Instructions to use microsoft/resnet-34 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/resnet-34 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/resnet-34") 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-34") model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-34") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
ceb1877
1
Parent(s): f5f4f59
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=1.211e-04; Maximum converted output difference=1.211e-04.
- 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:ae6a2847c51006462492cbbcdb571b206ac8d756abf04af4167ab7a1d7574740
|
| 3 |
+
size 87494664
|