[Pedestron] Generalizable Pedestrian Detection: The Elephant In The Room. @ CVPR2021
Pedestron is a MMdetection based repository, that focuses on the advancement of research on pedestrian detection. We provide a list of detectors, both general purpose and pedestrian specific to train and test. Moreover, we provide pre-trained models and benchmarking of several detectors on different pedestrian detection datasets. Additionally, we provide processed annotations and scripts to process the annotation of different pedestrian detection benchmarks. If you use Pedestron, please cite us (see at the end) and other respective sources.
We refer to the installation and list of dependencies to installation file. Clone this repo and follow installation. Alternatively, Google Colab step-by-step instruction can be followed for installation (Please download the pre-trained models from the table in the readme.md, the link is broken on google colab for the pre-trained model). Addiitonally, you can also refer to the google doc file for step-by-step installation. For running a docker image please see installation file.
Currently we provide configurations for the following detectors, with different backbones
Detector | Dataset | Backbone | Reasonable | Heavy |
---|---|---|---|---|
Cascade Mask R-CNN | CityPersons | HRNet | 7.5 | 28.0 |
Cascade Mask R-CNN | CityPersons | MobileNet | 10.2 | 37.3 |
Faster R-CNN | CityPersons | HRNet | 10.2 | 36.2 |
RetinaNet | CityPersons | ResNeXt | 14.6 | 39.5 |
RetinaNet with Guided Anchoring | CityPersons | ResNeXt | 11.7 | 41.5 |
Hybrid Task Cascade (HTC) | CityPersons | ResNeXt | 9.5 | 35.8 |
MGAN | CityPersons | VGG | 11.2 | 52.5 |
CSP | CityPersons | ResNet-50 | 10.9 | 41.3 |
Cascade Mask R-CNN | Caltech | HRNet | 1.7 | 25.7 |
Cascade Mask R-CNN | EuroCity Persons | HRNet | 4.4 | 21.3 |
Faster R-CNN | EuroCity Persons | HRNet | 6.1 | 27.0 |
Detector | Dataset | Backbone | AP |
---|---|---|---|
Cascade Mask R-CNN | CrowdHuman | HRNet | 84.1 |
Pre-trained model can be evaluated on sample images in the following way
python tools/demo.py config checkpoint input_dir output_dir
Download one of our provided pre-trained model and place it in models_pretrained folder. Demo can be run using the following command
python tools/demo.py configs/elephant/cityperson/cascade_hrnet.py ./models_pretrained/epoch_5.pth.stu demo/ result_demo/
See Google Colab demo.
Train with single GPU
python tools/train.py ${CONFIG_FILE}
Train with multiple GPUs
./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]
For instance training on CityPersons using single GPU
python tools/train.py configs/elephant/cityperson/cascade_hrnet.py
Training on CityPersons using multiple(7 in this case) GPUs
./tools/dist_train.sh configs/elephant/cityperson/cascade_hrnet.py 7
Test can be run using the following command.
python ./tools/TEST_SCRIPT_TO_RUN.py PATH_TO_CONFIG_FILE ./models_pretrained/epoch_ start end\
--out Output_filename --mean_teacher
For example for CityPersons inference can be done the following way
python ./tools/test_city_person.py configs/elephant/cityperson/cascade_hrnet.py ./models_pretrained/epoch_ 5 6\
--out result_citypersons.json --mean_teacher
Alternatively, for EuroCity Persons
python ./tools/test_euroCity.py configs/elephant/eurocity/cascade_hrnet.py ./models_pretrained/epoch_ 147 148 --mean_teacher
or without mean_teacher flag for MGAN
python ./tools/test_city_person.py configs/elephant/cityperson/mgan_vgg.py ./models_pretrained/epoch_ 1 2\
--out result_citypersons.json
Testing with multiple GPUs on CrowdHuman
./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]
./tools/dist_test.sh configs/elephant/crowdhuman/cascade_hrnet.py ./models_pretrained/epoch_19.pth.stu 8 --out CrowdHuman12.pkl --eval bbox
@InProceedings{Hasan_2021_CVPR,
author = {Hasan, Irtiza and Liao, Shengcai and Li, Jinpeng and Akram, Saad Ullah and Shao, Ling},
title = {Generalizable Pedestrian Detection: The Elephant in the Room},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {11328-11337}
}