Image Classification Model Development Urgent
Budget: $100 – $300 USD
I have a collection of raw image data that needs to be turned into a production-ready image classification model. The project is strictly data-analysis-oriented: everything from exploring the dataset through to delivering a trained network and clear performance metrics.
Scope of work
• Inspect and prepare the images (deduplication, resizing, augmentation where it actually improves results).
• Select or design a deep-learning architecture that suits the dataset size and class balance—transfer learning with TensorFlow or PyTorch is perfectly fine if it speeds up convergence.
• Train, validate and test the model, then hand over the full training notebook or script with comments so I can reproduce results on my own machine.
• Provide a concise evaluation report: final accuracy, confusion matrix, and any relevant error analysis that explains common misclassifications.
Acceptance criteria
• Minimum overall accuracy of 90 % on a held-out test set I will provide.
• All code must run end-to-end on a standard GPU environment using Python (3.8+).
• Final deliverables include the trained weights, environment file/requirements.txt, and the well-documented source code or notebook.
If any assumptions about the data or labels are needed, let me know early so we can pin them down before you start training.
Scope of work
• Inspect and prepare the images (deduplication, resizing, augmentation where it actually improves results).
• Select or design a deep-learning architecture that suits the dataset size and class balance—transfer learning with TensorFlow or PyTorch is perfectly fine if it speeds up convergence.
• Train, validate and test the model, then hand over the full training notebook or script with comments so I can reproduce results on my own machine.
• Provide a concise evaluation report: final accuracy, confusion matrix, and any relevant error analysis that explains common misclassifications.
Acceptance criteria
• Minimum overall accuracy of 90 % on a held-out test set I will provide.
• All code must run end-to-end on a standard GPU environment using Python (3.8+).
• Final deliverables include the trained weights, environment file/requirements.txt, and the well-documented source code or notebook.
If any assumptions about the data or labels are needed, let me know early so we can pin them down before you start training.
Related categories:
Python
Data Processing
Machine Learning (ML)
Data Mining
Data Science
Data Analysis
Deep Learning
Data Augmentation