Pasta Shape Recognizer

An image classification model that utilizes data collection, augmentation, model training, cleaning, deployment and API integration to classify 20 different types of pasta shapes. The types are following:

1. Spaghetti	2. Fettuccine	3. Penne	4. Rigatoni	5. Fusilli
6. Farfalle	7. Linguine	8. Tagliatelle	9. Lasagna	10. Ravioli
11. Tortellini	12. Orecchiette	13. Conchiglie	14. Rotini	15. Bucatini
16. Cannelloni	17. Macaroni	18. Orzo	19. Cavatappi	20. Gemelli

Dataset Preparation

• Data Collection: The code collects images for each pasta shape by searching for them using the DuckDuckGo search engine and downloading the images to corresponding folders in the data directory. It then verifies the downloaded images and removes any failed downloads.
• DataLoader: The code creates a DataBlock, which defines the structure of the data, including the image and label blocks, data splitting strategy, and image transformations. It then creates a DataLoader (dls) using the DataBlock, specifying the path to the data and the batch size.
• Data Augmentation: The code applies data augmentation techniques to the images using RandomResizedCrop, which randomly crops and resizes the images to a specified size (224x224) with a minimum scaling factor of 0.5. Additional augmentation transforms are applied using aug_transforms() to further enhance the variety of the training data.

The final dataset has 7.3K+ images of 20 different pasta shapes. Details can be found in data_prep.ipynb

Training and Data Cleaning

• Training: The model is trained using a ResNet34 architecture with a 90-10 train-validation split, and fine-tuned for 5 epochs initially, achieving ~75% accuracy. After data cleaning, the model is fine-tuned for 2 more epochs, reaching 79.5% accuracy, and then further fine-tuned for 2 epochs, achieving a satisfactory accuracy of ~85.6%.
• Data Cleaning: The ImageClassifierCleaner is used to identify and remove irrelevant data points from the dataset. The data points that need to be relabeled are moved to the correct directories, ensuring the dataset's integrity and improving the model's performance. Details can be found in training_and_data_cleaning.ipynb

Model Deployment

The model is deployed to HuggingFace Spaces. The implementation can be found in deployment folder or here.

Classifying fusilli pasta

Classifying spaghetti pasta

Classifying conchiglie pasta

API integration with GitHub Pages

The deployed model is integrated with HuggingFace API here in GitHub Pages Website. Implementation and other details can be found in docs folder.

Classifying fusilli pasta via API

Build from Source

Clone the repo

git clone https://github.com/zzarif/Pasta-Shape-Recognizer.git

Install CUDA Toolkit

• Go to the NVIDIA CUDA Toolkit download page: https://developer.nvidia.com/cuda-downloads
• Select "Windows" as the operating system and choose the appropriate version and installer type.
• Download and run the installer, following the installation instructions.

Initialize and activate virtual environment

virtualenv --no-site-packages venv
source venv/Scripts/activate

Install PyTorch with CUDA support

Run the following command to install PyTorch with CUDA support using pip3:

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

Note: Replace cu121 with the appropriate CUDA version tag that matches your installed CUDA version (e.g., cu121 for CUDA 12.1).

Verify the installation

Run the following code to check if PyTorch is using the GPU:

import torch
print(torch.cuda.is_available())

If the output is True, then PyTorch is successfully set up to use the GPU. torch.cuda.get_device_name(0) should also show your GPU config.

Install Dependencies

pip3 install fastai fastbook nbdev gradio

Contact Me

Would appreciate your feedback. For any further queries feel free to reach out to me at zibran.zarif.amio@gmail.com