About Repository:

• Basically this Repository is an attempt by me to make it easy for other developers to perform Object dection and Recognition.
  

• This Repository also provides users with weights and configuration files that I have generated for my particular task of detecting the emotions of Tom and Jerry.

• I am also providing images (test_images) to test this model without actually training it.
  

• This Repository has a mechanism for users to test it before they train it so that they can get insights of how the flow of code going on.
  

• Note that the main Aim of this Model is combine at one place the training and testing of the model .
  

• Training of the model is not the motto of this Repo.
  

• Refer to the links Given in below section to train the model.
  

Custom Training:

• Obtain the dataset for the that you wanna detect.

• For training the Model on Colab : Refer this.

• For training the Model on Local Machine either Linux , Windows or MacOS : Refer this.

• After training you should have a weights and configration file with you to proceed with the predictions.

Testing:

• A folder should be present having files similar to obj.data , obj.names , yolov3-tiny-obj.cfg , yolov3-tiny-obj_4000.weights as present under Data_for_colab folder. These files will be with you if you have training of the model.

• run yolorun.py file .

Information about yolorun.py

arguments :

• --image : path to the image file.
  

• --dir_path : path to the directory in which images are kept.

• --weights : path to the .weights file generated from training.

• --configs : path to the .cfg file.

• --labels : path to the .names file.

  Remember that at a time only one of the argument either --image or --dir_path can be used else error will be thrown.

  Examples:

• python yolorun.py --image image.jpg
  

• python yolorun.py --dir_path dirname

  Note : yolo.py (called by yolorun.py) saves the output of the prediction by the model in a text file named submission.txt . If you want you can remove this functionality by following the instructions in yolo.py , line - 23

Conclusions:

• This Repo is my best attempt to make object detection and recognition easier and would be introducing more convinient approaches in future.