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README.md

SFC2Net

This repository implements SFC2Net proposed in the work:

High-Throughput Rice Density Estimation from Transplantation to Tillering Stages Using Deep Networks

Liang Liu, Hao Lu, Yanan Li, and Zhiguo Cao

Plant Phenomics, 2020

Model Structure

Qualitative Results

Installation

The code has been tested on Python 3.7.3 and PyTorch 1.3.1. Please follow the official instructions to configure your environment. See other required packages in requirements.txt.

The RPC Dataset

  • Download the Rice Plant Counting (RPC) test dataset from: BaiduYun (597 MB) (code: cirv) or OneDrive (597 MB)
  • Unzip the dataset and move it into the ./data folder, the path structure should look like this:
$./data/rice_datasets-test
├──── images
├──── label_mat
├──── test.txt

Inference

Pre-trained Model on RPC dataset

$./snapshots/rice/sfc2net
├──── model_best.pth.tar

Evaluation

python hltest.py

Benchmark Results

Counting Results on the RPC Dataset

Method Venue, Year Pretrained MAE MSE rMAE r2
MCNN CVPR 2016 No 92.11 121.52 15.33 0.89
TasselNetV2 Plant Methods 2019 No 59.39 95.80 7.86 0.91
CSRNet CVPR 2018 VGG16 49.22 74.58 7.47 0.91
BCNet TCSVT 2019 VGG16 31.28 49.82 4.76 0.96
SFC2Net This Paper MixNet-L 25.51 38.06 3.82 0.98

Comparison of Different Backbones

Backbone MAE MSE rMAE r2 #Param. ImageNet Top-1 Acc.
ResNet18 31.82 66.80 4.66 0.93 12.6M 69.8
ResNet34 34.42 61.58 4.95 0.94 22.7M 73.3
ResNet50 30.94 67.52 4.45 0.92 44.5M 76.2
ResNet101 35.53 56.26 4.99 0.95 63.5M 77.4
ResNet152 32.20 67.77 4.71 0.93 79.2M 78.3
EfficientNet-B0 36.65 70.74 5.30 0.92 8.1M 77.3
EfficientNet-B1 27.51 42.80 4.14 0.97 13.1M 79.2
EfficientNet-B2 30.54 53.65 4.48 0.95 15.5M 80.3
EfficientNet-B3 30.76 54.52 4.44 0.95 21.5M 81.7
EfficientNet-B4 28.06 52.17 4.24 0.95 35.3M 83.0
EfficientNet-B5 27.36 41.91 4.16 0.97 56.8M 83.7
EfficientNet-B6 29.96 50.03 4.42 0.96 81.7M 84.2
EfficientNet-B7 27.15 40.79 3.96 0.97 127.8M 84.4
VGG16 30.67 57.53 4.51 0.95 15.7M 71.6
MixNet-L 25.51 38.06 3.82 0.98 8.3M 78.9

Citation

If you find this work or code useful for your research, please cite:

@article{liu2020high,
  title={High-Throughput Rice Density Estimation from Transplantation to Tillering Stages Using Deep Network},
  author={Liu, Liang and Lu, Hao and Li, Yanan and Cao, Zhiguo},
  journal={Plant Phenomics},
  year={2020}
}

Permission

The code and data are only for non-commercial purposes. Copyrights reserved.

The training set of the RPC dataset is made available upon request. Contact: Hao Lu (poppinace@foxmail.com)

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