ti-edgeai-train-model

Plan and run training/fine-tuning so a vision model (detector, classifier, segmenter) deploys well on TI Edge AI processors with a C7x/MMA accelerator (TDA4VM, AM68A, AM69A, AM67A, AM62A): pick a TIDL-friendly architecture and input size, choose TI's toolchain (edgeai-modelmaker, edgeai-yolox, edgeai-mmdetection, edgeai-torchvision, plain PyTorch), build datasets and leakage-safe splits, train with quantization-friendly settings, and evaluate with the same preprocessing the board uses before handing the checkpoint to ti-edgeai-import-model. Use this skill whenever the user wants to train, fine-tune, label, split or evaluate a model "for the TI board / edge NPU / C7x", asks which architecture or input size to use, why their detector scores near zero, how to make a model quantization-friendly, or how to prepare labels so the model is learnable - even before any deployment question comes up.

Version

0.1.0

License

MIT