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C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000001.png | A comparison of hyperplanes in 2D and 3D space for classification. | [
"hyperplane",
"classification",
"2D vs 3D"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000002.jpg | Visualization of support vector machines separating different classes. | [
"SVM",
"classification",
"hyperplane"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000003.png | SVM margin and hyperplanes visualized with support vectors. | [
"SVM",
"margin",
"hyperplane"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000004.jpg | Explanation of support vector machines with margin illustration. | [
"SVM",
"support vectors",
"margin"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000005.png | Support vector machine with decision boundary and margin. | [
"SVM",
"decision boundary",
"hyperplane"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000006.jpg | Kernel methods in SVM, comparing different kernel approaches. | [
"SVM",
"kernel",
"classification"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000007.png | Optimal hyperplane and support vectors for classification. | [
"SVM",
"optimal hyperplane",
"support vectors"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000008.png | SVM margin showing separation between different class features. | [
"SVM",
"margin",
"features"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000009.png | A detailed explanation of the support vector machine's workings. | [
"SVM",
"support vectors",
"classification"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000010.jpg | Binary classification with hyperplanes and feature separation. | [
"SVM",
"binary classification",
"hyperplane"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000011.png | An introductory guide to transformer-based NLP models and tasks. | [
"NLP",
"transformer",
"models"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000012.png | Visualization of the encoder-decoder architecture in transformers. | [
"transformer",
"encoder-decoder",
"NLP"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000013.png | Architecture of the Vision Transformer (ViT) model. | [
"transformer",
"vision transformer",
"ViT"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000014.png | Illustration of transformer encoders and decoders for translation tasks. | [
"transformer",
"encoder",
"decoder"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000015.png | Transformer encoder and decoder blocks in a neural network. | [
"transformer",
"encoder",
"decoder"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000016.png | Feedforward and attention layers within transformer architecture. | [
"transformer",
"attention",
"feedforward"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000017.png | Breakdown of tokenization and embedding in transformer models. | [
"transformer",
"embedding",
"tokenization"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000018.png | BERT input representation showing token, segment, and position embeddings. | [
"BERT",
"embeddings",
"NLP"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000019.jpg | An overview of transformer architecture for translation tasks. | [
"transformer",
"NLP",
"translation"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000020.jpg | Step-by-step guide illustrating transformers and their components. | [
"transformer",
"guide",
"NLP"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000021.jpg | Multinomial logistic regression showing probabilities across categories. | [
"logistic regression",
"multinomial",
"probabilities"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000022.png | Formula for binary logistic regression illustrating probability computation. | [
"logistic regression",
"formula",
"probability"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000023.png | Visualization of prediction and classification using logistic regression. | [
"logistic regression",
"classification",
"prediction"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000024.png | Comparison between linear and logistic regression models. | [
"logistic regression",
"linear regression",
"models"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000025.png | Sigmoid curve representation of logistic regression for classification. | [
"logistic regression",
"sigmoid",
"classification"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000026.jpg | Visualization of logistic regression using scikit-learn and Python. | [
"logistic regression",
"scikit-learn",
"python"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000027.jpg | Decision boundary illustration in logistic regression classification. | [
"logistic regression",
"decision boundary",
"classification"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000028.png | Logistic regression equation showing sigmoid function for prediction. | [
"logistic regression",
"sigmoid",
"equation"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000029.jpg | Example of logistic regression separating true and false samples. | [
"logistic regression",
"samples",
"classification"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000030.png | A data table comparing the presence and absence of a certain feature. | [
"data table",
"comparison",
"statistics"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000031.jpg | Multiple linear regression showing the relationship between temperature, income, and ice cream sales. | [
"multiple linear regression",
"temperature",
"ice cream sales"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000032.png | Fitted line plot showing weight versus height with a linear regression equation. | [
"fitted line plot",
"weight vs height",
"linear regression"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000033.png | Six variations of linear regression models showing the relationship between time and marks. | [
"linear regression",
"marks vs time",
"models"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000034.png | A scatter plot with a regression line showing the decline of values over time. | [
"regression plot",
"time series",
"decline"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000035.png | Graph showing the relationship between age and price with prediction and confidence bands. | [
"multiple regression",
"age vs price",
"prediction bands"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000036.jpg | Gradient descent visualized in a 3D plot along with different forms of the cost function. | [
"gradient descent",
"cost function",
