Multimodal Safety Forecast ML Model
Budget: ₹100 – ₹400 INR
I have safety sector time-series dataset that combines three synchronized streams: sensor imagery, textual maintenance logs, and high-frequency numeric readings. The objective is to forecast future values—not merely detect anomalies—so grid operators can anticipate demand, equipment stress, and renewable supply fluctuations.
Because this is a research-level effort, I’m not looking for an off-the-shelf CNN, RNN, or simple transformer stack. I need a genuinely novel architecture (or a rigorously justified adaptation of cutting-edge multimodal papers) that fuses image, text, and numeric signals into a single forecasting pipeline and demonstrably outperforms strong baselines.
Key expectations
• End-to-end experimentation code (Python, PyTorch or TensorFlow) with clear data loaders for each modality
• Custom model implementation with commented rationale for design decisions
• Reproducible training scripts, hyper-parameter configs, and a validation notebook that plots forecast accuracy against standard baselines
• Final technical report summarizing methodology, results, and potential publication avenues
Acceptance criteria
• Forecast MAE or MAPE improvement over baseline multimodal fusion of at least X% on my held-out test set (exact target set during kickoff)
• Ablation study proving the contribution of each modality
• Clean, runnable repository with README and environment file
If you thrive on research challenges and can back ideas with solid code and metrics, let’s push multimodal forecasting forward together.
Because this is a research-level effort, I’m not looking for an off-the-shelf CNN, RNN, or simple transformer stack. I need a genuinely novel architecture (or a rigorously justified adaptation of cutting-edge multimodal papers) that fuses image, text, and numeric signals into a single forecasting pipeline and demonstrably outperforms strong baselines.
Key expectations
• End-to-end experimentation code (Python, PyTorch or TensorFlow) with clear data loaders for each modality
• Custom model implementation with commented rationale for design decisions
• Reproducible training scripts, hyper-parameter configs, and a validation notebook that plots forecast accuracy against standard baselines
• Final technical report summarizing methodology, results, and potential publication avenues
Acceptance criteria
• Forecast MAE or MAPE improvement over baseline multimodal fusion of at least X% on my held-out test set (exact target set during kickoff)
• Ablation study proving the contribution of each modality
• Clean, runnable repository with README and environment file
If you thrive on research challenges and can back ideas with solid code and metrics, let’s push multimodal forecasting forward together.