Open Source Computer Vision Library https://opencv.org/

History

Dmitry Kurtaev 97fec07d96 Support YOLOv3 model from Darknet		7 years ago
..
face_detector	Merge pull request #11236 from dkurt:dnn_fuse_l2_norm	7 years ago
CMakeLists.txt	Update links to OpenCV's face detection network	7 years ago
README.md	Update links to OpenCV's face detection network	7 years ago
classification.cpp	Semantic segmentation sample.	7 years ago
classification.py	Update tutorials. A new cv::dnn::readNet function	7 years ago
colorization.cpp	Minor refactoring in several C++ samples:	7 years ago
colorization.py	Merge pull request #10777 from berak:dnn_colorize_cpp	7 years ago
fast_neural_style.py	Layers for fast-neural-style models: https://github.com/jcjohnson/fast-neural-style	7 years ago
js_face_recognition.html	Update links to OpenCV's face detection network	7 years ago
mobilenet_ssd_accuracy.py	Specific version of MobileNet-SSD from TensorFlow	7 years ago
object_detection.cpp	Support YOLOv3 model from Darknet	7 years ago
object_detection.py	Support YOLOv3 model from Darknet	7 years ago
openpose.cpp	dnn: add an openpose.cpp sample	7 years ago
openpose.py	fixed samples/dnn/openpose.py	7 years ago
segmentation.cpp	Semantic segmentation sample.	7 years ago
segmentation.py	Semantic segmentation sample.	7 years ago
shrink_tf_graph_weights.py	Text TensorFlow graphs parsing. MobileNet-SSD for 90 classes.	7 years ago
tf_text_graph_ssd.py	Fix minimal aspect ratio scale for SSDs from TensorFlow	7 years ago

README.md

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

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