Pedestron
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.
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Updates
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🧨 We have realeased PedesFormer - Transformer Based Pedestrian Detection repo (particularly Swin Transformer) along with pre-trained models. Stay tune for the updates.🧨
YouTube demo
- Caltech and EuroCity Persons. Pre-Trained model available.
Leaderboards
Installation
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.
List of detectors
Currently we provide configurations for the following detectors, with different backbones
- Cascade Mask-R-CNN
- Faster R-CNN
- RetinaNet
- RetinaNet with Guided Anchoring
- Hybrid Task Cascade (HTC)
- MGAN
- CSP
Following datasets are currently supported
Datasets Preparation
- We refer to Datasets preparation file for detailed instructions
Benchmarking
Benchmarking of pre-trained models on pedestrian detection datasets (autonomous driving)
| 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 |
Benchmarking of pre-trained models on general human/person detection datasets
| Detector | Dataset | Backbone | AP |
|---|---|---|---|
| Cascade Mask R-CNN | CrowdHuman | HRNet | 84.1 |
Getting Started
Running a demo using pre-trained model on few images
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.
Training
- single GPU training
- multiple GPU training
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
Testing
- single GPU testing
- multiple GPU testing
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
- Download the pretrained CityPersons model and place it in the folder "models_pretrained/".
- Run the following command:
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
- Similarly change respective paths for EuroCity Persons
- For Caltech refer to Datasets preparation file
Please cite the following work
@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}
}
Whlie training, the error(cuda out of memory) occured (I have 2 GeForce RTX 2080 and img_per_gpu is 1) . I want to know how the scale parameters are set and whether they can be changed. And is it influenced a lot to result if I change them?
Thanks!
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