Instructions to use SpotLab/MobileViT_DeepLabv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SpotLab/MobileViT_DeepLabv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="SpotLab/MobileViT_DeepLabv3")# Load model directly from transformers import AutoImageProcessor, MobileViTForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("SpotLab/MobileViT_DeepLabv3") model = MobileViTForSemanticSegmentation.from_pretrained("SpotLab/MobileViT_DeepLabv3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# MobileViT + DeepLabV3 (extra extra small-sized model)
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MobileViT model pre-trained on PASCAL VOC at resolution 512x512. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this repository](https://github.com/apple/ml-cvnets). The license used is [Apple sample code license](https://github.com/apple/ml-cvnets/blob/main/LICENSE).
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_Forked from [apple/deeplabv3-mobilevit-xx-small](https://huggingface.co/apple/deeplabv3-mobilevit-xx-small)_
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# MobileViT + DeepLabV3 (extra extra small-sized model)
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MobileViT model pre-trained on PASCAL VOC at resolution 512x512. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this repository](https://github.com/apple/ml-cvnets). The license used is [Apple sample code license](https://github.com/apple/ml-cvnets/blob/main/LICENSE).
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