yolo-v2-tf

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.

- Download or clone the original repository (tested on
`d38c3d8`

commit). - Use the following commands to get original model (named
`yolov2`

in repository) and convert it to Keras* format (see details in the README.md file in the official repository):- Download YOLO v2 weights: ``` wget -O weights/yolov2.weights https://pjreddie.com/media/files/yolov2.weights ```
- Convert model weights to Keras*: ``` python tools/model_converter/convert.py cfg/yolov2.cfg weights/yolov2.weights weights/yolov2.h5 ```

- Convert model to protobuf: ``` python tools/model_converter/keras_to_tensorflow.py –input_model weights/yolov2.h5 –output_model=weights/yolo-v2.pb ```

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 size`H`

- height`W`

- width`C`

- channel

Channel order is `RGB`

. Scale value - 255.

Image, name - `image_input`

, shape - `1,3,608,608`

, format is `B,C,H,W`

where:

`B`

- batch size`C`

- channel`H`

- height`W`

- width

Channel 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 size`Cx`

,`Cy`

- cell index`N`

- number of detection boxes for cell

Detection 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 cell `h`

,`w`

- raw height and width of box, apply exponential function and multiply by corresponding anchors to get height and width values relative to the cell`box_score`

- confidence of detection box, apply sigmoid function to get confidence in [0,1] range`class_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 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 size`N`

- number of detection boxes for cell`Cx`

,`Cy`

- cell index

Detection 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 cell `h`

,`w`

- raw height and width of box, apply exponential function and multiply with corresponding anchors to get height and width values relative to the cell`box_score`

- confidence of detection box in [0,1] range`class_no_1`

,...,`class_no_80`

- probability distribution over the classes in the [0,1] range, multiply by confidence value to get confidence of each class

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 original model is distributed under the following license:

MIT License

Copyright (c) 2019 david8862

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.

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. ```