YOLO v2 is a real-time object detection model implemented with Keras* from this repository and converted to TensorFlow* framework. This model was pretrained on COCO* dataset with 80 classes.
d38c3d8
commit).yolov2
in repository) and convert it to Keras* format (see details in the README.md file in the official repository):Metric | Value |
---|---|
Type | Detection |
GFLOPs | 63.03 |
MParams | 50.95 |
Source framework | Keras* |
Accuracy metrics obtained on COCO* validation dataset for converted model.
Metric | Value |
---|---|
mAP | 53.15% |
COCO* mAP | 56.5% |
Image, name - image_input
, shape - 1,608,608,3
, format is B,H,W,C
where:
B
- batch sizeH
- heightW
- widthC
- channelChannel order is RGB
. Scale value - 255.
Image, name - image_input
, shape - 1,3,608,608
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- widthChannel order is BGR
.
The array of detection summary info, name - conv2d_22/BiasAdd
, shape - 1,19,19,425
, format is B,Cx,Cy,N*85
where
B
- batch sizeCx
, Cy
- cell indexN
- number of detection boxes for cellDetection box has format [x
,y
,h
,w
,box_score
,class_no_1
, ..., class_no_80
], where:
x
,y
) - raw coordinates of box center, apply sigmoid function to get coordinates relative to the cellh
,w
- raw height and width of box, apply exponential function and multiply by corresponding anchors to get height and width values relative to the cellbox_score
- confidence of detection box, apply sigmoid function to get confidence in [0,1] rangeclass_no_1
,...,class_no_80
- probability distribution over the classes in logits format, apply softmax function and multiply by obtained confidence value to get confidence of each class.The model was trained on Microsoft* COCO dataset version with 80 categories of object. Mapping to class names provided in <omz_dir>/data/dataset_classes/coco_80cl.txt
file. The anchor values are 0.57273,0.677385, 1.87446,2.06253, 3.33843,5.47434, 7.88282,3.52778, 9.77052,9.16828
.
The array of detection summary info, name - conv2d_22/BiasAdd/YoloRegion
, shape - 1,153425
, which could be reshaped to 1,425,19,19
with format B,N*85,Cx,Cy
where
B
- batch sizeN
- number of detection boxes for cellCx
, Cy
- cell indexDetection box has format [x
,y
,h
,w
,box_score
,class_no_1
, ..., class_no_80
], where:
x
,y
) - coordinates of box center relative to the cellh
,w
- raw height and width of box, apply exponential function and multiply with corresponding anchors to get height and width values relative to the cellbox_score
- confidence of detection box in [0,1] rangeclass_no_1
,...,class_no_80
- probability distribution over the classes in the [0,1] range, multiply by confidence value to get confidence of each classThe model was trained on Microsoft* COCO dataset version with 80 categories of object. Mapping to class names provided in <omz_dir>/data/dataset_classes/coco_80cl.txt
file. The anchor values are 0.57273,0.677385, 1.87446,2.06253, 3.33843,5.47434, 7.88282,3.52778, 9.77052,9.16828
.
You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.
An example of using the Model Downloader:
An example of using the Model Converter:
The original model is distributed under the following license:
The MIT License (MIT)
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ```