Project Description: The goal of this project is to develop a deep learning model that can accurately detect and classify objects in images. The model will use convolutional neural networks (CNNs) to automatically learn features from the input images and identify the objects present in them.
Dataset: The model will be trained and tested on a dataset of images containing various objects such as cars, bicycles, people, animals, etc. The dataset will be labeled with bounding boxes around each object to indicate its location and class label.
Model Architecture: The model will be designed using a deep CNN architecture such as VGG, ResNet, or Inception. The model will consist of several convolutional and pooling layers to extract features from the input images, followed by several fully connected layers for classification.
Training: The model will be trained using a combination of stochastic gradient descent and backpropagation algorithms. The training process will involve feeding batches of images to the model, computing the loss function, and updating the model parameters to minimize the loss.
Testing: After training, the model will be evaluated on a separate test set of images to measure its accuracy and performance. The evaluation metrics will include precision, recall, F1-score, and mean average precision (mAP).
Results: The final output of the project will be a deep learning model that can accurately detect and classify objects in images. The model can be deployed in various applications such as autonomous driving, surveillance, and robotics.