opencv/samples/dnn

OpenCV deep learning module samples

Model Zoo

Object detection

Model	Scale	Size WxH	Mean subtraction	Channels order
MobileNet-SSD, Caffe	0.00784 (2/255)	300x300	127.5 127.5 127.5	BGR
OpenCV face detector	1.0	300x300	104 177 123	BGR
SSDs from TensorFlow	0.00784 (2/255)	300x300	127.5 127.5 127.5	RGB
YOLO	0.00392 (1/255)	416x416	0 0 0	RGB
VGG16-SSD	1.0	300x300	104 117 123	BGR
Faster-RCNN	1.0	800x600	102.9801 115.9465 122.7717	BGR
R-FCN	1.0	800x600	102.9801 115.9465 122.7717	BGR
Faster-RCNN, ResNet backbone	1.0	300x300	103.939 116.779 123.68	RGB
Faster-RCNN, InceptionV2 backbone	0.00784 (2/255)	300x300	127.5 127.5 127.5	RGB

Face detection

An origin model with single precision floating point weights has been quantized using TensorFlow framework. To achieve the best accuracy run the model on BGR images resized to 300x300 applying mean subtraction of values (104, 177, 123) for each blue, green and red channels correspondingly.

The following are accuracy metrics obtained using COCO object detection evaluation tool on FDDB dataset (see script) applying resize to 300x300 and keeping an origin images' sizes.

AP - Average Precision                            | FP32/FP16 | UINT8          | FP32/FP16 | UINT8          |
AR - Average Recall                               | 300x300   | 300x300        | any size  | any size       |
--------------------------------------------------|-----------|----------------|-----------|----------------|
AP @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] | 0.408     | 0.408          | 0.378     | 0.328 (-0.050) |
AP @[ IoU=0.50      | area=   all | maxDets=100 ] | 0.849     | 0.849          | 0.797     | 0.790 (-0.007) |
AP @[ IoU=0.75      | area=   all | maxDets=100 ] | 0.251     | 0.251          | 0.208     | 0.140 (-0.068) |
AP @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.050     | 0.051 (+0.001) | 0.107     | 0.070 (-0.037) |
AP @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.381     | 0.379 (-0.002) | 0.380     | 0.368 (-0.012) |
AP @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.455     | 0.455          | 0.412     | 0.337 (-0.075) |
AR @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] | 0.299     | 0.299          | 0.279     | 0.246 (-0.033) |
AR @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] | 0.482     | 0.482          | 0.476     | 0.436 (-0.040) |
AR @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] | 0.496     | 0.496          | 0.491     | 0.451 (-0.040) |
AR @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.189     | 0.193 (+0.004) | 0.284     | 0.232 (-0.052) |
AR @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.481     | 0.480 (-0.001) | 0.470     | 0.458 (-0.012) |
AR @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.528     | 0.528          | 0.520     | 0.462 (-0.058) |

Classification

Model	Scale	Size WxH	Mean subtraction	Channels order
GoogLeNet	1.0	224x224	104 117 123	BGR
SqueezeNet	1.0	227x227	0 0 0	BGR

Semantic segmentation

Model	Scale	Size WxH	Mean subtraction	Channels order
ENet	0.00392 (1/255)	1024x512	0 0 0	RGB
FCN8s	1.0	500x500	0 0 0	BGR

References

• Models downloading script
• Configuration files adopted for OpenCV
• How to import models from TensorFlow Object Detection API
• Names of classes from different datasets