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
metadata
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, mirrored from the source. License: mit.
from kerasformers.models.mask2former import Mask2FormerUniversalSegment
model = Mask2FormerUniversalSegment.from_weights("mask2former-swin-tiny-coco-panoptic")