Instructions to use facebook/convnextv2-pico-1k-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/convnextv2-pico-1k-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/convnextv2-pico-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-pico-1k-224") model = AutoModelForImageClassification.from_pretrained("facebook/convnextv2-pico-1k-224", device_map="auto") - Inference
- 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=4.435e-05; Maximum crossload hidden layer difference=9.155e-05;
Maximum conversion output difference=4.435e-05; Maximum conversion hidden layer difference=9.155e-05;
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: 1.764e-05
List of maximum hidden layer differences above the threshold (1e-05):
hidden_states[2]: 1.259e-04
hidden_states[3]: 2.441e-04
Minor error in conversion code when I created this. See GH PR for details.
lysandre changed pull request status to merged