Instructions to use litert-community/repvit_m0_9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use litert-community/repvit_m0_9 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
metadata
library_name: litert
base_model: timm/repvit_m0_9.dist_300e_in1k
tags:
- vision
- image-classification
datasets:
- imagenet-1k
repvit_m0_9
Converted TIMM image classification model for LiteRT.
- Source architecture:
repvit_m0_9 - Source checkpoint:
timm/repvit_m0_9.dist_300e_in1k - File:
model.tflite - Input:
float32tensor in NCHW layout, shape[1, 3, 224, 224] - Output: ImageNet-1K logits, shape
[1, 1000]
Model Details
- Model Type: Image classification / feature backbone
- Model Stats:
- Params (M): 5.5
- GMACs: 0.8
- Activations (M): 7.4
- Image size: 224 x 224
- Papers:
- RepViT: Revisiting Mobile CNN From ViT Perspective: https://arxiv.org/abs/2307.09283
- Original: https://github.com/THU-MIG/RepViT
- Dataset: ImageNet-1k
Citation
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={2307.09283},
archivePrefix={arXiv},
primaryClass={cs.CV}
}