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object_detection_yolox

README.md

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

Demo

Python

Run the following command to try the demo:

# 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:

# 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),

1_res.jpg 2_res.jpg 3_res.jpg

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