WireSegHR

WireSegHR (Segmentation Only)

This repository contains the segmentation-only implementation of the two-stage WireSegHR model, training on the WireSegHR dataset plus the TTPLA dataset.

Quick Start

1. Get secrets necessary for fetching of the dataset:

You'll need a GDrive service account to fetch the WireSegHR dataset using scripts in this repo. Get a GDrive key as described in this short README, and put it in /secrets/drive-json.json

1. Run:

scripts/setup.sh

This installs dependencies and merges the TTPLA dataset into the WireSegHR dataset format.

1. Train and run a quick inference check:

python3 train.py --config configs/default.yaml
python3 infer.py --config configs/default.yaml --image /path/to/image.jpg

The default config default.yaml is suitable for a 24GB VRAM GPU with support for bf16 (e.g., RTX 3090/4090).

<!-- For a quick RTX GPU setup, I recommend [vast.ai](https://cloud.vast.ai/?ref_id=162850) -->

Project Overview

• Two-stage, global-to-local segmentation with a shared encoder and a fine decoder conditioned on the coarse stage.
• Full training loop with AMP (optional), Poly LR, periodic evaluation, checkpointing, and test visualizations (train.py).
• Dataset utilities under src/wireseghr/data/ and model components under src/wireseghr/model/.
• Paper text and figures live in paper-tex/ (paper-tex/sections/ contains the Method, Results, etc.).

Notes

• This is a segmentation-only codebase. Inpainting is out of scope here.
• Defaults locked: SegFormer MiT-B3 encoder, patch size 768, MinMax 6×6, global+binary mask conditioning with patch-cropped global map.

Backbone Source

• HuggingFace Transformers SegFormer (e.g., nvidia/mit-b3). We set num_channels to match input channels.
• Alternative: TorchVision ResNet-50 (backbone: resnet50). The stem is adapted to the requested in_channels, and we expose features from layer1..layer4 at strides 1/4, 1/8, 1/16, 1/32 with channels [256, 512, 1024, 2048].

Dataset Convention

• Flat directories with numeric filenames; images are .jpg/.jpeg, masks are .png.
• Example (after split 85/5/10):
  • dataset/train/images/1.jpg, 2.jpg, ... and dataset/train/gts/1.png, 2.png, ...
  • dataset/val/images/... and dataset/val/gts/...
  • dataset/test/images/... and dataset/test/gts/...
• Masks are binary: foreground = white (255), background = black (0).
• The loader strictly enforces numeric stems and 1:1 pairing of naming and will raise on file name mismatches.

Update configs/default.yaml with your paths under data.train_images, data.train_masks, etc. Defaults point to dataset/train/images, dataset/train/gts, and validation to dataset/val/....

Inference

• Single image (optionally save outputs to a directory):

python3 infer.py \
  --config configs/default.yaml \
  --ckpt ckpt_5000.pt \
  --image dataset/test/images/123.jpg \
  --out outputs/infer

• Compute metrics for a single image (requires a GT mask):

python3 infer.py \
  --config configs/default.yaml \
  --ckpt ckpt_5000.pt \
  --image dataset/test/images/123.jpg \
  --out outputs/infer \
  --metrics \
  --mask dataset/test/gts/123.png

• Run inference over the entire directory with metrics (images_dir sets the image directory, masks_dir sets the ground truth mask directory):

python3 infer.py \
  --config configs/default.yaml \
  --ckpt ckpt_5000.pt \
  --images_dir dataset/test/images \
  --out outputs/infer \
  --metrics \
  --masks_dir dataset/test/gts

Notes:

• Predictions are saved as 0/255 PNGs. For metrics, predictions are binarized with > 0 to match training logic.
• Masks are matched by filename stem: images/123.jpg ↔ gts/123.png.

Benchmarking and Metrics

Benchmark mode times the model on a directory of images and reports coarse/fine/total latency statistics. When --metrics is provided, it also computes IoU/F1/Precision/Recall over the benchmark set (both fine and coarse outputs).

Example (uses data.test_images and data.test_masks from the config by default):

python3 infer.py \
  --config configs/default.yaml \
  --benchmark \
  --ckpt ckpt_5000.pt \
  --bench_warmup 2 \
  --bench_limit 0 \
  --bench_report_json outputs/bench_report.json \
  --metrics

If your ground truth directory is different from data.test_masks, please override it with --bench_masks_dir:

python3 infer.py \
  --config configs/default.yaml \
  --benchmark \
  --ckpt ckpt_5000.pt \
  --bench_warmup 2 \
  --bench_limit 0 \
  --bench_report_json outputs/bench_report.json \
  --metrics \
  --bench_masks_dir /path/to/gts

You will see output like:

[WireSegHR][bench] Results (ms):
  Coarse  avg=50.16  p50=44.48  p95=76.78
  Fine    avg=534.38  p50=419.52  p95=1187.66
  Total   avg=584.54  p50=464.73  p95=1300.07
  Target  < 1000 ms per 3000x4000 image: YES
[WireSegHR][bench][Fine]   IoU=0.6098 F1=0.7576 P=0.6418 R=0.9244
[WireSegHR][bench][Coarse] IoU=0.5315 F1=0.6941 P=0.5467 R=0.9502

*These metrics were obtained after 5000 iterations

Optional: you can save a JSON timing report with --bench_report_json. Schema:

• summary
  • avg_ms, p50_ms, p95_ms
  • avg_coarse_ms, avg_fine_ms
  • images
• per_image: list of objects with
  • path, H, W, t_coarse_ms, t_fine_ms, t_total_ms

Utils:

• Export your model to inference-only weights by scripts/strip_checkpoint.py

Example:

Input: <img src="images/026_input.png" alt="Input" width="50%"/>

Fine prediction: <img src="images/026_fine_pred.png" alt="Fine prediction" width="50%"/>