In this work, we introduce a new learning target named local counting map, and show its feasibility and advantages in local counting regression. Meanwhile, we propose an adaptive mixture regression framework in a coarse-to-fine manner. It reports marked improvements in counting accuracy and the stability of the training phase, and achieves the start-of-the-art performances on several author- itative datasets. For more details, please refer to our arXiv paper.
Prerequisites
requirements.txt
, run pip install -r requirements.txt
.Data Preparation
Pretrained Model (Only for Training)
vgg16-397923af.pth
from torchvision.models
../models/Pretrain_model/
. Folder Tree
+-- source_code
| +-- datasets
| +-- SHHA
| +-- ......
| +-- misc
| +-- models
| +-- Prerain_Model
| +-- SCC_Model
| +-- ......
| +-- ProcessedData
| +-- shanghaitech_part_A
| +-- ......
QNRF-model (MAE/MSE: 86.6/152.1):
Google Drive: download link, Baidu Yun: download link (key: pe2r)
./demo_image
.python demo.py
../demo_image/result
.test_config.py
.python test.py
.