CLIP vs SigLIP Experiment
Budget: ₹1,500 – ₹12,500 INR
I have a clean, well-documented codebase that already wires the SmallCap captioning architecture to three retrieval encoders—CLIP, SigLIP, and SigLIP2. All hyper-parameters, data splits, and preprocessing steps for MS-COCO 2017 have been locked down. Your task is to finish the implementation where noted, run the comparative training + inference cycle, and return a fully reproducible set of results.
Evaluation must cover CIDEr, METEOR, BLEU, and SPICE, with CIDEr called out as the primary figure of merit in the final table. I will need:
• the updated training / evaluation scripts
• raw and aggregated metric outputs (JSON or CSV) for each encoder checkpoint
• a concise report or notebook that plots the head-to-head scores and explains any anomalies
• a README detailing exact commands, environment specs, and seeds so I—or any reviewer—can replicate the runs end-to-end on MS-COCO 2017.
Everything should slot straight back into the existing repo (PyTorch-based) without refactoring. No new model exploration is required; just faithful execution of the defined plan and neat packaging of the evidence that it worked.
Evaluation must cover CIDEr, METEOR, BLEU, and SPICE, with CIDEr called out as the primary figure of merit in the final table. I will need:
• the updated training / evaluation scripts
• raw and aggregated metric outputs (JSON or CSV) for each encoder checkpoint
• a concise report or notebook that plots the head-to-head scores and explains any anomalies
• a README detailing exact commands, environment specs, and seeds so I—or any reviewer—can replicate the runs end-to-end on MS-COCO 2017.
Everything should slot straight back into the existing repo (PyTorch-based) without refactoring. No new model exploration is required; just faithful execution of the defined plan and neat packaging of the evidence that it worked.
Related categories:
Python
Software Architecture
Machine Learning (ML)
Data Science
Pytorch
Deep Learning