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README.md

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----
-license: mit
----
+---
+license: mit
+tags:
+  - object-detection
+  - yolox
+  - onnx
+  - int8
+  - coco
+  - fpga
+language: en
+library_name: onnxruntime
+pipeline_tag: object-detection
+---
+
+# YOLOX-Nano ReLU (MIT) — work-in-progress training
+
+A YOLOX-Nano variant with **ReLU activations and no depthwise convolutions**,
+**trained from random initialisation on COCO 2017 by Pablo Mendoza
+(`@thefalley`)** on his own server (NVIDIA GTX 1050 Ti, 4 GB VRAM). The
+weights, the ONNX export pipeline, the INT8 quantization, the decoder and
+all inference scripts in this repository are original work, released under
+the MIT License.
+
+The training architecture is `yolox_nano_ti_lite` from TexasInstruments'
+`edgeai-yolox` repository — used as a build tool only, **not redistributed**
+here. See `NOTICE.md` for the full provenance chain.
+
+## Training status (live)
+
+| | |
+|---|---|
+| Current epoch | **1 / 300** |
+| AP @ IoU=0.5:0.95 | *** (will be measured at validation milestones) |
+| Hardware | GTX 1050 Ti, 4 GB VRAM (own server, no cloud) |
+| Started | 2026-05-10 |
+| Last update | 2026-05-11 |
+| Status | 🔄 Training in progress |
+| Target | epoch 300, target mAP@0.5:0.95 ≈ 0.26 (TI baseline for the same architecture) |
+
+> **This is an early-checkpoint release** intended to validate the full
+> pipeline (PTH → ONNX → INT8 → inference) end-to-end. Detection quality
+> will improve substantially as training progresses; the repository will
+> be updated incrementally with later checkpoints.
+
+## Files
+
+| File | Size | SHA-256 |
+|---|---:|---|
+| `yolox_nano_relu_float.onnx`     | *** | *** |
+| `yolox_nano_relu_int8_qop.onnx`  | *** | *** |
+
+## Architecture
+
+| | |
+|---|---|
+| Family | YOLOX-Nano (Megvii, 2021) with the TI ti-lite modifications |
+| Depth multiplier | 0.33 |
+| Width multiplier | 0.25 |
+| Parameters | ~1.9 M |
+| Activation | **ReLU** (every Conv block; no SiLU) |
+| Convolutions | **Regular Conv2D only** (no depthwise separable) |
+| Input | 1×3×416×416, RGB, NCHW |
+| Output (when exported with `--no-onnxsim`) | single tensor `(1, N, 85)` with `[cx, cy, w, h, obj, class_0..class_79]` already in input-pixel coords, anchor-free YOLOX-style |
+| Quantization | Per-tensor INT8 (W symmetric, A asymmetric); bias INT32 |
+
+These design choices are deliberate to match the operator set of an
+INT8 FPGA DPU (Xilinx ZedBoard XC7Z020) that supports Conv2D + ReLU/Leaky
++ Add + Concat + MaxPool + Resize + Transpose only.
+
+## Performance
+
+| Metric | FP32 | INT8 | Reference (TI baseline) |
+|---|---|---|---|
+| AP @ IoU=0.5:0.95 | *** | *** | 0.261 |
+| AP @ IoU=0.5      | *** | *** | 0.418 |
+
+Reference numbers are TI's published metrics for the fully-trained
+yolox_nano_ti_lite (300 epochs). Our weights are still being trained;
+intermediate metrics will appear here as checkpoints are released.
+
+## Visual inference samples
+
+Sample detections produced by the current published checkpoint of this
+model on classic test images (epoch 1 / 300 — detection quality will
+improve as training advances).
+
+| | |
+|---|---|
+| ![dog](images/float_dog.png) | ![traffic](images/float_traffic.png) |
+| ![parking](images/float_parking.png) | ![dining](images/float_dining.png) |
+
+## Reproducibility
+
+This release is a snapshot of an ongoing training run. The pipeline:
+
+```
+1.  Train         (TI edgeai-yolox, GTX 1050 Ti, COCO train2017)
+       → yolox_nano_relu.pth
+
+2.  Export ONNX   (tools/export_onnx.py from edgeai-yolox, --opset 13)
+       → yolox_nano_relu_float.onnx
+
+3.  Quantize INT8 (onnxruntime.quantize_static, 1000 random COCO val2017
+                   images for calibration, seed = 42, per-tensor)
+       → yolox_nano_relu_int8_qop.onnx
+```
+
+Calibration was performed once on the float ONNX of the published
+checkpoint and will be re-run for each later checkpoint to keep INT8
+in sync with the trained float weights.
+
+## Provenance summary
+
+```
+TI yolox_nano_ti_lite exp file   (BSD-3 + Apache-2.0, build tool only)
+        │
+        │  Train from scratch, COCO train2017, own hardware
+        ▼
+yolox_nano_relu.pth                MIT (original training output)
+        │
+        │  TI export_onnx.py (build tool)
+        ▼
+yolox_nano_relu_float.onnx         MIT (this repository)
+        │
+        │  onnxruntime.quantize_static (MIT, tool)  +  COCO val2017 (CC BY 4.0)
+        ▼
+yolox_nano_relu_int8_qop.onnx      MIT (this repository)
+```
+
+The TI source code is used only as a build-time tool and is **not**
+redistributed here. The weights themselves are an original training
+output of the author. See `NOTICE.md` for full attribution.
+
+## Detector size collection by the same author
+
+| Repository | INT8 size | mAP@0.5:0.95 |
+|---|---|---|
+| [yolov4-leaky-416-int8-qop](https://huggingface.co/Thefalley/yolov4-leaky-416-int8-qop) | 61.66 MiB | 0.345 |
+| [yolov4-tiny-416-int8-qop](https://huggingface.co/Thefalley/yolov4-tiny-416-int8-qop) | 5.83 MiB | 0.163 |
+| [yolo-fastest-1.1-320-int8-qop](https://huggingface.co/Thefalley/yolo-fastest-1.1-320-int8-qop) | 0.47 MiB | (pending) |
+| **yolox-nano-relu-mit** (this) | *** | *** (training in progress) |
+
+## Citation
+
+```bibtex
+@article{ge2021yolox,
+  author  = {Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
+  title   = {YOLOX: Exceeding YOLO Series in 2021},
+  journal = {arXiv:2107.08430},
+  year    = {2021}
+}
+```
+
+Author of the trained weights and INT8 derivative: **Pablo Mendoza**
+(`@thefalley`), 2026.