1 Intro
2 Updates
3 Coming Soon
4 Models
5 Quick Start
6 Cite EdgeYOLO
7 Bugs found currently
- In embeded device such as Nvidia Jetson AGX Xavier, EdgeYOLO reaches 34FPS with 50.6% AP in COCO2017 dataset and 25.9% AP in VisDrone2019 (image input size is 640x640, batch=16, post-process included). And for smaller model EdgeYOLO-S, it reaches 53FPS with 44.1% AP and 63.3% AP0.5(SOTA in P5 small models) in COCO2017.
- we provide a more effective data augmentation during training.
- small object and medium object detect performace is imporved by using RH loss during the last few training epochs.
- Our pre-print paper is released on arxiv.
[2023/2/20]
- TensorRT cpp inference console demo (lib opencv and qt5 required)
- Fix bugs when exporting models using Version 7.X TensorRT
[2023/2/19]
- Publish TensorRT int8 export code with Calibration (torch2trt is required)
- Now evaluate.py doesn't support tensorrt model, we will update it in the near future
- MNN deployment code
- More different models
- C++ code for TensorRT inference with UI
- EdgeYOLO-mask for segmentation task
- Simple but effective pretrain method
- models trained on COCO2017-train
Model | Size | mAPval 0.5:0.95 |
mAPval 0.5 |
FPSAGX Xavier trt fp16 batch=16 include NMS |
Params train / infer (M) |
Download |
---|---|---|---|---|---|---|
EdgeYOLO-Tiny-LRELU | 416 640 |
33.1 37.8 |
50.5 56.7 |
206 109 |
7.6 / 7.0 | github |
EdgeYOLO-Tiny | 416 640 |
37.2 41.4 |
55.4 60.4 |
136 67 |
5.8 / 5.5 | github |
EdgeYOLO-S | 640 | 44.1 | 63.3 | 53 | 9.9 / 9.3 | github |
EdgeYOLO-M | 640 | 47.5 | 66.6 | 46 | 19.0 / 17.8 | github |
EdgeYOLO | 640 | 50.6 | 69.8 | 34 | 41.2 / 40.5 | github |
- models trained on VisDrone2019 (pretrained backbone on COCO2017-train)
we use VisDrone2019-DET dataset with COCO format in our training.
Model | Size | mAPval 0.5:0.95 |
mAPval 0.5 |
Download |
---|---|---|---|---|
EdgeYOLO-Tiny-LRELU | 416 640 |
12.1 18.5 |
22.8 33.6 |
github |
EdgeYOLO-Tiny | 416 640 |
14.9 21.8 |
27.3 38.5 |
github |
EdgeYOLO-S | 640 | 23.6 | 40.8 | github |
EdgeYOLO-M | 640 | 25.0 | 42.9 | github |
EdgeYOLO | 640 | 25.9 26.4 |
43.9 44.8 |
github(legacy) github(new) |
git clone https://github.com/LSH9832/edgeyolo.git
cd edgeyolo
pip install -r requirements.txt
if you use tensorrt, please make sure torch2trt and TensorRT Development Toolkit(version>7.1.3.0) are installed.
git clone https://github.com/NVIDIA-AI-IOT/torch2trt.git
cd torch2trt
python setup.py install
or to make sure you use the same version of torch2trt as ours, download here
First download weights here
python detect.py --weights edgeyolo_coco.pth --source XXX.mp4 --fp16
# all options
python detect.py --weights edgeyolo_coco.pth
--source /XX/XXX.mp4 # or dir with images, such as /dataset/coco2017/val2017 (jpg/jpeg, png, bmp, webp is available)
--conf-thres 0.25
--nms-thres 0.5
--input-size 640 640
--batch 1
--save-dir ./output/detect/imgs # if you press "s", the current frame will be saved in this dir
--fp16
--no-fuse # do not reparameterize model
--no-label # do not draw label with class name and confidence
--mp # use multi-process to show images more smoothly when batch > 1
--fps 30 # max fps limitation, valid only when option --mp is used
- first preparing your dataset and create dataset config file(./params/dataset/XXX.yaml), make sure your dataset config file contains:
(COCO, VOC, VisDrone and DOTA formats are supported)
type: "coco" # dataset format(lowercase),COCO, VOC, VisDrone and DOTA formats are supported currently
dataset_path: "/dataset/coco2017" # root dir of your dataset
kwargs:
suffix: "jpg" # suffix of your dataset's images
use_cache: true # test on i5-12490f: Total loading time: 52s -> 10s(seg enabled) and 39s -> 4s(seg disabled)
train:
image_dir: "images/train2017" # train set image dir
label: "annotations/instances_train2017.json" # train set label file(format with single label file) or directory(multi label files)
val:
image_dir: "images/val2017" # evaluate set image dir
label: "annotations/instances_val2017.json" # evaluate set label file or directory
test:
test_dir: "test2017" # test set image dir (not used in code now, but will)
segmentaion_enabled: true # whether this dataset has segmentation labels and you are going to use them instead of bbox labels
