YOLO v3 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).yolov3
in repository) and convert it to Keras* format (see details in the README.md file in the official repository):Metric | Value |
---|---|
Type | Detection |
GFLOPs | 65.984 |
MParams | 61.922 |
Source framework | Keras* |
Accuracy metrics obtained on COCO* validation dataset for converted model.
Metric | Value |
---|---|
mAP | 62.27% |
COCO* mAP | 67.7% |
Image, name - input_1
, shape - 1,416,416,3
, format is B,H,W,C
where:
B
- batch sizeH
- heightW
- widthC
- channelChannel order is RGB
. Scale value - 255.
Image, name - input_1
, shape - 1,3,416,416
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- widthChannel order is BGR
.
conv2d_58/BiasAdd
, shape - 1,13,13,255
. The anchor values are 116,90, 156,198, 373,326
.conv2d_66/BiasAdd
, shape - 1,26,26,255
. The anchor values are 30,61, 62,45, 59,119
.conv2d_74/BiasAdd
, shape - 1,52,52,255
. The anchor values are 10,13, 16,30, 33,23
.For each case 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 relative to the cell coordinatesh
,w
- raw height and width of box, apply exponential function and multiply by corresponding anchors to get absolute height and width valuesbox_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 sigmoid function and multiply by obtained 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.
conv2d_58/BiasAdd/YoloRegion
, shape - 1,255,13,13
. The anchor values are 116,90, 156,198, 373,326
.conv2d_66/BiasAdd/YoloRegion
, shape - 1,255,26,26
. The anchor values are 30,61, 62,45, 59,119
.conv2d_74/BiasAdd/YoloRegion
, shape - 1,255,52,52
. The anchor values are 10,13, 16,30, 33,23
.For each case format is 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 by corresponding anchors to get absolute height and width valuesbox_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.
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: