---
library_name: litert
tags:
- vision
- image-classification
datasets:
- imagenet-1k
model-index:
- name: litert-community/MobileNet-v3-small
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: ImageNet-1K
      type: imagenet-1k
      split: validation
      args:
        split: validation
    metrics:
    - type: accuracy
      value: 0.67624
      name: Top-1 Accuracy
---

# MobileNet V3 Small


MobileNet V3 model pre-trained on ImageNet-1k at resolution 224x224


## Model description

The model was converted from a checkpoint from PyTorch Vision. 

The original model has:    
acc@1 (on ImageNet-1K): 67.668%      
acc@5 (on ImageNet-1K): 87.402%    
num_params: 2,542,856     
The license information of the original model was missing.


## Intended uses & limitations

The model files were converted from pretrained weights from PyTorch Vision. The models may have their own licenses or terms and conditions derived from PyTorch Vision and the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.


### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1905-02244,
  author       = {Andrew Howard and
                  Mark Sandler and
                  Grace Chu and
                  Liang{-}Chieh Chen and
                  Bo Chen and
                  Mingxing Tan and
                  Weijun Wang and
                  Yukun Zhu and
                  Ruoming Pang and
                  Vijay Vasudevan and
                  Quoc V. Le and
                  Hartwig Adam},
  title        = {Searching for MobileNetV3},
  journal      = {CoRR},
  volume       = {abs/1905.02244},
  year         = {2019},
  url          = {http://arxiv.org/abs/1905.02244},
  eprinttype    = {arXiv},
  eprint       = {1905.02244},
  timestamp    = {Thu, 27 May 2021 16:20:51 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1905-02244.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
```