This is the implementation of YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design using ultralytics/yolov3. Thanks to the original author.
arXiv: https://arxiv.org/abs/2009.05697 In Proceeding in AAAI 2021
For those who may be interested in the compiler code (How to deploy it onto Android?): The compiler source code is associated with our collaborator at William & Mary, and has joint IP related stuff. We have no plans to open source this part now. Sorry for the inconvenience.
For IOS developer: We only use Android platform to build and test the compiler because of the advantages of highly open source. We also believe the same techniques can be applied on Apple IOS platform, but we haven't tested it yet.
The rapid development and wide utilization of object detection techniques have aroused attention on both accuracy and speed of object detectors. However, the current state-of-the-art object detection works are either accuracy-oriented using a large model but leading to high latency
or speed-oriented using a lightweight model but sacrificing accuracy. In this work, we propose YOLObile framework, a real-time object detection on mobile devices via compression-compilation co-design. A novel block-punched pruning scheme is proposed for any kernel size. To improve computational efficiency on mobile devices, a GPU-CPU collaborative scheme is adopted along with advanced compiler-assisted optimizations. Experimental results indicate that our pruning scheme achieves 14x compression rate of YOLOv4 with 49.0 mAP.
Under our YOLObile framework, we achieve 17 FPS inference speed using GPU on Samsung Galaxy S20.
By incorporating our proposed GPU-CPU collaborative scheme, the inference speed is increased to 19.1 FPS, and outperforms the original YOLOv4 by 5x speedup.
Python 3.7 or later with all
pip install -U -r requirements.txt packages including
torch == 1.4. Docker images come with all dependencies preinstalled. Docker requirements are:
- Nvidia Driver >= 440.44
- Docker Engine - CE >= 19.03
Download Coco Dataset: (18 GB)
cd ../ && sh YOLObile/data/get_coco2014.sh
The default path for coco data folder is outside the project root folder.
/Project /Project/YOLObile (Project root) /Project/coco (coco data)
Download Model Checkpoints:
Google Drive: Google Drive Download
Baidu Netdisk: Baidu Netdisk Download code: r3nk
After downloads, please put the weight file under ./weights folder
Docker build instructions
1. Install Docker and Nvidia-Docker
Docker images come with all dependencies preinstalled, however Docker itself requires installation, and relies of nvidia driver installations in order to interact properly with local GPU resources. The requirements are:
- Nvidia Driver >= 440.44 https://www.nvidia.com/Download/index.aspx
- Nvidia-Docker https://github.com/NVIDIA/nvidia-docker
- Docker Engine - CE >= 19.03 https://docs.docker.com/install/
2. Build the project
# Build and Push t=YOLObile && sudo docker build -t $t .
3. Run Container
# Pull and Run with local directory access t=YOLObile && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"your/cocodata/path:/usr/src/coco $t bash
4. Run Commands
Once the container is launched and you are inside it, you will have a terminal window in which you can run all regular bash commands, such as:
Train Options and Model Config:
./cfg/csdarknet53s-panet-spp.cfg (model configuration) ./cfg/darknet_admm.yaml (pruning configuration) ./cfg/darknet_retrain.yaml (retrain configuration)
./weights/yolov4dense.pt (dense model) ./weights/best8x-514.pt (pruned model)
The training process includes two steps:
python train.py --img-size 320 --batch-size 64 --device 0,1,2,3 --epoch 25 --admm-file darknet_admm --cfg cfg/csdarknet53s-panet-spp.cfg --weights weights/yolov4dense.pt --data data/coco2014.data
The pruning process does NOT support resume.
python train.py --img-size 320 --batch-size 64 --device 0,1,2,3 --epoch 280 --admm-file darknet_retrain --cfg cfg/csdarknet53s-panet-spp.cfg --weights weights/yolov4dense.pt --data data/coco2014.data --multi-scale.
The masked retrain process support resume.
You can run the total process via
Check model Weight Parameters & Flops:
Test model MAP:
python test.py --img-size 320 --batch-size 64 --device 0 --cfg cfg/csdarknet53s-panet-spp.cfg --weights weights/best8x-514.pt --data data/coco2014.data
Class Images Targets P R [email protected] F1: 100%|| 79/79 [00: all 5e+03 3.51e+04 0.501 0.544 0.508 0.512 person 5e+03 1.05e+04 0.643 0.697 0.698 0.669 bicycle 5e+03 313 0.464 0.409 0.388 0.435 car 5e+03 1.64e+03 0.492 0.547 0.503 0.518 motorcycle 5e+03 388 0.602 0.635 0.623 0.618 airplane 5e+03 131 0.676 0.786 0.804 0.727 bus 5e+03 259 0.67 0.788 0.792 0.724 train 5e+03 212 0.731 0.797 0.805 0.763 truck 5e+03 352 0.414 0.526 0.475 0.463 toothbrush 5e+03 77 0.35 0.301 0.269 0.323 Speed: 3.6/1.4/5.0 ms inference/NMS/total per 320x320 image at batch-size 64 COCO mAP with pycocotools... loading annotations into memory... Done (t=3.87s) creating index... index created! Loading and preparing results... DONE (t=3.74s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=83.06s). Accumulating evaluation results... DONE (t=9.39s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.334 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.514 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.350 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.117 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.374 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.519 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.295 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.466 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.504 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.240 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.583 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.727
FPS vs mAP on COCO dataset
Already Known Issues
The accuracy printed in retraining process is not accurate. Please run the test.py individually to check the accuracy. I raised this issue in the old versions of Ultralytics/YOLOv3 repository, and I am not sure if they had already solved yet.
When you use multi-card training（4 cards or more ), the training process may stop after a few hours without any errors printed.
I suggest using docker instead if you use 4 cards or more. The docker build instructions can be found above.
Pytorch 1.5+ might have multi card issues