Instructions to use litert-community/maxvit_tiny_rw_224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use litert-community/maxvit_tiny_rw_224 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 maxvit_tiny_rw_224
Browse files- README.md +66 -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/maxvit_tiny_rw_224.sw_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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# maxvit_tiny_rw_224
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Converted TIMM image classification model for LiteRT.
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- Source architecture: `maxvit_tiny_rw_224`
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- Source checkpoint: `timm/maxvit_tiny_rw_224.sw_in1k`
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- File: `model.tflite`
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- Input: `float32` tensor in NCHW layout, shape `[1, 3, 224, 224]`
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- Output: ImageNet-1K logits, shape `[1, 1000]`
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## Runtime Status
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- CPU smoke test: passed with LiteRT `CompiledModel`.
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- GPU delegation: currently blocked for this model by rank-5 tensor patterns in the GPU backend, mostly `RESHAPE`, `TRANSPOSE`, and related window/attention operations. The model is published as CPU-ready while GPU support is being improved.
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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): 29.1
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- GMACs: 5.1
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- Activations (M): 33.1
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- Image size: 224 x 224
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- **Papers:**
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- MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697
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- **Dataset:** ImageNet-1k
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## Citation
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```bibtex
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@misc{rw2019timm,
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author = {Ross Wightman},
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title = {PyTorch Image Models},
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year = {2019},
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publisher = {GitHub},
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journal = {GitHub repository},
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doi = {10.5281/zenodo.4414861},
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howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
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}
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```
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```bibtex
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@article{tu2022maxvit,
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title={MaxViT: Multi-Axis Vision Transformer},
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author={Tu, Zhengzhong and Talebi, Hossein and Zhang, Han and Yang, Feng and Milanfar, Peyman and Bovik, Alan and Li, Yinxiao},
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journal={ECCV},
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year={2022},
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}
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```
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```bibtex
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@article{dai2021coatnet,
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title={CoAtNet: Marrying Convolution and Attention for All Data Sizes},
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author={Dai, Zihang and Liu, Hanxiao and Le, Quoc V and Tan, Mingxing},
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journal={arXiv preprint arXiv:2106.04803},
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year={2021}
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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:d912f15f864f3b4ccdedfb8fb21f3658aba060914d2a75420c5b277b4eafc9c4
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size 117852416
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