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% Document Info
\author{massontl}
\pdfinfo{
  /Title (a09-itai-1378-cv.pdf)
  /Creator (Cheatography)
  /Author (massontl)
  /Subject (A09 ITAI 1378 CV Cheat Sheet)
}

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        \hspace*{-6pt}\includegraphics[width=5.8cm]{/web/www.cheatography.com/public/images/cheatography_logo.pdf}}
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    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{A09 ITAI 1378 CV Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{massontl} via \textcolor{DarkBackground}{\uline{cheatography.com/208718/cs/44777/}}}
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  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}massontl \\
  \uline{cheatography.com/massontl} \\
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Not Yet Published.\\
   Updated 24th October, 2024.\\
   Page {\thepage} of \pageref{LastPage}.
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  %\includegraphics[width=48px,height=48px]{dave.jpeg}
  Measure your website readability!\\
  www.readability-score.com
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\begin{document}
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\begin{multicols*}{3}

\begin{tabularx}{5.377cm}{X}
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Basics of Object Detection}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{{\bf{Object Detection.}} A computer vision technique used to identify and locate objects in an image or video with bounding boxes.} \tn 
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\mymulticolumn{1}{x{5.377cm}}{{\bf{Annotations.}} Labeled data (images and their corresponding bounding boxes) used to train object detection models.} \tn 
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% Row 2
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\mymulticolumn{1}{x{5.377cm}}{{\bf{Intersection over Union (IoU).}} Measures the overlap between the predicted bounding box and the ground-truth bounding box.} \tn 
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% Row 3
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\mymulticolumn{1}{x{5.377cm}}{{\bf{Bounding Boxes.}} Rectangles drawn around detected objects, represented as (x\_min, y\_min, x\_max, y\_max).} \tn 
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% Row 4
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\mymulticolumn{1}{x{5.377cm}}{{\bf{Confidence Scores.}} Probability score representing the model's confidence in detecting an object.} \tn 
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\mymulticolumn{1}{x{5.377cm}}{{\bf{IoU}} = Area of Overlap / Area of Union {\bf{Higher IoU indicates better accuracy in object localization.}}} \tn 
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\end{tabularx}
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Challenges in Object Detection}}  \tn
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Objects may vary in size, position, and rotation. & Part of the object might be hidden or blocked by another object. \tn 
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Some classes may have fewer examples than others, affecting model performance. & Trade-off between detection accuracy and speed for real-time applications. \tn 
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% Row 2
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\mymulticolumn{2}{x{5.377cm}}{Model performs well on training data but poorly on unseen data.} \tn 
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\begin{tabularx}{5.377cm}{X}
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Common Object Detection Algorithms}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{{\bf{R-CNN (Region-based Convolutional Neural Network).}} Uses Selective Search to generate region proposals. Extracts features using CNN and applies classifiers to each region.} \tn 
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% Row 1
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\mymulticolumn{1}{x{5.377cm}}{{\bf{Fast R-CNN.}}  Improves R-CNN by using a shared CNN feature map for all region proposals. Introduces the RoI (Region of Interest) pooling layer.} \tn 
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% Row 2
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\mymulticolumn{1}{x{5.377cm}}{{\bf{Faster R-CNN.}} Replaces Selective Search with a Region Proposal Network (RPN). Achieves faster region proposal generation.} \tn 
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\mymulticolumn{1}{x{5.377cm}}{{\bf{SSD (Single Shot MultiBox Detector).}} Detects objects in a single forward pass. Uses feature maps from multiple layers for detecting objects of various sizes.} \tn 
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\mymulticolumn{1}{x{5.377cm}}{{\bf{YOLO (You Only Look Once).}} Treats object detection as a single regression problem. Divides the image into a grid and predicts bounding boxes and class probabilities directly. Known for speed and real-time performance.} \tn 
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\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Tools and Libraries Overview (1/2)}}  \tn
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{\bf{Tensorflow}} & {\bf{Keras}} \tn 
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Installation: pip install tensorflow & Installation: pip install keras \tn 
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Widely used for creating custom object detection models. & Provides high-level APIs to build and train models. \tn 
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 & Example: model.fit(x\_train, y\_train, epochs=10) \tn 
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\begin{tabularx}{5.377cm}{X}
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Object Detection Workflow}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{Step 1: Data Collection and Annotation.} \tn 
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\mymulticolumn{1}{x{5.377cm}}{Step 2: Data Preprocessing (resizing, normalization).} \tn 
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\mymulticolumn{1}{x{5.377cm}}{Step 3: Model Selection (R-CNN, SSD, YOLO, etc.).} \tn 
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\mymulticolumn{1}{x{5.377cm}}{Step 4: Model Training (using frameworks like TensorFlow or PyTorch).} \tn 
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\mymulticolumn{1}{x{5.377cm}}{Step 5: Model Evaluation (using metrics like IoU, precision, recall).} \tn 
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\mymulticolumn{1}{x{5.377cm}}{Step 6: Model Optimization (hyperparameter tuning, model pruning).} \tn 
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\mymulticolumn{1}{x{5.377cm}}{Step 7: Deployment (integrate the model into applications).} \tn 
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\mymulticolumn{4}{x{5.377cm}}{\bf\textcolor{white}{Tools and Libraries Overview (2/2)}}  \tn
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{\bf{OpenCV}} & {\bf{PyTorch}} &  &  \tn 
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\seqsplit{Installation:} pip install \seqsplit{opencv-python} & Installation: pip install torch torchvision &  &  \tn 
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Useful for image processing tasks like resizing and \seqsplit{augmentations}. & Known for dynamic computational graphs, making it flexible for research. &  &  \tn 
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Example: \seqsplit{cv2.imread('image.jpg')} & Example: torch.nn.Module for creating custom models. &  &  \tn 
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\hhline{>{\arrayrulecolor{DarkBackground}}----}
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% That's all folks
\end{multicols*}

\end{document}