Custom Image Vision Model Development
Budget: ₹12,500 – ₹37,500 INR
I’m building a computer-vision engine that will analyse still images supplied by my users. Every photo is uploaded through the platform, so the system has to cope with the usual variety in resolution, lighting and framing you see in crowd-sourced content. The ultimate goal is a bespoke model, not a generic object detector or facial recogniser, so I need someone who can help me explore the data, define the most suitable task formulation, and then implement, train and deploy a production-ready solution.
The workflow I have in mind starts with a solid data-prep pipeline: cleaning, augmentation and balanced splitting of the image set. From there we’ll iterate on architecture choice—whether that ends up being a transfer-learned CNN, a vision transformer or something more exotic—using PyTorch or TensorFlow. Robust experimentation, hyper-parameter tuning and clear evaluation reports are essential so I can trace how each change affects performance.
Once the model satisfies agreed accuracy and recall thresholds, I’ll need it wrapped behind a lightweight REST or gRPC service, complete with a simple authentication layer and JSON responses ready to drop into the existing backend. Dockerisation is preferred so I can redeploy easily across staging and production.
Deliverables I expect
• Reproducible training scripts and preprocessing code
• Trained model weights plus inference service (containerised)
• Evaluation notebook showing metrics on a held-out test set
• Concise deployment & usage documentation
If you thrive on turning raw photos into useful, measurable insight, let’s talk data splits, augmentation tricks and loss curves.
The workflow I have in mind starts with a solid data-prep pipeline: cleaning, augmentation and balanced splitting of the image set. From there we’ll iterate on architecture choice—whether that ends up being a transfer-learned CNN, a vision transformer or something more exotic—using PyTorch or TensorFlow. Robust experimentation, hyper-parameter tuning and clear evaluation reports are essential so I can trace how each change affects performance.
Once the model satisfies agreed accuracy and recall thresholds, I’ll need it wrapped behind a lightweight REST or gRPC service, complete with a simple authentication layer and JSON responses ready to drop into the existing backend. Dockerisation is preferred so I can redeploy easily across staging and production.
Deliverables I expect
• Reproducible training scripts and preprocessing code
• Trained model weights plus inference service (containerised)
• Evaluation notebook showing metrics on a held-out test set
• Concise deployment & usage documentation
If you thrive on turning raw photos into useful, measurable insight, let’s talk data splits, augmentation tricks and loss curves.