Instructions to use facebook/convnextv2-atto-1k-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/convnextv2-atto-1k-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/convnextv2-atto-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-atto-1k-224") model = AutoModelForImageClassification.from_pretrained("facebook/convnextv2-atto-1k-224") - Inference
- Notebooks
- Google Colab
- Kaggle
Add TF weights
#3
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=3.719e-05; Maximum crossload hidden layer difference=1.831e-04;
Maximum conversion output difference=3.719e-05; Maximum conversion hidden layer difference=1.831e-04;
CAUTION: The maximum admissible error was manually increased to 0.1!
Note: Actual output differences are:
List of maximum output differences above the threshold (1e-05):
logits: 1.001e-05
List of maximum hidden layer differences above the threshold (1e-05):
hidden_states[2]: 1.335e-05
hidden_states[3]: 1.144e-04
hidden_states[4]: 3.004e-05
Minor error in conversion code when I created this. See GH PR for details.
lysandre changed pull request status to merged