Instructions to use litert-community/mobilevitv2_100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use litert-community/mobilevitv2_100 with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Add LiteRT converted mobilevitv2_100
Browse files- README.md +41 -0
- model.tflite +3 -0
README.md
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---
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library_name: litert
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base_model: timm/mobilevitv2_100.cvnets_in1k
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tags:
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- vision
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- image-classification
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datasets:
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- imagenet-1k
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---
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# mobilevitv2_100
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Converted TIMM image classification model for LiteRT.
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- Source architecture: mobilevitv2_100
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- File: model.tflite
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## Model Details
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- **Model Type:** Image classification / feature backbone
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- **Model Stats:**
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- Params (M): 4.9
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- GMACs: 1.8
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- Activations (M): 16.1
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- Image size: 256 x 256
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- **Papers:**
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- Separable Self-attention for Mobile Vision Transformers: https://arxiv.org/abs/2206.02680
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- **Original:** https://github.com/apple/ml-cvnets
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- **Dataset:** ImageNet-1k
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## Citation
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```bibtex
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@article{Mehta2022SeparableSF,
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title={Separable Self-attention for Mobile Vision Transformers},
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author={Sachin Mehta and Mohammad Rastegari},
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journal={ArXiv},
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year={2022},
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volume={abs/2206.02680}
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}
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```
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model.tflite
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version https://git-lfs.github.com/spec/v1
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oid sha256:9bfc136fa3cf4ab8450ee8cd53da46b8dbb33fc039c121555a0651c0bcdd1750
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size 19702160
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