AITS Journal
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AITS Journal

FISH BREED CLASSIFICATION

Photo by July PJuxa (Puchkova) on Dribbble

Fishes also known as Ichthyology , accounts for the majority of Sea Life on our planet . They range from small centimetre to many meters .It can be even said that is one of the most diversified species on the planet that we mere humans have not explored .Distinguishing them is a task that even puzzle experts let alone a normal person .But that’s Exactly what we will try to accomplish here .

CONTENT

  • Problem Statement
  • Library and Concepts
  • Methodology
  • Modelling
  • Kernel link

PROBLEM STATEMENT

We will be making a model to predict the fish breed of the image passed to it .We will be classifying the fish into one of 9 different categories .

LIBRARY AND CONCEPTS

Convolutional neural network

Image Augmentation

Sklearn

Keras

Matplotlib / Seaborn

Methodology

Our dataset contains 3 channels images of shape 288x384 . We will be using multiple Convolutional layers followed by a Max Pool layer with Dropout layer to keep our model generalized and prevent overfitting .We will be also using Image Augmentation on features such as rescaling ,horizontal and vertical flips . This increases our dataset size and adds a bit of randomness to our model .

Modelling

We will be using Convolution layers of 32 filters of size 3x3 at start followed by MaxPooling of size 2x2 accompanied by Dropout layer . As we go down deep in the model we increase the number of filter to 64 to allow our model to learn complex features .

To pass the Images into the Model we will be using Keras flow from dataframe .This allows our model to handle images easily and apply transformation with ease rather than getting exhausted by applying the whole dataset in one go .

Accuracy over training
Loss over Training

We are achieving about 80 % accuracy .

Predictions

Kernel Link

https://cainvas.ai-tech.systems/use-cases/fish-breed-prediction/

Credit : Devansh Chowdhury

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AI content from AITS associates with 💝. AITS is a deep learning company and lead developer of open source deep learning compiler.

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