Instructions to use facebook/regnet-x-040 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/regnet-x-040 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/regnet-x-040") 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-x-040") model = AutoModelForImageClassification.from_pretrained("facebook/regnet-x-040") - Notebooks
- Google Colab
- Kaggle
Add TF weights
#1
by joaogante - opened
Model converted by the transformers' pt_to_tf CLI.
All converted model outputs and hidden layers were validated against its Pytorch counterpart. Maximum crossload output difference=4.809e-04; Maximum converted output difference=4.809e-04.
cc @patrickvonplaten [HF maintainer(s) for this repo]
Related PR: https://github.com/huggingface/transformers/pull/17554
The error on the internal hidden layers was slightly above the desired level (<1e-5), but the output layers were fine. cc @sayakpaul @nielsr
Thanks!
nielsr changed pull request status to merged