EnYa32/LargeScaleFishClassification

🐟 Fish Classification (CNN From Scratch)

This project builds a Convolutional Neural Network (CNN) from scratch to classify images from the "A Large Scale Fish Dataset", containing 9 different fish species.

This model is part of a larger deep learning assignment where the goal is to build:

1. 1.A CNN without transfer learning
2. 2.A CNN with transfer learning (e.g., ResNet50)
3. 3.A Streamlit app for each model
4. 4.A technical PDF report for each dataset

📂 Dataset

A Large Scale Fish Dataset Kaggle link: https://www.kaggle.com/datasets/crowww/a-large-scale-fish-dataset

Dataset structure (after download):

Fish_Dataset/ ├── Black Sea Sprat ├── Gilt-Head Bream ├── Horse Mackerel ├── Red Mullet ├── Red Sea Bream ├── Sea Bass ├── Shrimp ├── Striped Red Mullet └── Trout

yaml Code kopieren

🧠 Model Overview

The CNN was built using Keras Sequential API with:

• —5 Convolutional Layers
• —3 MaxPooling Layers
• —1 Dense Layer (256 units)
• —Dropout (0.5)
• —Softmax output layer (9 classes)

Training Setup:

• —Loss: sparse_categorical_crossentropy
• —Optimizer: Adam(lr=0.0005)
• —Metrics:
• —accuracy
• —top-3 accuracy
• —Callbacks:
• —EarlyStopping
• —ModelCheckpoint (best_fish_cnn.keras)
• —ReduceLROnPlateau

📈 Results

Final model performance on validation data:

• —Accuracy: ~84%
• —Top-3 Accuracy: ~99%
• —Validation Loss: ~0.45

The model performed very well on distinct species (Shrimp, Red Mullet, Trout). Classes that look visually similar (Sea Bass, Bream types) caused some confusion.

The technical report (PDF) contains:

• —Classification Report
• —Confusion Matrix
• —Full explanation of methods & reflection

🚀 Usage

How to Use the Model (CNN From Scratch)

_Load the model_

import tensorflow as tf

model = tf.keras.models.loadmodel("bestfish_cnn.keras")

Predict on a new image import numpy as np from tensorflow.keras.preprocessing import image

img = image.loadimg("yourimage.jpg", targetsize=(224, 224)) imgarray = np.expanddims(image.imgto_array(img), axis=0) / 255.0 # normalize

pred = model.predict(img_array) print(pred)

_Streamlit App_

This HuggingFace Space includes an interactive Streamlit app where you can:

Upload a fish image

See the model’s prediction

View confidence scores for all 9 fish classes

_Project Files File Description bestfishcnn.keras Saved CNN model (from scratch, no transfer learning) streamlitapp.py Streamlit app code requirements.txt Required Python packages FishCNNReport_English.pdf Technical report (simple English)

👤 Author

Enes Yamac

Machine Learning & Computer Vision Project (2025)