# 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`](https://huggingface.co/thefalley)

Companion repositories:

| Repository | Purpose |
|---|---|
| [`Thefalley/yolov4-tiny-416-int8-qop`](https://huggingface.co/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.