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We will first set up the Python code to run in a notebook. Next, we will download the custom dataset, and convert the annotations to the Yolov7 format. There are provided helper functions to make it easy to test that the annotations match the images. We will then partition the dataset into training and validation sets. For training, we will then establish several training options, and set up our data and hyper parameter configuration files. We will then walk through how we can modify the network architecture as needed for our custom data. Once set up is complete, we will show how to train the model for 100 epochs, and then use it to generate predictions on our validation set. We will then use the results to compute the mAP on the test data, so that we may assess the quality of the final model.
To download the dataset from Kaggle for a Gradient Notebook, first get a Kaggle account. Next, create an API token by going to your Account settings, and save kaggle.json to your local machine. Upload kaggle.json to the Gradient Notebook. Finally, run the code cell below to download the zip file. 781b155fdc