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
traffic
parking
dining
Reproducibility
This release is a snapshot of an ongoing training run. The pipeline:
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.
1@article{ge2021yolox,
2 author = {Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
3 title = {YOLOX: Exceeding YOLO Series in 2021},
4 journal = {arXiv:2107.08430},
5 year = {2021}
6}
Author of the trained weights and INT8 derivative: Pablo Mendoza
(@thefalley), 2026.
