NOTICE — Attribution and Provenance

This repository hosts an INT8-quantized ONNX derivative of YOLOv4-Leaky-416, prepared by Pablo Mendoza (@thefalley) for deployment on a custom INT8 DPU (ZedBoard XC7Z020 FPGA).

The work this repository adds (ONNX export pipeline + INT8 quantization with COCO calibration + decoder) is released under the MIT License (see LICENSE). All upstream components keep their original licenses, listed below in dependency order.

Provenance chain

AlexeyAB / darknet                    (YOLO License v2 = public domain)
  yolov4-leaky-416.weights  (245.78 MiB)
  yolov4-leaky-416.cfg
        │
        │  parsed and loaded by:
        ▼
gwinndr / YOLOv4-Pytorch (MIT)         (used as conversion tool)
  utilities/configs.py::parse_config
  utilities/weights.py::load_weights
  + DarknetRaw wrapper (this repo, MIT) that captures pre-YoloLayer outputs
        │
        │  torch.onnx.export(opset=13)
        ▼
yolov4-leaky-416_float.onnx           (this repo, MIT)
  3 raw outputs:
    out_stride8   — shape (1, 255, 52, 52)
    out_stride16  — shape (1, 255, 26, 26)
    out_stride32  — shape (1, 255, 13, 13)
        │
        │  onnxruntime.quantize_static (MIT, used as tool)
        │  + COCO val2017 calibration (1000 images, CC BY 4.0)
        ▼
yolov4-leaky-416_int8_qop.onnx        (this repo, MIT)

External Python decoder (this repo, MIT) reproduces standard YOLOv4
post-processing: sigmoid + scale_xy + grid offset + anchor multiplication
+ NMS. The decoder lives outside the ONNX graph.

Component-level attribution

1. Model weights — AlexeyAB / darknet

Project: AlexeyAB/darknet
Source: https://github.com/AlexeyAB/darknet
Wiki listing: https://github.com/AlexeyAB/darknet/wiki/YOLOv4-model-zoo
License: YOLO License v2 — "Darknet is public domain. Do whatever you want with it."
What we use: the trained yolov4-leaky-416.weights file (245.78 MiB) and the corresponding yolov4-leaky-416.cfg. No modifications.

2. Darknet → PyTorch conversion — gwinndr / YOLOv4-Pytorch

Project: gwinndr/YOLOv4-Pytorch
Source: https://github.com/gwinndr/YOLOv4-Pytorch
License: MIT (Copyright (c) 2020 Damon Gwinn)
What we use: the parse_config cfg parser and the load_weights AlexeyAB binary loader.
We do NOT redistribute gwinndr's source code in this HF repo. It is a build-time tool.

3. ONNX export

Tool: torch.onnx.export (PyTorch core, BSD-3-Clause). Used as a tool, not redistributed.
Wrapper: DarknetRaw (≈ 30 lines, this work, MIT) intercepts the pre-YoloLayer feature maps and exports them as 3 raw 4D tensors.

4. INT8 quantization

Tool: onnxruntime.quantization.quantize_static (Microsoft, MIT). Used as a tool, not redistributed.
Configuration:
- quant_format = QuantFormat.QOperator
- weight_type = QuantType.QInt8 (per-tensor, symmetric)
- activation_type = QuantType.QInt8 (per-tensor, asymmetric)
- per_channel = False
- reduce_range = False
Calibration data: 1000 randomly-sampled images from MS COCO val2017 (CC BY 4.0). No COCO image is embedded in the ONNX file.

5. Calibration dataset — COCO val2017

Source: https://cocodataset.org
License: Creative Commons Attribution 4.0 (CC BY 4.0).

File-level integrity (SHA-256)

File	Size	SHA-256
`yolov4-leaky-416.weights` (AlexeyAB upstream)	257,717,640 B	`5b5b359940fd91e6d35bb8a957f6a8b27a05316889319173d39d25f8f33c0640`
`yolov4-leaky-416_float.onnx`	257,388,314 B	`d7277fc1c6522cb063999d2d72058fb15de6f15900c66d0093d535df0bcf200f`
`yolov4-leaky-416_int8_qop.onnx`	64,655,943 B	`ca31b2c53227518f1e29cb50e59294e758b69de26f33e374f1e65c922d338da4`

Author of the INT8 derivative

Pablo Mendoza — HuggingFace @thefalley

Companion repositories:

Repository	Purpose
`Thefalley/yolov4-tiny-416-int8-qop`	Smaller sibling: YOLOv4-tiny-416 INT8 (5.83 MiB) — fast embedded inference
`Thefalley/dpu-firmware` (GitHub, ***)	Bare-metal C firmware + RTL for the custom DPU on ZedBoard XC7Z020

Contact

If you are a rights holder and believe this attribution is inaccurate or incomplete, please open an issue on this repository and it will be corrected promptly.