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If the files are in a folder named "detectors", the first file in the folder is used for training.
Objective: The objective is to develop a project for detection of objects (cars, persons, vehicles) with the help of machine learning.
Step 1: Before we start the data collection, we need to decide the specific objects (detectors) that we want to detect.
Step 2: We need to compile data from every object of the dataset.
Step 3: Train the model, check the accuracy of the training.
Step 4: Select the model which has the highest accuracy.
Step 5: Create a model with the selected model and run the model.
Step 6: Input data from cameras, check accuracy of the model.
Example model.
Problem 3: Detect Object at the Road Side.
Task 1: Given the image below as input. The task is to detect the vehicle in the middle of the road. The dataset contains 100 pictures of road sides.
Image 1: Input picture 1. (For the best accuracy, please use the image with the car on the right side. If you don't have this image, please use image with a person on the right side. )
Input file location: /home/user/folder/in.jpg
Problem 4: Train Detection Model.
Task 2: Use the models of "detect objects (cars, persons, vehicles)" to train a model to detect object of the specific type.
Step 1: Start the code with the command line argument -s=s/: the dataset folder.
Step 2: Check the accuracy of the model.
Step 3: Upload the model to Kaggle Notebooks.
Problem 5: Model export
Task 3: Export the model to Kaggle Notebooks for others to use.
Step 1: Start the code with the command line argument -s=s/: the folder containing the model.
Step 2: Upload the model.
Problem 6: Training model (detect objects)
Task 4: We train the model to detect the objects of the specific type (cars, persons, vehicles).
Step 1: Run the code with command line argument -s=s/: the folder containing the model.
Step 3: Select the model which has 3125841983

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