yolo-v1-tiny-tf

## Use Case and High-Level Description

YOLO v1 Tiny is a real-time object detection model from TensorFlow.js* framework. This model was pretrained on VOC* dataset with 20 classes.

## Conversion

0. Install additional dependencies:  h5py keras tensorflowjs 

 Download model from [here](https://github.com/shaqian/tfjs-yolo-demo/tree/master/dist/model/v1tiny) (tested onaa4354ccommit). Convert model to Keras\* format usingtensorflowjs_converterscript, e.g.:  tensorflowjs_converter –input_format tfjs_layers_model –output_format keras <model_in>.json <model_out>.h5  Convert the produced model to protobuf format. Get conversion script from repository: buildoutcfg git clone https://github.com/amir-abdi/keras_to_tensorflow  (Optional) Checkout the commit that the conversion was tested on:  git checkout c841508a88faa5aa1ffc7a4947c3809ea4ec1228  Applykeras_to_tensorflow.py.patch<tt>:  git apply keras_to_tensorflow.py.patch  Run script:  python keras_to_tensorflow.py –input_model=<model_in>.h5 –output_model=<model_out>.pb  
 

## Specification

 

Metric Value
Type Detection
GFLOPs 6.988
MParams 15.858
Source framework TensorFlow.js*
 

## Accuracy

 

Accuracy metric obtained on VOC2012* validation dataset for converted model.

Metric Value
mAP 72.17%
 

## Input

 

### Original model

 

Image, name - input_1, shape - 1,416,416,3, format is B,H,W,C where:

 B - batch size H - height W - width C - channel 
 

Channel order is RGB. Scale value - 255.

### Converted model

 

Image, name - input_1, shape - 1,3,416,416, format is B,C,H,W where:

 B - batch size C - channel H - height W - width 
 

Channel order is BGR.

## Output

 

### Original model

 

The array of detection summary info, name - conv2d_9/BiasAdd, shape - 1,13,13,125, format is B,Cx,Cy,N*25 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_20], 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 cell box_score - confidence of detection box, apply sigmoid function to get confidence in [0,1] range class_no_1,...,class_no_20 - 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 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52.

### Converted model

 

The array of detection summary info, name - conv2d_9/BiasAdd/YoloRegion, shape - 1,21125, which could be reshaped to 1, 125, 13, 13, format is B,N*25,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_20], 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_20 - probability distribution over the classes in the [0,1] range, multiply by confidence value to get confidence of each class 
 

The anchor values are 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52.

## Legal Information

 

The original model is distributed under the following license:

Copyright (c) 2018 Qian Sha
 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.