 3 |
+
size 17911488
|