Image Segmentation
ZeroModels
Keras
PyTorch
JAX
TensorFlow
mobilevit
deeplabv3
semantic-segmentation
Instructions to use zeromodels/mobilevit_s_deeplabv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ZeroModels
How to use zeromodels/mobilevit_s_deeplabv3 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/mobilevit_s_deeplabv3") - Keras
How to use zeromodels/mobilevit_s_deeplabv3 with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://zeromodels/mobilevit_s_deeplabv3") - Notebooks
- Google Colab
- Kaggle
File size: 477 Bytes
df3c962 aaf57aa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"library_name": "zeromodels",
"preprocessor_module": "zeromodels.models.mobilevit",
"preprocessor_class": "MobileViTImageProcessor",
"variant": null,
"size": {
"shortest_edge": 544
},
"crop_size": {
"height": 512,
"width": 512
},
"resample": "bilinear",
"do_resize": true,
"do_center_crop": true,
"do_rescale": true,
"rescale_factor": 0.00392156862745098,
"do_flip_channel_order": true,
"return_tensor": true,
"data_format": null
} |