Project_23_1_RCNN_Architecture.ipynb

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      "source": [
        "## Q1. What are the objectives of using Selective Search in R-CNN"
      ],
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      "cell_type": "markdown",
      "source": [
        "Selective Search is a region proposal method in which pixels that are closer and have similar colour and shape get grouped together.\n",
        "\n",
        "The objectives of using Selective Search in R-CNN (Region-based Convolutional Neural Network) are:\n",
        "\n",
        "1. **Region Proposal**: Selective Search generates a set of candidate object regions or bounding boxes in an image. These regions serve as proposals for potential objects.\n",
        "\n",
        "2. **Efficient Computation**: It reduces the computational burden by pre-selecting a limited number of promising regions, which makes it more efficient to process images with a large number of potential objects.\n",
        "\n",
        "3. **Handling Objects at Different Scales**: Selective Search can generate object region proposals at various scales and aspect ratios. This is important for detecting objects of different sizes and shapes in the image.\n",
        "\n",
        "4. **Region Diversity**: It aims to produce diverse region proposals by considering multiple low-level image cues, such as color, texture, and shape. This diversity helps ensure that object regions with different visual characteristics are included in the proposal set.\n",
        "\n",
        "5. **Flexibility**: Selective Search can be used with various CNN architectures and can be combined with pretrained models for feature extraction, allowing for flexibility in the overall object detection system.\n",
        "\n",
        "6. **Improved Object Localization**: Selective Search tends to provide accurate object region proposals, which helps in localizing objects more precisely, leading to better object detection accuracy.\n"
      ],
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      "cell_type": "markdown",
      "source": [
        "## Q2.  Explain the following phases involved in R-CNN\n",
        "\n",
        "- a) Region Proposal\n",
        "- b) Warping and Resizing\n",
        "- c) Pretrained CNN Architecture\n",
        "- d) Pretrained SVM models\n",
        "- e) Cleanup\n",
        "- f) Implementation of bounding box\n"
      ],
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      "source": [
        "R-CNN (Region-based Convolutional Neural Network) is an object detection framework which consistes of the following phases :\n",
        "\n",
        "\n",
        "a) **Region Proposal**:\n",
        "   - In the region proposal phase, potential object regions or bounding boxes in an image are generated. In the original R-CNN, this was typically done using methods like Selective Search or EdgeBoxes. These proposed regions serve as candidates for objects in the image.\n",
        "\n",
        "b) **Warping and Resizing**:\n",
        "   - After region proposal, the regions are cropped from the image and warped to a fixed size (e.g., 224 x 224 pixels) to ensure consistency for further processing. This warping and resizing step prepares the proposed regions for input into a CNN.\n",
        "\n",
        "c) **Pretrained CNN Architecture**:\n",
        "   - R-CNN uses a pretrained Convolutional Neural Network (CNN) as a feature extractor. Common choices include AlexNet, VGG, or other architectures. The CNN processes the warped and resized region proposals to extract feature vectors that represent the content of each region.\n",
        "\n",
        "d) **Pretrained SVM models**:\n",
        "   - Support Vector Machines (SVMs) are trained on the extracted CNN features to classify the contents of each region proposal into different object categories (e.g., dog, car, person). R-CNN uses a separate SVM for each category, and these SVM models are pretrained on a labeled dataset.\n",
        "\n",
        "e) **Cleanup**:\n",
        "   - In the cleanup phase, non-maximum suppression (NMS) is often applied to eliminate duplicate or highly overlapping region proposals. This step ensures that only the most confident and distinct object detections are retained while discarding redundant ones.\n",
        "\n",
        "f) **Implementation of bounding box**:\n",
        "   - After classification, the region proposals that survive the cleanup step are associated with bounding boxes. These bounding boxes are used to localize and label the detected objects in the original image. The bounding boxes are adjusted based on the CNN features to improve the object localization accuracy."
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "## Q3. What are the possible pretrained CNN's we can use in the pretrained CNN architecture ?"
