---
license: apache-2.0
tags:
  - flight-planning
  - transformer
  - coordinate-prediction
  - sequence-to-sequence
  - count-classification
---
# Flight Plan Coordinate Prediction Model (Seq2SeqCoordsTransformer)
Encoder-Decoder Transformer model trained for AI flight planning project. Predicts normalized coordinates directly and waypoint count via classification.
## Model Description
Seq2SeqCoordsTransformer architecture using `torch.nn.Transformer`. Predicts normalized lat/lon coordinates autoregressively and waypoint count (0-10) via classification head on encoder output.
* Embed Dim: 256, Heads: 8, Enc Layers: 4, Dec Layers: 4, Max Waypoints: 10
## Intended Use
Research prototype. **Not for real-world navigation.**
## Limitations
Accuracy depends on data/tuning. Fixed max waypoints (10). Not certified. **Architecture differs significantly from previous versions in this repo.**
## How to Use
Requires loading the custom `Seq2SeqCoordsTransformer` class and weights. Generation requires autoregressive decoding and taking argmax of count logits.
## Training Data
Trained on `frankmorales2020/flight_plan_waypoints` - https://huggingface.co/datasets/frankmorales2020/flight_plan_waypoints.
## Contact
Frank Morales, BEng, MEng, SMIEEE (Boeing ATF) - https://www.linkedin.com/in/frank-morales1964/