🖼️ ML.NET Image Classification Project

📌 Project Overview

This project is an image classification model built using ML.NET. It trains a deep learning model to classify images using ResNetV2-101. The model is trained on a dataset of images and can predict the category of an image based on its features.

🚀 Features

• Uses ML.NET for training an image classifier
• Implements ResNetV2-101 as the pretrained model
• Loads images and trains the model
• Validates and predicts image categories
• Saves the trained model for later inference

🛠️ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/your-username/MLNET-ImageClassification.git
cd MLNET-ImageClassification

2️⃣ Install Python & TensorFlow

ML.NET requires TensorFlow dependencies. Ensure you have Python installed and TensorFlow properly set up.

1️⃣ Check Python Version (must be 3.8 - 3.10):

python --version

2️⃣ If you don’t have Python 3.10, download & install it:
🔗 Python 3.10 Download

3️⃣ Install TensorFlow for Python 3.10

pip install tensorflow-cpu

4️⃣ Verify TensorFlow Installation

python -c "import tensorflow as tf; print(tf.__version__)"

Once TensorFlow is installed, proceed with the ML.NET setup below.

3️⃣ Install Dependencies

Ensure you have the required NuGet packages installed:

dotnet add package Microsoft.ML --version 4.0.1
dotnet add package Microsoft.ML.ImageAnalytics --version 4.0.1
dotnet add package Microsoft.ML.TensorFlow --version 4.0.1
dotnet add package Microsoft.ML.Vision --version 4.0.1
dotnet add package SciSharp.TensorFlow.Redist --version 2.3.1
dotnet add package TensorFlow.NET --version 0.20.1.0

4️⃣ Run the Application

dotnet run

📂 Project Structure

MLNET-ImageClassification/
│── Data/                     # Folder containing training images
│── bin/                      # Compiled output (ignored in .gitignore)
│── obj/                      # Temporary build files (ignored in .gitignore)
│── model.zip                 # (Optional) Saved trained model
│── ImageClassification.csproj # Project file
│── Program.cs                 # Main application file
│── README.md                  # Project documentation
│── .gitignore                 # Git ignore rules

🏗️ How It Works

1. Loads images from the Data/ folder
2. Preprocesses images using ML.NET transformations
3. Trains a ResNetV2-101 model for classification
4. Evaluates accuracy using a validation set
5. Uses the trained model to predict new images

📊 Example Output

AI Predictions:
Image: puppy3.jpg | Actual Label: puppy | Predicted Label: puppy
Image: kitten5.jpg | Actual Label: kitten | Predicted Label: kitten
Image: kitten4.jpg | Actual Label: kitten | Predicted Label: kitten
Image: puppy4.jpg | Actual Label: puppy | Predicted Label: puppy

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🚀 Happy Coding! 🎉