Vehicle-Detection/Libraries at main · Keval10github/Vehicle-Detection · GitHub

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Libraries Used in Code
1. argparse:
This library is used for parsing command-line arguments. It allows users to specify options and parameters when running the script.

2. logging:
This module provides a flexible framework for emitting log messages from Python programs. It is used here to log information about the training process, such as hyperparameters and training progress.

3. math:
A standard library for mathematical functions. It is used for various mathematical operations, such as calculating sizes and scaling factors.

4. os:
This module provides a way of using operating system-dependent functionality like reading or writing to the file system. It is used for handling file paths and environment variables.

5. random:
This library implements pseudo-random number generators for various distributions. It is used for random sampling, such as selecting images for training.

6. sys:
This module provides access to some variables used or maintained by the interpreter and to functions that interact with the interpreter. It is used here to manipulate the Python path.

7. time:
This module provides various time-related functions. It is used to measure the duration of the training process.

8. copy:
The deepcopy function from this module is used to create deep copies of objects, ensuring that changes to the copy do not affect the original object.

9. pathlib:
This module offers classes to handle filesystem paths in an object-oriented way. It is used to manage file paths more conveniently.

10. numpy:
A fundamental package for scientific computing in Python. It is used for handling arrays and performing numerical operations.

11. torch:
The main PyTorch library, which is used for building and training neural networks. It provides functionalities for tensor operations, automatic differentiation, and GPU acceleration.

12. yaml:
A library for parsing YAML (YAML Ain't Markup Language) files. It is used to read configuration files that specify hyperparameters and dataset information.

13. tqdm:
A library for creating progress bars in Python. It is used to provide visual feedback during the training process.

14. val:
A custom module (not a standard library) that is likely used for validation purposes, specifically to calculate mean Average Precision (mAP) at the end of each epoch.

15. utils.datasets:
A custom utility module for creating data loaders for training and validation datasets.

16. utils.general:
A custom utility module that contains various general-purpose functions used throughout the training process.

17. utils.downloads:
A custom utility module for handling downloads of model weights or datasets.

18. utils.loss:
A custom utility module that likely contains the loss computation functions used during training.

19. utils.plots:
A custom utility module for plotting functions, such as visualizing labels and training evolution.