"3D plot"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000037.png | Comparison between gradient step and Newton step for minimizing cost in optimization. | [
"gradient step",
"Newton step",
"optimization"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000038.png | Visualization of gradient descent showing how the tangent line updates weights toward the global cost minimum. | [
"gradient descent",
"global minimum",
"tangent line"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000039.jpg | Graph showing local and global minimum in an optimization problem using gradient descent. | [
"local minimum",
"global minimum",
"gradient descent"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000040.png | Contour plot visualizing the trajectory of optimization with gradient descent and constraint boundaries. | [
"gradient descent",
"contour plot",
"optimization"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000041.png | Visualization of gradient descent optimization over a convex loss surface. | [
"gradient descent",
"convex",
"optimization"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000042.png | Neural network demonstrating the cost function for digit classification. | [
"neural network",
"classification",
"cost function"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000043.png | Illustration of local and global minima in a loss function curve. | [
"local minimum",
"global minimum",
"loss function"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000044.png | Gradient descent process visualized with respect to cost reduction. | [
"gradient descent",
"cost reduction",
"optimization"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000045.jpg | Stochastic gradient descent shown with initial weight and final global minimum. | [
"stochastic gradient descent",
"global minimum",
"deep learning"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000046.png | Boosting technique illustrated as a sequence of weak learners combining to form a strong model. | [
"boosting",
"weak learners",
"strong model"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000047.jpg | Decision tree showing steps leading to various predictions based on conditions. | [
"decision tree",
"predictions",
"conditions"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000048.png | Visualization of ground truth and decision tree predictions through various iterations. | [
"decision tree",
"iterations",
"ground truth"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000049.png | Random subset testing shown in a decision tree format for cost prediction. | [
"decision tree",
"subset testing",
"cost prediction"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000050.jpg | Visualization of boosting technique combining multiple weak learners into a single strong model. | [
"boosting",
"weak learners",
"machine learning"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000051.jpg | Icon representing an abstract structure of neural networks. | [
"neural network",
"abstract",
"icon"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000052.png | Diagram showcasing the mechanism of multi-head attention and its operations in transformers. | [
"multi-head attention",
"transformers",
"neural networks"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000053.png | Comparison of BERT and RoBERTa architectures for natural language processing tasks. | [
"BERT",
"RoBERTa",
"NLP",
"transformers"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000054.png | Visualization of three attention mechanisms: encoder-decoder attention, encoder self-attention, and masked decoder self-attention. | [
"attention mechanisms",
"encoder-decoder",
"self-attention"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000055.jpg | Diagram comparing unidirectional and bidirectional context in deep learning models. | [
"context",
"unidirectional",
"bidirectional",
"deep learning"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000056.jpg | Schematic representation of a transformer architecture's encoder and decoder blocks. | [
"transformer",
"encoder",
"decoder",
"architecture"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000057.png | Flowchart explaining the structure of a convolutional neural network (CNN). | [
"convolutional neural network",
"CNN",
"deep learning"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000058.png | Abstract visualization of a convolutional neural network with data passing through layers. | [
"convolutional neural network",
"CNN",
"layers"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000059.png | Diagram depicting various components and connections within a complex system, likely representing a neural network or a related structure. | [
"system",
"neural network",
"connections"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000060.png | Detailed visualization of the architecture of a convolutional neural network with multiple layers. | [
"CNN",
"architecture",
"layers"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000061.png | Flowchart of neural network architecture with attention mechanism. | [
"neural network",
"attention",
"temporal modeling",
"deep learning"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000062.jpg | Representation of an RNN cell with input, output, and hidden states. | [
"RNN",
"recurrent neural network",
"cell states"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000063.png | Unrolled view of a recurrent neural network with multiple time steps. | [
"RNN",
"unfolded",
"temporal sequence",
"neural network"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000064.png | Illustration of an LSTM cell showing the internal components and gates. | [
"LSTM",
"gates",
"neural network",
"long short-term memory"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000065.png | Illustration of LSTM with multiple time steps in sequence modeling. | [
"LSTM",
"sequence modeling",
"gates",
"neural network"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000066.png | Visualization of CNN applied for image classification with feature maps and fully connected layers. | [
"CNN",
"image classification",
"feature maps",
"fully connected layers"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000067.png | Detailed illustration of CNN architecture for object recognition tasks. | [
"CNN",
"object recognition",
"deep learning",
"convolution"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000068.png | Diagram showing the process of feature extraction and classification in deep learning. | [
"feature extraction",
"deep learning",
"classification"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000069.png | Neural network diagram illustrating backpropagation and error minimization. | [
"backpropagation",
