Project I | Deep Learning: Image Classification with CNN

Task Description

Students will build a Convolutional Neural Network (CNN) model to classify images from a given dataset into predefined categories/classes.

Datasets (pick one!)

The dataset for this task is the CIFAR-10 dataset, which consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. You can download the dataset from here.
The second dataset contains about 28,000 medium quality animal images belonging to 10 categories: dog, cat, horse, spyder, butterfly, chicken, sheep, cow, squirrel, elephant. The link is here.

Assessment Components

Data Preprocessing
- Data loading and preprocessing (e.g., normalization, resizing, augmentation).
- Create visualizations of some images, and labels.
Model Architecture
- Design a CNN architecture suitable for image classification.
- Include convolutional layers, pooling layers, and fully connected layers.
Model Training
- Train the CNN model using appropriate optimization techniques (e.g., stochastic gradient descent, Adam).
- Utilize techniques such as early stopping to prevent overfitting.
Model Evaluation
- Evaluate the trained model on a separate validation set.
- Compute and report metrics such as accuracy, precision, recall, and F1-score.
- Visualize the confusion matrix to understand model performance across different classes.
Transfer Learning
- Evaluate the accuracy of your model on a pre-trained models like ImagNet, VGG16, Inception... (pick one an justify your choice)
  - You may find this link helpful.
  - This is the Pytorch version.
- Perform transfer learning with your chosen pre-trained models i.e., you will probably try a few and choose the best one.
Code Quality
- Well-structured and commented code.
- Proper documentation of functions and processes.
- Efficient use of libraries and resources.
Report
- Write a concise report detailing the approach taken, including:
  - Description of the chosen CNN architecture.
  - Explanation of preprocessing steps.
  - Details of the training process (e.g., learning rate, batch size, number of epochs).
  - Results and analysis of models performance.
  - What is your best model. Why?
  - Insights gained from the experimentation process.
- Include visualizations and diagrams where necessary.
Model deployment
- Pick the best model
- Build an app using Flask - Can you host somewhere other than your laptop? +5 Bonus points if you use Tensorflow Serving
- User should be able to upload one or multiples images get predictions including probabilities for each prediction

Evaluation Criteria

Accuracy of the trained models on the validation set. 30 points
Clarity and completeness of the report. 20 points
Quality of code implementation. 5 points
Proper handling of data preprocessing and models training. 30 points
Demonstration of understanding key concepts of deep learning. 5 points
Model deployment. 10 points

Passing Score is 70 points.

Submission Details

Deadline for submission: end of the week or as communicated by your teaching team.
Submit the following:
1. Python code files (*.py, ipynb) containing the model implementation and training process.
2. A data folder with 5-10 images to test the deployed model/app if hosted somewhere else other than your laptop (strongly recommended! Not a must have)
3. A PDF report documenting the approach, results, and analysis.
4. Any additional files necessary for reproducing the results (e.g., requirements.txt, README.md).
5. PPT presentation

Additional Notes

Students are encourage to experiment with different architectures, hyper-parameters, and optimization techniques.
Provide guidance and resources for troubleshooting common issues during model training and evaluation.
Students will discuss their approaches and findings in class during assessment evaluation sessions.

ironhack-labs/project-1-deep-learning-image-classification-with-cnn

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