      ],
      "metadata": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "The few possible pretrained CNN's we can use in a pretrained CNN architecture are :\n",
        "\n",
        "**AlexNet**: One of the earliest deep CNN architectures that gained popularity after winning the ImageNet Large Scale Visual Recognition Challenge in 2012.\n",
        "\n",
        "**VGG (Visual Geometry Group)Net**: Known for its simplicity and depth, with variations like VGG16 and VGG19, which have 16 and 19 weight layers, respectively.\n",
        "\n",
        "**GoogLeNet (Inception)**: Introduced the concept of inception modules for efficient use of computational resources. It was the winner of the 2014 ImageNet competition.\n",
        "\n",
        "**ResNet (Residual Network)**: Known for its deep architecture and residual connections that make training very deep networks more manageable. Variants like ResNet-50 and ResNet-101 are popular choices.\n",
        "\n",
        "**MobileNet**: Designed for mobile and embedded vision applications, it's a lightweight CNN architecture that's computationally efficient.\n",
        "\n",
        "**DenseNet**: Stands for Densely Connected Convolutional Networks and is known for its dense connectivity pattern, allowing features to be reused more effectively.\n",
        "\n",
        "**EfficientNet**: Designed to be highly efficient in terms of computational resources while achieving high accuracy. It uses a compound scaling method to balance network depth, width, and resolution."
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Q4. How is SVM implemented in the R-CNN framework ?"
      ],
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      "cell_type": "markdown",
      "source": [
        "In the R-CNN (Region-based Convolutional Neural Network) framework, Support Vector Machines (SVMs) are used as classifiers to categorize the content of region proposals.\n",
        "\n",
        "1. **Feature Extraction**: Before SVM classification, feature vectors are extracted from each region proposal using a pretrained Convolutional Neural Network (CNN) (Eg. AlexNet here each crop will generate 4096 feature vectors because of the output layer structure of AlexNet). These features represent the content of the proposed regions.\n",
        "\n",
        "2. **Training SVMs**:\n",
        "   - For each object category (e.g., \"cat,\" \"dog,\" \"car\"), a separate binary SVM classifier is trained.\n",
        "   - Positive training examples are the feature vectors extracted from region proposals that contain objects of the target category. These regions are labeled as positives.\n",
        "   - Negative training examples are feature vectors from region proposals that do not contain objects of the target category. These regions are labeled as negatives.\n",
        "   - The SVM is trained to learn a decision boundary that separates positive and negative examples effectively.\n",
        "\n",
        "3. **Classifier Output**: After training, each SVM provides a confidence score that indicates the likelihood of a region proposal containing an object of the target category. The score can be interpreted as a measure of object presence.\n",
        "\n",
        "4. **Non-Maximum Suppression (NMS)**: To eliminate duplicate or highly overlapping region proposals, a cleanup step is often performed using non-maximum suppression. This ensures that only the most confident and distinct detections are retained.\n",
        "\n",
        "5. **Bounding Box Refinement**: The final bounding box coordinates are adjusted based on the CNN features and the output of the SVM to improve the localization accuracy. This often involves regression models to refine the bounding box dimensions and coordinates.\n",
        "\n",
        "6. **Object Localization**: Once the cleanup and bounding box refinement are completed, the remaining region proposals with their associated SVM scores and bounding boxes are used to localize and label the detected objects in the original image. The region proposals with high confidence scores are considered as positive detections for a specific object category.\n",
        "\n",
        "In summary, SVMs in the R-CNN framework are used to determine the presence or absence of specific object categories within region proposals. They are trained on extracted CNN features, and their output scores are used to classify and localize objects in images. Non-maximum suppression and bounding box refinement are additional steps to improve the accuracy of object localization and reduce duplicate detections."
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Q5. How does non-maximum suppression works ?"
      ],
      "metadata": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Non-Maximum Suppression (NMS) is a post-processing technique commonly used in object detection to eliminate redundant or overlapping bounding boxes while retaining the most confident and distinct detections. It works by keeping the highest-scoring (most confident) bounding box for each detected object and removing the others.\n",
        "\n",
        "Working of NMS :\n",
        "\n",
        "1. **Input**: NMS takes as input a list of bounding boxes and their associated confidence scores. These bounding boxes are the result of object detection, typically generated by techniques like region proposal methods (e.g., Selective Search in R-CNN) or anchor boxes in modern object detection frameworks.\n",
        "\n",
        "2. **Sort by Confidence**: The list of bounding boxes is first sorted in descending order based on their associated confidence scores. This ensures that the highest-scoring boxes come first.\n",
        "\n",
        "3. **Select the Highest Scoring Box**: The bounding box with the highest confidence score is selected as the first detection. This box is considered the most likely to contain the object.\n",
        "\n",
        "4. **Remove Overlapping Boxes**: Starting from the second-highest scoring box in the sorted list, NMS compares each box to the currently selected highest-scoring box. Boxes that have a significant overlap (intersection over union, IoU) with the selected box are removed. The IoU is a measure of how much two bounding boxes overlap. If the IoU is above a certain threshold (commonly 0.5), the box is considered redundant and is removed.\n",
        "\n",
        "5. **Repeat**: Steps 3 and 4 are repeated for the remaining boxes, selecting the highest-scoring box from those that haven't been removed and removing overlapping boxes. This process continues until all boxes have been considered.\n",
        "\n",
        "6. **Output**: The final output of NMS is a reduced list of bounding boxes, typically with the highest confidence scores, and without significant overlaps. These are the retained detections after eliminating duplicates and redundant bounding boxes."
