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

Download the model from: BaiduYun (26.2 MB) (code: nhxk) or OneDrive (26.2 MB)
Move the model into the folder, and the path structure should be:

$./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)

corncob1985/sfc2net

SFC2Net