Develop an object detection model using the PASCAL VOC dataset. The goal is to correctly identify and

localize various objects in images from $20$ different classes including animals, vehicles, and household

objects.

Dataset Preparation

Download the PASCAL VOC $2012$ dataset.

Understand the structure of the dataset, including the annotations for object bounding

boxes.

Model Selection

Choose a suitable convolutional neural network $($CNN$)$ architecture for object detection,

such as SSD$,$ YOLO, or Faster R$-$CNN$.$

Explain your choice of model and how it works for object detection.

Training

Implement the chosen model using a deep learning framework like TensorFlow or PyTorch.

Train your model on the training part of the dataset.

Evaluation

Validate your model on the validation set provided by the PASCAL VOC dataset.

Use appropriate metrics such as Mean Average Precision $($mAP$)$ to evaluate the model.

Reporting

Discuss the challenges faced during the implementation and training processes.

Analyze the performance of your model. Provide visual examples of object detection from

the validation set.