Plastic Waste Image Classification
Budget: ₹600 – ₹1,500 INR
Waste Image Classification is a deep learning-based web application that classifies waste images into various categories to promote efficient recycling and waste management. It leverages a Convolutional Neural Network (CNN) model and provides a real-time, interactive interface using Streamlit.
Requirements:
- Develop a CNN model for image classification.
- Use Python and Streamlit for implementation.
- Used streamlit cloud for deployment.
- Handle and preprocess a large dataset of plastic waste images.
- Achieve 90.695 accuracy across a vast number of categories.
Ideal Skills and Experience:
- Expertise in deep learning and CNNs.
- Proficiency in Python and Streamlit.
- Experience with image classification tasks.
- Ability to manage and process large datasets.
- Strong background in developing and optimizing deep learning models.
Deliverables:
- A trained and validated CNN model.
- Streamlit app for model deployment and testing.
- Documentation on model architecture and usage.
Requirements:
- Develop a CNN model for image classification.
- Use Python and Streamlit for implementation.
- Used streamlit cloud for deployment.
- Handle and preprocess a large dataset of plastic waste images.
- Achieve 90.695 accuracy across a vast number of categories.
Ideal Skills and Experience:
- Expertise in deep learning and CNNs.
- Proficiency in Python and Streamlit.
- Experience with image classification tasks.
- Ability to manage and process large datasets.
- Strong background in developing and optimizing deep learning models.
Deliverables:
- A trained and validated CNN model.
- Streamlit app for model deployment and testing.
- Documentation on model architecture and usage.