      ],
      "metadata": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Q6. How Fast-RCNN is better than RCNN ?"
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Fast R-CNN is an improvement over the original R-CNN (Region-based Convolutional Neural Network) in various ways such as :\n",
        "\n",
        "1. **End-to-End Training**:\n",
        "   - In R-CNN, the feature extraction (using a pretrained CNN) and object classification (using SVMs) were separate stages. In Fast R-CNN, both stages are combined into a single, end-to-end trainable network. This simplifies the training process and allows for joint optimization.\n",
        "\n",
        "2. **Region of Interest (RoI) Pooling**:\n",
        "   - Fast R-CNN introduced RoI pooling, which efficiently extracts fixed-size feature maps from the CNN for each region proposal. This eliminates the need for warping and resizing region proposals as in R-CNN, resulting in faster processing.\n",
        "\n",
        "3. **Shared Convolutional Features**:\n",
        "   - R-CNN extracted features independently for each region proposal, which was computationally expensive. Fast R-CNN shares convolutional features across all proposals, leading to significant speed improvements.\n",
        "\n",
        "4. **Single Forward Pass**:\n",
        "   - In R-CNN, each region proposal was passed through the CNN separately, resulting in a large number of redundant computations. In Fast R-CNN, all region proposals are processed in a single forward pass, making it much faster.\n",
        "\n",
        "5. **Improved Localization**:\n",
        "   - Fast R-CNN includes a bounding box regression head that refines the location of detected objects, resulting in better object localization accuracy compared to R-CNN.\n",
        "\n",
        "6. **Reduced Computation Time**:\n",
        "   - Fast R-CNN's sharing of convolutional features, single forward pass, and RoI pooling results in a significant reduction in computation time compared to R-CNN.\n",
        "\n"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "## Q7. Using Mathematical Intution explain ROI pooling in Fast - RCNN ?"
      ],
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      "cell_type": "markdown",
      "source": [
        "Region of Interest (RoI) pooling is a crucial component of the Fast R-CNN architecture that allows you to extract fixed-size feature maps from variable-sized region proposals. It ensures that the features from these regions can be used for subsequent classification and regression tasks.\n",
        "\n",
        "Imagine you have a feature map from the last convolutional layer of your CNN, and you want to extract features from a region proposal, which is represented as a bounding box. The goal is to transform the features inside this arbitrary-sized bounding box into a fixed-sized grid that can be fed into fully connected layers for object classification and bounding box regression.\n",
        "\n",
        "Working of ROI Pooling :\n",
        "\n",
        "1. **Input Features**: You have a feature map with dimensions H x W (height x width). This is essentially a 2D grid of features.\n",
        "\n",
        "2. **Region Proposal**: You have a region proposal, which is represented as a bounding box. Let's say this bounding box has coordinates (x, y, x', y'). The goal is to extract features from this bounding box.\n",
        "\n",
        "3. **Partition into Grid**: The first step is to divide the bounding box into a fixed-size grid. Suppose you want a grid of size GxG, meaning you want to extract GxG features from the bounding box.\n",
        "\n",
        "4. **Quantize Grid Cells**: You quantize the coordinates of the grid cells so that each grid cell corresponds to a specific location on the feature map. This is done by mapping the coordinates (x, y, x', y') to indices on the feature map.\n",
        "\n",
        "5. **Subdivide Grid Cells**: If a grid cell corresponds to a fractional part of a feature map pixel, you need to further subdivide the cell into sub-cells and allocate a portion of the feature value to each sub-cell based on the overlap.\n",
        "\n",
        "6. **Pooling Operation**: Within each quantized grid cell, you perform a pooling operation (usually max pooling) over the corresponding portion of the feature map. This pooling operation reduces the features within the bounding box to a fixed-size grid of features, regardless of the size and aspect ratio of the bounding box.\n",
        "\n",
        "7. **Output**: The result is a fixed-size grid of features, typically represented as a GxG grid, which can be directly used for subsequent classification and regression tasks.\n",
        "\n",
        "The RoI Pooling process ensures that you extract features from the bounding box while considering its size, shape, and position on the feature map. RoI pooling allows you to adapt the features from arbitrary regions of an image to a consistent format suitable for further processing, making it a key component in the Fast R-CNN architecture."
