Instructions to use apple/deeplabv3-mobilevit-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apple/deeplabv3-mobilevit-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="apple/deeplabv3-mobilevit-small")# Load model directly from transformers import AutoImageProcessor, MobileViTForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-small") model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-small", 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=2.029e-04; Maximum crossload hidden layer difference=9.155e-05;
Maximum conversion output difference=2.029e-04; Maximum conversion hidden layer difference=9.155e-05;
CAUTION: The maximum admissible error was manually increased to 0.0003!
- tf_model.h5 +3 -0
tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:5e14ab532bd4b573c60e4f4c6639de6176db4c35c803cc7c0ba05fdb16e5b3de
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size 25943848
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