This project implements basic object detection framework using python.
- Download Caltech-101
- Download houses : This dataset will be used as the negative images
- Extract dataset files.
- Locate dataset directory as the following structure.
|--- [Project Directory]
|--- [datasets]
|--- [caltech101]
|--- [101_ObjectCategories]
|--- [Annotations]
|--- [houses]
Run "0_check_dataset_path.py". If you can find the following message, you can go to the next step.
c:\object-detector>python 0_check_dataset_path.py
Positive dataset location is correct
Negative dataset location is correct
Run "main.py". It will automatically build object-detector with the 7-step framework.
After finishing step 6, you can see the following test image and recognized bounding box.
In the case of using faces.conf we can see the following test image and recognized bounding box.
AP(Average Precision) is a measure of evaluating object detector. After finishing step 7, object detector's average precision score will be printed like this
Average Precision : 0.937441470843
It is also displayed precision-recall curve.
In the project it is provided 2 detectors. It is face-detector and carside-detector. It can easily build other 99 object detectors in the Caltech-101. It needs just 3-steps.
- Write configuration file like
Project/Directory/conf/car_side.conf
orProject/Directory/conf/faces.conf
- In the main.py change CONFIG_FILE parameter
- Run the main.py