Python shape calculations and static adaptation for ONNX export
Resolve an ONNX export blocked by Python shape calculations, preserve pooling semantics and validate a fixed-shape model against PyTorch and ONNX Runtime.
About 6 min readNotes from training to deployment
Specific problems encountered in model training, export and edge deployment, with versions, test conditions and unresolved limits preserved.
Every article is available in full in Chinese and English.
8 articles
Capturing a training NaN failure and restoring its state
Separate AMP skipped updates, non-finite losses and logging failures, and capture the batch, model, optimizer and random state needed to investigate a training NaN.
About 8 min readQualcomm QNN deployment and ABI troubleshooting
Set up ONNX Runtime with a standalone QNN Plugin EP on Linux ARM64 and check ELF compatibility, ARM/DSP loading, device discovery and EPContext deployment.
About 10 min readDEIMv2 fine-tuning, preprocessing and model artifacts
Diagnose low DEIMv2 AP by checking weight loading, backbone preprocessing, EMA selection and a reproducible ONNX export baseline.
About 7 min readDEIM sampling semantics and deployment limits on RKNN
Examine DEIMv2 GridSample semantics, CPU and GPU fallback costs, discrete sampling changes and the limits of archived RK3576 and RK3588 measurements.
About 7 min readKeeping video transcoding, frame sampling and temporal localization consistent
Trace differences between one-shot and iterative video localization through decoded frames, source timestamps, crop offsets and evaluation on the original timeline.
About 8 min readNVIDIA AGX Orin system setup and TensorRT deployment
Build a Jetson AGX Orin deployment baseline from BSP and CUDA libraries to TensorRT bindings, GPU containers, engine builds and numerical validation.
About 13 min readStaged Qwen3-VL SFT: training strategy and limits
Check trainable modules, optimizer state, multimodal inputs and independent evaluation when adapting Qwen3-VL with staged supervised fine-tuning.
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