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opencv/
object_detection_yolox

onnx

2107.08430

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YOLOX

Nanodet: YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. YOLOX is a high-performing object detector, an improvement to the existing YOLO series. YOLO series are in constant exploration of techniques to improve the object detection techniques for optimal speed and accuracy trade-off for real-time applications.

Key features of the YOLOX object detector

Anchor-free detectors significantly reduce the number of design parameters
A decoupled head for classification, regression, and localization improves the convergence speed
SimOTA advanced label assignment strategy reduces training time and avoids additional solver hyperparameters
Strong data augmentations like MixUp and Mosiac to boost YOLOX performance

Note:

This version of YoloX: YoloX_s
object_detection_yolox_2022nov_int8bq.onnx represents the block-quantized version in int8 precision and is generated using block_quantize.py with block_size=64.

Demo

Python

Run the following command to try the demo:

shell

# detect on camera input
python demo.py
# detect on an image
python demo.py --input /path/to/image -v

Note:

image result saved as "result.jpg"
this model requires opencv-python>=4.8.0

C++

Install latest OpenCV and CMake >= 3.24.0 to get started with:

shell

# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
cmake --build build

# detect on camera input
./build/opencv_zoo_object_detection_yolox
# detect on an image
./build/opencv_zoo_object_detection_yolox -m=/path/to/model -i=/path/to/image -v
# get help messages
./build/opencv_zoo_object_detection_yolox -h

Results

Here are some of the sample results that were observed using the model (yolox_s.onnx),

Check benchmark/download_data.py for the original images.

Model metrics:

The model is evaluated on COCO 2017 val. Results are showed below:

Average Precision

Average Recall

area	IoU	Average Precision(AP)
all	0.50:0.95	0.405
all	0.50	0.593
all	0.75	0.437
small	0.50:0.95	0.232
medium	0.50:0.95	0.448
large	0.50:0.95	0.541

area	IoU	Average Recall(AR)
all	0.50:0.95	0.326
all	0.50:0.95	0.531
all	0.50:0.95	0.574
small	0.50:0.95	0.365
medium	0.50:0.95	0.634
large	0.50:0.95	0.724

class	AP	class	AP	class	AP
person	54.109	bicycle	31.580	car	40.447
motorcycle	43.477	airplane	66.070	bus	64.183
train	64.483	truck	35.110	boat	24.681
traffic light	25.068	fire hydrant	64.382	stop sign	65.333
parking meter	48.439	bench	22.653	bird	33.324
cat	66.394	dog	60.096	horse	58.080
sheep	49.456	cow	53.596	elephant	65.574
bear	70.541	zebra	66.461	giraffe	66.780
backpack	13.095	umbrella	41.614	handbag	12.865
tie	29.453	suitcase	39.089	frisbee	61.712
skis	21.623	snowboard	31.326	sports ball	39.820
kite	41.410	baseball bat	27.311	baseball glove	36.661
skateboard	49.374	surfboard	35.524	tennis racket	45.569
bottle	37.270	wine glass	33.088	cup	39.835
fork	31.620	knife	15.265	spoon	14.918
bowl	43.251	banana	27.904	apple	17.630
sandwich	32.789	orange	29.388	broccoli	23.187
carrot	23.114	hot dog	33.716	pizza	52.541
donut	47.980	cake	36.160	chair	29.707
couch	46.175	potted plant	24.781	bed	44.323
dining table	30.022	toilet	64.237	tv	57.301
laptop	58.362	mouse	57.774	remote	24.271
keyboard	48.020	cell phone	32.376	microwave	57.220
oven	36.168	toaster	28.735	sink	38.159
refrigerator	52.876	book	15.030	clock	48.622
vase	37.013	scissors	26.307	teddy bear	45.676
hair drier	7.255	toothbrush	19.374

License

All files in this directory are licensed under Apache 2.0 License.

Contributor Details

Google Summer of Code'22
Contributor: Sri Siddarth Chakaravarthy
Github Profile: https://github.com/Sidd1609
Organisation: OpenCV
Project: Lightweight object detection models using OpenCV

Reference

YOLOX article: https://arxiv.org/abs/2107.08430
YOLOX weight and scripts for training: https://github.com/Megvii-BaseDetection/YOLOX
YOLOX blog: https://arshren.medium.com/yolox-new-improved-yolo-d430c0e4cf20
YOLOX-lite: https://github.com/TexasInstruments/edgeai-yolox

opencv/object_detection_yolox