"error minimization",
"neural network"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000070.jpg | CNN architecture showing convolution, max pooling, and fully connected layers for image classification. | [
"CNN",
"convolution",
"max pooling",
"image classification"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000071.png | Diagram of a bidirectional recurrent neural network showcasing forward and backward propagation. | [
"bidirectional RNN",
"forward propagation",
"backward propagation"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000072.png | Abstract neural network illustration showing multiple interconnected nodes and pathways. | [
"neural network",
"nodes",
"interconnected"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000073.jpg | Training and analysis process for an RNN model, showing input, output, and network structure. | [
"RNN",
"training process",
"network analysis"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000074.jpg | Illustration of an unfolded recurrent neural network showing the flow of information over time. | [
"unfolded RNN",
"information flow",
"neural network"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000075.jpg | Simple neural network structure diagram with multiple interconnected nodes. | [
"neural network",
"structure",
"nodes"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000076.png | Recurrent neural network diagram with time-based input and output representation. | [
"RNN",
"time-based input",
"output"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000077.jpg | Graphical representation of recurrent neural network layers and information flow. | [
"RNN",
"information flow",
"network layers"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000078.png | Visualization of forward and backward propagation in a recurrent neural network. | [
"forward propagation",
"backward propagation",
"RNN"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000079.png | Flow diagram representing an RNN with hidden states and output layers. | [
"RNN",
"hidden states",
"output layers"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000080.jpg | Bidirectional recurrent neural network model illustrating simultaneous forward and backward information flow. | [
"bidirectional RNN",
"forward flow",
"backward flow"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000081.png | Recurrent neural network (RNN) with multiple layers showing sequential input and hidden states. | [
"RNN",
"sequential",
"hidden states"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000082.png | Diagram showcasing a grid of RNNs with interconnected hidden states for advanced learning. | [
"RNN",
"grid",
"hidden states"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000083.png | Basic structure of a Recurrent Neural Network (RNN) highlighting input, hidden state, and output. | [
"RNN",
"input",
"output"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000084.png | Diagram showing the encoder-decoder architecture with latent variable z for generating outputs. | [
"encoder",
"decoder",
"latent variable"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000085.jpg | Neural network diagram illustrating input, hidden layers, and output with connections between nodes. | [
"neural network",
"hidden layers",
"output"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000086.png | Visualization of latent space mapping between input (x) and latent variable (z) for generating outputs. | [
"latent space",
"mapping",
"neural network"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000087.png | A 2D scatter plot illustrating a regression line with data points and annotations for dependent and independent variables. | [
"scatter plot",
"regression line",
"data points"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000088.png | A visualization showing the regression line with residual errors connecting data points to the line. | [
"regression",
"errors",
"line of best fit"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000089.jpg | A 2D and 3D visualization demonstrating hyperplanes used for data separation in classification. | [
"hyperplanes",
"classification",
"visualization"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000090.jpg | An SVM diagram showing support vectors, margins, and the separating hyperplane for classification. | [
"SVM",
"support vectors",
"hyperplane"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000091.png | An SVM diagram highlighting the optimal hyperplane, support vectors, and maximum margin for classification tasks. | [
"SVM",
"optimal hyperplane",
"support vectors"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000092.png | A gradient descent graph illustrating the weight update process and the role of learning rate in optimization. | [
"gradient descent",
"learning rate",
"optimization"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000093.png | A detailed diagram showing the incremental steps of gradient descent towards minimizing the cost function. | [
"gradient descent",
"cost function",
"optimization steps"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000094.png | Mathematical equation representing the mean squared error (MSE) loss function for regression analysis. | [
"MSE",
"loss function",
"regression"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000095.png | Equations for loss and cost functions used in machine learning to evaluate regression models. | [
"loss function",
"cost function",
"regression"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000096.png | An illustration of a dog explaining logistic regression with a sigmoid function and probability concepts. | [
"logistic regression",
"sigmoid function",
"probability"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000097.png | A playful illustration of a cat explaining linear regression with a scatter plot and line of best fit. | [
"linear regression",
"scatter plot",
"line of best fit"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000098.png | A detailed diagram of a neural network showcasing input, hidden, and output layers with node connections. | [
"neural network",
"layers",
"node connections"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/000099.png | A colorful neural network visualization with a yellow background and clearly labeled layers. | [
"neural network",
"yellow background",
"layers"
] |
|
C:/Users/rohka/OneDrive/Desktop/Data collection for the A&I Project/sample_dataset/Sample/0000100.jpg | A relaxed orange cat lounging on a couch, looking directly at the camera. | [
"orange cat",
"relaxed",
"couch"
] |
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