source/README.md

QAT training source for the inblick YOLOv8n models

This is the source code that trained and exported the weights in this repository, published under AGPL-3.0 (see LICENSE) as the corresponding source of the AGPL-3.0 weights. It is a snapshot of the training and export path of the inblick project's cnnc compiler (commit 86f249d).

What is included

Path	What
cnnc/export/qat.py	QdqNet: runs the PTQ qdq.onnx graph node by node in PyTorch with straight-through fake quantisation that matches the accelerator's int8 arithmetic; writes the trained values back into the QDQ graph
cnnc/export/qat_train.py	the CLI: prep-coco, prep-crowd, crowd-init, fp32-train, train (QAT), export, eval
cnnc/export/yolov8n.py, quantize.py, coco.py, constraints.py, preprocess.py	the YOLOv8n export (PyTorch → ONNX → int8 QDQ with onnxruntime static quantisation) and its constraint checks
cnnc/frontend/qdq_graph.py, qdq_math.py	QDQ ONNX graph reading and the quantisation maths
cnnc/accuracy/coco_map.py, heads.py, ort_ref.py	COCO mAP evaluation and the onnxruntime reference used for checkpoint selection
cnnc/target/	a stub: the export reads one value of the accelerator target, activation_plane_channels = 16. The full compiler derives it from the accelerator's own constants, which are not published here.
requirements-train.txt	the training environment (CUDA PyTorch for an RTX 5090, Ultralytics 8.4.164)
doc/qat.md, doc/qat-design.md	how to reproduce the training, and the int8 arithmetic mapping

Not included: the rest of the inblick compiler (the hardware backend, scheduling and the golden model of the accelerator). The functions in cnnc/accuracy/heads.py that compile a network and run it on that golden model therefore don't work in this snapshot. They are only used for the bit-exact verification gates, not for training, export or mAP evaluation.

Running it

uv venv .venv-train --python 3.13
uv pip install --python .venv-train/bin/python -r requirements-train.txt
export PYTHONPATH=$PWD YOLO_AUTOINSTALL=False
.venv-train/bin/python -m cnnc.export.qat_train --help

The full recipe for both models (data preparation, the PTQ template export, QAT, export and evaluation) is in doc/qat.md. The exact hyper-parameters and results are in each model folder's README.md and metrics.json.

Licence

This code is released under the GNU Affero General Public License v3.0 (LICENSE). It builds on Ultralytics YOLOv8 (AGPL-3.0). The CrowdHuman-trained model is also subject to the CrowdHuman dataset's non-commercial research terms: see ../LICENSE-NOTICE.md.