Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity
Open Source Computer Vision Library https://opencv.org/
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
24312 Commits
7 Branches
126 Tags
3.0 GiB
 
 
 
 
 
 
Tag: Branch: Tree: 014ae55379
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from '014ae55379'
${ noResults }
opencv/samples/dnn
berak 1c75f3d037
dnn: fix typo in object_detection.cpp sample 		6 years ago
..
face_detector Merge pull request #11631 from sparkecho:3.4 7 years ago
CMakeLists.txt
…
README.md documentation: avoid links to 'master' branch from 3.4 maintenance branch (2) 7 years ago
classification.cpp Make Intel's Inference Engine backend is default if no preferable backend is specified. 7 years ago
classification.py Make Intel's Inference Engine backend is default if no preferable backend is specified. 7 years ago
colorization.cpp Make Intel's Inference Engine backend is default if no preferable backend is specified. 7 years ago
colorization.py Make Intel's Inference Engine backend is default if no preferable backend is specified. 7 years ago
custom_layers.hpp EAST: An Efficient and Accurate Scene Text Detector (https://arxiv.org/abs/1704.03155v2) 7 years ago
edge_detection.py
…
fast_neural_style.py Make Intel's Inference Engine backend is default if no preferable backend is specified. 7 years ago
js_face_recognition.html documentation: avoid links to 'master' branch from 3.4 maintenance branch (2) 7 years ago
mobilenet_ssd_accuracy.py Make Intel's Inference Engine backend is default if no preferable backend is specified. 7 years ago
object_detection.cpp dnn: fix typo in object_detection.cpp sample 6 years ago
object_detection.py Set output layers names and types for models in DLDT's intermediate representation 6 years ago
openpose.cpp select the device (video capture) 7 years ago
openpose.py Make Intel's Inference Engine backend is default if no preferable backend is specified. 7 years ago
segmentation.cpp Make Intel's Inference Engine backend is default if no preferable backend is specified. 7 years ago
segmentation.py Make Intel's Inference Engine backend is default if no preferable backend is specified. 7 years ago
shrink_tf_graph_weights.py
…
text_detection.cpp Added ResizeBilinear op for tf (#11050) 7 years ago
tf_text_graph_faster_rcnn.py Faster-RCNN object detection models from TensorFlow 7 years ago
tf_text_graph_ssd.py Enable SSD models from TensorFlow with OpenCL plugin of Intel's Inference Engine 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
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