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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}
}
```
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