Skip to content

Latest commit

 

History

History
120 lines (97 loc) · 4.61 KB

README.md

File metadata and controls

120 lines (97 loc) · 4.61 KB
Raw

Visual-Wake-Word

Visual wake word is a simple case where we have to detect the presence of a person inside an image frame. Please clone the repo using the following commands

git clone https://github.com/L-A-Sandhu/Visual-Wake-Word.git

The rest of the repository is divided as follows.

  1. Requirements
  2. Data-Set Prepration
  3. Fine Tunning
  4. Pre-trained Model
  5. Results

Requirements

This repository requires

  • tensorflow
  • matplotlib
  • scipy
  • protobuf

This work requires to build two different environments one for data prepration and other for training and testing of Mobile Net and inception net on the preapared data.

Building Environment for data prepration

please follow the following set of commands

cd Visual-Wake-Word/
cd data-prep/
conda create env -n <environment -name> python==3.9.12
conda activate <environment-name>
pip install -r requirements.txt

Building Environment for Testing and Traning

Please follow the following set of ommands

cd ../
conda create env -n <environment -name> python==3.7.4
conda activate <environment-name>
pip install -r requirements.txt

Data Set Prepration

The dataset used in this case is actually drived from the MS coco 2014 dataset. Although it is a huge dataset however we have used Yolov3 to divide images in to two classes named as

  1. Person
  2. No-Person

To divide the actual data set in these two classes with the help of yolov3 we have made some changes in detect.py. Follow these steps to prepare the dataset.

conda activate < Environment for data prepration >
cd ./data-prep/
python detect.py --source <data-set path> --resize=96

Now you can see new folder name data inside the main repository.

Fine Tunning

The keras implementation of Mobile and Inception Net trained these models imagenet dataset. However, to train these model on coustom data set we may use transfer learning . In the comming section we be explaining the training and testing of Mobile and well and Incepion Ne.

cd../<M0bile_Net or Inception_NET>/

Tuning

python <Mobile-Net.py or Inception_NET>  --model_dir=<Location for saving model> --data=<data location> --inp=<tune> --b_s=< Batch size> --e=<epoch>
example command 
python Mobile-Net.py  --model_dir='./checkpoint/' --data='../data/' --inp=tune --b_s=16 --e=100

Testing

python <Mobile-Net.py or Inception_NET>  --model_dir=<Location for saving model> --data=<data location> --inp=<test> 
example command 
python Mobile-Net.py  --model_dir='./checkpoint/' --data='../data/' --inp=test

Training

python <Mobile-Net.py or Inception_NET>  --model_dir=<Location for saving model> --data=<data location> --inp=<train > --b_s=< Batch size> --e=<epoch>
example command 
python Mobile-Net.py  --model_dir='./checkpoint/' --data='../data/' --inp=train --b_s=16 --e=100

Resume

python <Mobile-Net.py or Inception_NET>  --model_dir=<Location for saving model> --data=<data location> --inp=<resume> --b_s=< Batch size> --e=<epoch>
example command 
python Mobile-Net.py  --model_dir='./checkpoint/' --data='../data/' --inp=resume --b_s=16 --e=100

Pretrained Model

Orignally the keras fram work has trained Mobile and Inception Net implementations on image-net dataset. This work has fine tunned it on coustom dataset to find the exsisitance of a person with in an image. You can use this trained model by simpley downloading their checkpoints from the links given below and placing them in side the folders M0bile_Net and Inception_NET respectively.

https://drive.google.com/file/d/1jJ4ZxF5q5tkrmgjVziXJhN3UD21470w9/view?usp=sharing
https://drive.google.com/file/d/1vi4KFKIRsRQ_dkuU90EWfpKd4JxvjHO7/view?usp=sharing

Results and Comparisons

The comparision between Mobile and Inception Net is shown in the following table

Model Parameter Latency Acc. FineTune Acc. Scratch Train Size on disk(MB) FLops
Inception-Net 22,114,850 0.00138 0.78 0.71 273.0 0.681 G
Mobile-Net 3,783,510 0.00091 0.74 0.67 49.8 0.116 G

Confusion matix for Mobile Net

Confusion Matrix Person No-Person
Person 4613 918
No-Person 1541 2958

Confusion Matrix for Inception Net

Confusion Matrix Person No-Person
Person 3061 2740
No-Person 292 4207