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Q8. Explain the following processes\n",
        "\n",
        "- a) ROI Projection\n",
        "- b) ROI Pooling"
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "a) **ROI Projection**:\n",
        "   - ROI Projection is the process of mapping region proposals (ROIs) from the original image space onto the feature map space. It aligns the ROIs with the feature map for subsequent processing, typically involving coordinate transformations.\n",
        "\n",
        "b) **ROI Pooling**:\n",
        "   - ROI pooling is a technique that extracts fixed-size feature maps from variable-sized region proposals. It divides the region into a grid, quantizes the grid cells to the feature map, and performs pooling (e.g., max pooling) within each cell to obtain a consistent feature representation for object detection."
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Q9. In Comparision with RCNN, why did the object classifier activation function change in Fast RCNN?"
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "The object classifier activation function changed in Fast R-CNN compared to R-CNN due to the shift from using Support Vector Machines (SVMs) to softmax-based classifiers.\n",
        "\n",
        "1. **End-to-End Training**: In Fast R-CNN, the entire model, including feature extraction, region proposal, and object classification, is trained end-to-end in a single neural network. In contrast, R-CNN used SVMs, which were trained separately. Using softmax-based classifiers in Fast R-CNN allows for seamless end-to-end training, simplifying the training pipeline.\n",
        "\n",
        "2. **Simplification of the Pipeline**: The use of softmax classifiers simplifies the object detection pipeline. R-CNN had a multi-stage process involving region proposals, feature extraction, SVM classifiers, and bounding box regression. Fast R-CNN streamlines this process by using softmax classifiers, making the implementation and training more straightforward.\n",
        "\n",
        "3. **Gradient Flow**: SVM classifiers are not end-to-end differentiable, which means that gradients cannot flow through the SVM loss function. In contrast, softmax-based classifiers are differentiable, allowing for gradient flow through the entire network during training. This is important for effective optimization and the ability to fine-tune the Convolutional Neural Network (CNN) for object detection.\n",
        "\n",
        "4. **Scalability**: SVM classifiers require a separate model for each object category. This can become impractical when dealing with a large number of object categories. Softmax-based classifiers are more efficient in handling multiple classes, making the system more scalable.\n",
        "\n",
        "5. **Consistency in Training**: Using softmax classifiers in Fast R-CNN allows for the use of the same classification loss function that is employed in other deep learning tasks, such as image classification. This consistency simplifies the training process and leverages established training techniques and tools.\n"
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "## Q10. What are the major changes in the Faster RCNN compared to Fast RCNN"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "The major changes in Faster R-CNN compared to Fast R-CNN are:\n",
        "\n",
        "1. **Region Proposal Network (RPN)**: Faster R-CNN introduces the RPN within the same network, enabling efficient end-to-end training and eliminating the need for external region proposal methods.\n",
        "\n",
        "2. **Anchor Boxes**: It uses anchor boxes to propose object locations with different scales and aspect ratios, improving object detection flexibility.\n",
        "\n",
        "3. **Single Network**: Faster R-CNN integrates region proposal and object detection into a single network, improving speed and efficiency.\n",
        "\n",
        "4. **Improved Speed**: Faster R-CNN is faster in both training and inference, thanks to shared convolutional layers between RPN and object detection."
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Q11. Explain the concept of anchor boxes ?"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Anchor boxes, are a key component in object detection algorithms, such as Faster-RCNN and YOLO. They are pre-defined boxes of various sizes and aspect ratios that are used to detect objects of different shapes and sizes in an image. The network predicts the presence of objects within these anchor boxes and refines their positions. By using anchor boxes, the algorithm can efficiently detect and classify objects at multiple scales and aspect ratios, making it a fundamental technique in object detection tasks."
      ],
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