Instructions to use facebook/convnextv2-base-1k-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/convnextv2-base-1k-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/convnextv2-base-1k-224") 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/convnextv2-base-1k-224") model = AutoModelForImageClassification.from_pretrained("facebook/convnextv2-base-1k-224") - Notebooks
- Google Colab
- Kaggle
Add TF weights
#2
by neggles - 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=9.060e-06; Maximum crossload hidden layer difference=6.409e-03;
Maximum conversion output difference=9.060e-06; Maximum conversion hidden layer difference=6.409e-03;
CAUTION: The maximum admissible error was manually increased to 0.1!
See GitHub PR #25558 for details, precision overridden due to hidden states being a little weird in TF; final output logits are within 1.788e-05 for all model variants/sizes.