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Object Detection

Object detection using ImageAI, all you need to do is

  1. Install Python on your computer system
  2. Install ImageAI and its dependencies
  3. Download the Object Detection model file
  4. Run the sample codes (which is as few as 10 lines)

Now let’s get started.

1. download and install python on your pc.

2. Install ImageAI and dependencies

– Tensorflow

pip install tensorflow==2.4.0

– Others

pip install keras==2.4.3 numpy==1.19.3 pillow==7.0.0 scipy==1.4.1 h5py==2.10.0 matplotlib==3.3.2 opencv-python keras-resnet==0.2.0

Install the ImageAI library

pip install imageai --upgrade

3. Download the RetinaNet model file that will be used for object detection via this link.

Great. Now that you have installed the dependencies, you are ready to write your first object detection code. Create a Python file and give it a name (For example,, and then write the code below into it. Copy the RetinaNet model file and the image you want to detect to the folder that contains the python file.

from imageai.Detection import ObjectDetection
import os
execution_path = os.getcwd()
detector = ObjectDetection()
detector.setModelPath( os.path.join(execution_path , “resnet50_coco_best_v2.1.0.h5”))
detections = detector.detectObjectsFromImage(input_image=os.path.join(execution_path,“image.jpg”),                                   output_image_path=os.path.join(execution_path , “imagenew.jpg”))
for eachObject in detections:
print(eachObject[“name”] , ” : “ , eachObject[“percentage_probability”] )

Then run the code and wait while the results prints in the console. Once the result is printed to the console, go to the folder in which your is and you will find a new image saved. Take a look at a 2 image samples below and the new images saved after detection.

Before Detection:

After Detection:

Console result for above image:

person : 55.8402955532074

person : 53.21805477142334

person : 69.25139427185059

person : 76.41745209693909

bicycle : 80.30363917350769

person : 83.58567953109741

person : 89.06581997871399

truck : 63.10953497886658

person : 69.82483863830566

person : 77.11606621742249

bus : 98.00949096679688

truck : 84.02870297431946

car : 71.98476791381836


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