Thefalley/yolox-nano-relu-mit

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).

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	61.66 MiB	0.345
yolov4-tiny-416-int8-qop	5.83 MiB	0.163
yolo-fastest-1.1-320-int8-qop	0.47 MiB	(pending)
yolox-nano-relu-mit (this)	***	*** (training in progress)

Citation

@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.