Merge pull request #384 from aindree-2005/seaanimal

Sea Animal Detection using Neural Networks

Sea Animal Detection Using Neural Networks/Datasets/Readme.md

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+https://www.kaggle.com/datasets/vencerlanz09/sea-animals-image-dataste/data
+Dataset

Sea Animal Detection Using Neural Networks/Images/readme.md

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+The grouping of images by class has  been shown that for tortoise/turtle higher number of images are present. But, the rest of the dataset is balanced overall

Sea Animal Detection Using Neural Networks/Models/Readme.md

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+# SEA ANIMAL DETECTION USING DEEP LEARNING
+Full name : Aindree Chatterjee
+
+GitHub Profile Link : https://github.com/aindree-2005
+
+Email ID : aindree2005@gmail.com
+
+Program : CodePeak
+
+Approach for this Project :
+
+**Description**
+Using CNNS to handle image data and identify sea animals from a diverse set
+
+**Model Used**
+Input Layers:
+Type: Conv2D
+Parameters:
+64 filters
+Kernel size of (5, 5)
+Activation function: ReLU
+Padding: 'valid'
+Input shape: (224, 224, 3)
+This layer is responsible for detecting 64 different features using 5x5 convolutional filters on the input image.
+Max Pooling Layer:
+
+Type: MaxPooling2D
+Parameters:
+Pool size of (2, 2)
+This layer performs max pooling, reducing the spatial dimensions of the representation.
+Dropout Layer:
+
+Type: Dropout
+Parameters:
+Dropout rate: 0.2
+Dropout layers are used to prevent overfitting by randomly setting a fraction of input units to 0 at each update during training.
+Batch Normalization Layer:
+
+Type: BatchNormalization
+Batch normalization normalizes the activations of the network, which can help improve training stability and speed.
+Repeat of Convolutional, Pooling, Dropout, and Batch Normalization Blocks:
+
+Similar blocks are repeated three more times with different filter sizes and configurations:
+128 filters, (5,5) kernel, ReLU activation, MaxPooling, Dropout, and BatchNormalization.
+256 filters, (3,3) kernel, ReLU activation, MaxPooling, Dropout, and BatchNormalization.
+512 filters, (3,3) kernel, ReLU activation, MaxPooling, Dropout, and BatchNormalization.
+1024 filters, (3,3) kernel, ReLU activation, MaxPooling.
+Flatten Layer:
+
+Type: Flatten
+This layer flattens the input to a one-dimensional array, preparing it for the fully connected layers.
+Dense Layer (Fully Connected):
+
+Type: Dense
+Parameters:
+512 units/neurons
+Activation function: ReLU
+This layer connects every neuron in the previous layer to every neuron in this layer.
+Dense Output Layer:
+
+Type: Dense
+Parameters:
+Number of units: equal to the number of labels/classes
+Activation function: Softmax
+This is the output layer responsible for producing the final classification probabilities for each class.
+**Visualisation**
+Visualisation plots have been added in notebook for showing class distribution, accuracy and loss in data while training
+
+**Accuracies**
+90.19% using CNN
+
+**My Conclusion**
+Convolutional Neural Networks (CNNs) are ideal for sea animal detection due to their ability to automatically learn hierarchical features from image data. The hierarchical nature of CNNs allows them to capture spatial patterns and features crucial for identifying sea animals in underwater images. With their capacity to recognize complex patterns, CNNs excel at image classification tasks, making them well-suited for accurately detecting and classifying diverse sea creatures in marine environments.
+
+**YOUR NAME**
+Aindree Chatterjee

Sea Animal Detection Using Neural Networks/requirements.txt

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+utils
+keras
+tensorflow
+numpy
+pandas
+sklearn