Instructions to use zeromodels/mask2former-swin-tiny-coco-panoptic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ZeroModels
How to use zeromodels/mask2former-swin-tiny-coco-panoptic with ZeroModels:
# pip install -U zeromodels # ZeroModels is pure Keras 3, so pick a backend: "jax", "torch" or "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" from zeromodels import AutoZModel # AutoZModel reads the repo's model_type and loads the matching class. # For a task head use the matching loader, e.g. AutoZMImageClassify / AutoZMDetect / # AutoZMSemanticSegment / AutoZMTextGenerate (see zeromodels.auto). model = AutoZModel.from_weights("zeromodels/mask2former-swin-tiny-coco-panoptic") - Keras
How to use zeromodels/mask2former-swin-tiny-coco-panoptic with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://zeromodels/mask2former-swin-tiny-coco-panoptic") - Notebooks
- Google Colab
- Kaggle
| pipeline_tag: image-segmentation | |
| license: mit | |
| library_name: kerasformers | |
| tags: | |
| - keras | |
| - kerasformers | |
| - mask2former | |
| - tf | |
| - jax | |
| - pytorch | |
| # mask2former-swin-tiny-coco-panoptic (Keras 3) | |
| Pure-Keras 3 weights for [kerasformers](https://github.com/IMvision12/KerasFormers), mirrored from the source. License: `mit`. | |
| ```python | |
| from kerasformers.models.mask2former import Mask2FormerUniversalSegment | |
| model = Mask2FormerUniversalSegment.from_weights("mask2former-swin-tiny-coco-panoptic") | |
| ``` | |