Instructions to use facebook/convnextv2-large-1k-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/convnextv2-large-1k-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/convnextv2-large-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-large-1k-224") model = AutoModelForImageClassification.from_pretrained("facebook/convnextv2-large-1k-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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=1.669e-05; Maximum crossload hidden layer difference=9.766e-03;
Maximum conversion output difference=1.669e-05; Maximum conversion hidden layer difference=9.766e-03;
CAUTION: The maximum admissible error was manually increased to 0.1!
See [GitHub PR #25558](https://github.com/huggingface/transformers/pull/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.
- tf_model.h5 +3 -0
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f0013dc3d41ac70bb1d2f8937e4611ff390f3517a1aeb724469e84466ab04f3d
|
| 3 |
+
size 792265144
|