object_detection_nanodet

Nanodet

Nanodet: NanoDet is a FCOS-style one-stage anchor-free object detection model which using Generalized Focal Loss as classification and regression loss.In NanoDet-Plus, we propose a novel label assignment strategy with a simple assign guidance module (AGM) and a dynamic soft label assigner (DSLA) to solve the optimal label assignment problem in lightweight model training.

Note:

• This version of nanodet: Nanodet-m-plus-1.5x_416

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"

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_nanodet
# detect on an image
./build/opencv_zoo_object_detection_nanodet -i=/path/to/image
# get help messages
./build/opencv_zoo_object_detection_nanodet -h

Results

Here are some of the sample results that were observed using the model,

Check benchmark/download_data.py for the original images.

Video inference result,

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 0.304 all 0.50 0.459 all 0.75 0.317 small 0.50:0.95 0.107 medium 0.50:0.95 0.322 large 0.50:0.95 0.478	area IoU Average Recall all 0.50:0.95 0.278 all 0.50:0.95 0.434 all 0.50:0.95 0.462 small 0.50:0.95 0.198 medium 0.50:0.95 0.510 large 0.50:0.95 0.702

class	AP50	mAP	class	AP50	mAP
person	67.5	41.8	bicycle	35.4	18.8
car	45.0	25.4	motorcycle	58.9	33.1
airplane	77.3	58.9	bus	68.8	56.4
train	81.1	60.5	truck	38.6	24.7
boat	35.5	16.7	traffic light	30.5	14.0
fire hydrant	69.8	54.5	stop sign	60.9	54.6
parking meter	55.1	38.5	bench	26.8	15.9
bird	38.3	23.6	cat	82.5	62.1
dog	67.0	51.4	horse	64.3	44.2
sheep	57.7	35.8	cow	61.2	39.9
elephant	79.9	56.2	bear	81.8	63.0
zebra	85.4	59.5	giraffe	84.1	59.9
backpack	12.4	5.9	umbrella	46.5	28.8
handbag	8.4	3.7	tie	35.2	19.6
suitcase	38.1	23.8	frisbee	60.7	43.9
skis	30.5	14.5	snowboard	32.3	18.2
sports ball	37.6	24.5	kite	51.1	30.4
baseball bat	28.9	13.6	baseball glove	40.1	21.6
skateboard	59.4	35.2	surfboard	47.9	26.6
tennis racket	55.2	30.5	bottle	34.7	20.2
wine glass	27.8	16.3	cup	35.5	23.7
fork	25.9	14.8	knife	10.9	5.6
spoon	8.7	4.1	bowl	42.8	29.4
banana	35.5	18.5	apple	19.4	12.9
sandwich	46.7	33.4	orange	35.2	25.9
broccoli	36.4	19.1	carrot	30.9	17.8
hot dog	42.7	29.3	pizza	61.0	44.9
donut	47.3	34.0	cake	39.9	24.4
chair	28.8	16.1	couch	60.5	42.6
potted plant	29.0	15.3	bed	63.3	46.0
dining table	39.6	27.5	toilet	71.3	55.3
tv	66.5	48.1	laptop	62.6	46.9
mouse	63.5	44.1	remote	19.8	10.3
keyboard	62.1	41.5	cell phone	33.7	22.8
microwave	54.9	39.6	oven	48.1	30.4
toaster	30.0	16.4	sink	44.5	27.8
refrigerator	63.2	46.1	book	18.4	7.3
clock	57.8	35.8	vase	33.7	22.1
scissors	27.8	17.8	teddy bear	54.1	35.4
hair drier	2.9	1.1	toothbrush	13.1	8.2

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

• Nanodet: https://zhuanlan.zhihu.com/p/306530300
• Nanodet Plus: https://zhuanlan.zhihu.com/p/449912627
• Nanodet weight and scripts for training: https://github.com/RangiLyu/nanodet