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush'] # category names
- then edit file ./params/train/train_XXX.yaml
- finally
python train.py --cfg ./params/train/train_XXX.yaml
python evaluate.py --weights edgeyolo_coco.pth --dataset params/dataset/XXX.yaml --batch 16 --device 0
# all options
python evaluate.py --weights edgeyolo_coco.pth
--dataset params/dataset/XXX.yaml
--batch 16 # batch size for each gpu
--device 0
--input-size 640 640 # height, width
- ONNX
python export.py --onnx --weights edgeyolo_coco.pth --batch 1
# all options
python export.py --onnx # or --onnx-only if tensorrt and torch2trt are not installed
--weights edgeyolo_coco.pth
--input-size 640 640 # height, width
--batch 1
--opset 11
--no-simplify # do not simplify this model
it generates
output/export/edgeyolo_coco/640x640_batch1.onnx
- TensorRT
# fp16
python export.py --trt --weights edgeyolo_coco.pth --batch 1 --workspace 8
# int8
python export.py --trt --weights edgeyolo_coco.pth --batch 1 --workspace 8 --int8 --dataset params/dataset/coco.yaml --num-imgs 1024
# all options
python export.py --trt # you can add --onnx and relative options to export both models
--weights edgeyolo_coco.pth
--input-size 640 640 # height, width
--batch 1
--workspace 10 # (GB)
--no-fp16 # fp16 mode in default, use this option to disable it(fp32)
--int8 # int8 mode, the following options are needed for calibration
--datset params/dataset/coco.yaml # generates calibration images from its val images(upper limit:5120)
--train # use train images instead of val images(upper limit:5120)
--all # use all images(upper limit:5120)
--num-imgs 512 # (upper limit:5120)
it generates
(optional) output/export/edgeyolo_coco/640x640_batch1.onnx
output/export/edgeyolo_coco/640x640_batch1_fp16/int8.pt # for python inference
output/export/edgeyolo_coco/640x640_batch1_fp16/int8.engine # for c++ inference
output/export/edgeyolo_coco/640x640_batch1_fp16/int8.json # for c++ inference
- enviroment: TensorRT Version 8.2.5.1, Windows, i5-12490F, RTX 3060 12GB
- increase workspace and the number of images for calibration may improve the performance
COCO2017-TensorRT-int8
Int8 Model | Size | Calibration Image number |
Workspace (GB) |
mAPval 0.5:0.95 |
mAPval 0.5 |
FPSRTX 3060 trt int8 batch=16 include NMS |
---|---|---|---|---|---|---|
Tiny-LRELU | 416 640 |
512 | 8 | 31.5 36.4 |
48.7 55.5 |
730 360 |
Tiny | 416 640 |
512 | 8 | 34.9 39.8 |
53.1 59.5 |
549 288 |
S | 640 | 512 | 8 | 42.4 | 61.8 | 233 |
M | 640 | 512 | 8 | 45.2 | 64.2 | 211 |
L | 640 | 512 | 8 | 49.1 | 68.0 | 176 |
python detect.py --trt --weights output/export/edgeyolo_coco/640x640_batch1_int8.pt --source XXX.mp4
# all options
python detect.py --trt
--weights output/export/edgeyolo_coco/640x640_batch1_int8.pt
--source XXX.mp4
--legacy # if "img = img / 255" when you train your train model
--use-decoder # if use original yolox tensorrt model before version 0.3.0
--mp # use multi-process to show images more smoothly when batch > 1
--fps 30 # max fps limitation, valid only when option --mp is used
# build
cd cpp/console/linux
mkdir build && cd build
cmake ..
make -j4
# help
./yolo -?
./yolo --help
# run
# ./yolo [json file] [source] [--conf] [--nms] [--loop] [--no-label]
./yolo ../../../../output/export/edgeyolo_coco/640x640_batch1_int8.json ~/Videos/test.avi --conf 0.25 --nms 0.5 --loop --no-label
@article{edgeyolo2023,
title={EdgeYOLO: An Edge-Real-Time Object Detector},
author={Shihan Liu, Junlin Zha, Jian Sun, Zhuo Li, and Gang Wang},
journal={arXiv preprint arXiv:2302.07483},
year={2023}
}
- Sometimes it raises error as follows during training. Reduce pytorch version to 1.8.0 might solve this problem.
File "XXX/edgeyolo/edgeyolo/train/loss.py", line 667, in dynamic_k_matching
_, pos_idx = torch.topk(cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False)
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
- For DOTA dataset, we only support single GPU training mode now, please do not train DOTA dataset with distributed mode or model can not be trained correctly.
- Sometimes converting to TensorRT fp16 model with 8.4.X.X or higher version might lose a lot of precision, please use TensorRT Verson 7.X.X.X or 8.2.X.X