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PM2.5 Forecasting Demo Data
This dataset stores the lightweight artifacts used by the Hugging Face Space demo:
https://huggingface.co/spaces/sumit1703/pm25-forecasting
The files are precomputed outputs from the ANRF AISEHack Phase 2 Theme 2 Pollution Forecasting project. The Space uses these artifacts only for visualization. It does not run live model inference, training, or torch at runtime.
Files
| File | Description |
|---|---|
demo_preds.npy |
Precomputed PM2.5 forecast predictions for demo windows. Shape: (22, 140, 124, 16) |
demo_inputs.npy |
Input PM2.5 history windows used for comparison. Shape: (22, 10, 140, 124) |
lat_lon.npy |
Latitude/longitude grid used for map axes. |
demo_stats.json |
Feature normalization/statistics metadata from the preprocessing pipeline. |
sample_indices.npy |
Original sample/window indices used by the demo. Shape: (22,) |
Intended Use
This dataset is intended for the companion Gradio demo only. It allows the app to load saved prediction artifacts from the Hugging Face Hub instead of storing .npy files inside the GitHub repository.
Source Project
- GitHub: https://github.com/sumitjadhav1703/pm25-forecasting-demo
- Live Space: https://huggingface.co/spaces/sumit1703/pm25-forecasting
- Kaggle Competition: https://www.kaggle.com/competitions/anrf-aise-hack-phase-2-theme-2-pollution-forecasting-iitd
Notes and Limitations
- These files are for demo visualization, not full model training.
- The dataset contains a small subset of demo windows, not the complete competition dataset.
- The predictions are precomputed and static.
- The Space downloads these files at startup using
huggingface_hub.
License
MIT
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