flashseg 1.0.0

Ultra-lightweight real-time image segmentation with LoRA fine-tuning & Knowledge Distillation

FlashSeg

Ultra-lightweight real-time semantic segmentation with LoRA fine-tuning & knowledge distillation

Install • Usage • Models • Solutions • Training • Examples

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What is FlashSeg?

FlashSeg is an ultra-lightweight semantic segmentation framework built for speed and edge deployment. Using a ShuffleNetV2 backbone with FPN neck, it delivers real-time pixel-level predictions with models as small as 0.3M parameters.

pip install -e .
flashseg train --model-size m --num-classes 21 --train-images data/images --train-masks data/masks --val-images data/val_img --val-masks data/val_mask
flashseg predict --model best.pth --source images/

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Installation

# From source
git clone https://github.com/FlashVision/FlashSeg.git
cd FlashSeg
pip install -e ".[all]"

Optional extras

pip install -e ".[export]"      # ONNX export
pip install -e ".[analytics]"   # Benchmarking, plots
pip install -e ".[solutions]"   # Background removal, lane detection
pip install -e ".[all]"         # Everything

Verify

flashseg check
flashseg settings
flashseg version

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Usage

Python API

from flashseg import Trainer, Predictor, Exporter

# Train
trainer = Trainer(
    model_size="m",
    train_images="data/images",
    train_masks="data/masks",
    val_images="data/val_images",
    val_masks="data/val_masks",
    num_classes=21,
    epochs=100,
    device="cuda",
)
trainer.train()

# Predict
predictor = Predictor(model_path="workspace/best.pth", num_classes=21, device="cuda")
mask = predictor.predict("photo.jpg")

# Export
exporter = Exporter(model_path="workspace/best.pth", num_classes=21)
exporter.export(output="model.onnx", simplify=True)

CLI

flashseg train --model-size m --epochs 100 --device cuda \
  --train-images data/images --train-masks data/masks \
  --val-images data/val_img --val-masks data/val_mask --num-classes 21

flashseg predict --model best.pth --source images/ --save-dir output/

flashseg val --model best.pth --val-images data/val --val-masks data/val_mask

flashseg export --model best.pth --output model.onnx --simplify

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Models

Model	Params	FP16 Size	Input	mIoU (VOC)
FlashSeg-n	0.3M	~0.7 MB	256	—
FlashSeg-s	0.8M	~1.6 MB	256	—
FlashSeg-m	1.5M	~3.0 MB	512	—
FlashSeg-l	3.2M	~6.4 MB	512	—

Config-driven Training

flashseg train --config configs/flashseg_m_512_voc.yaml
flashseg train --config configs/flashseg_s_256_cityscapes.yaml
flashseg train --config configs/flashseg_m_512_lora.yaml

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Solutions

Built-in high-level applications:

from flashseg import Predictor
from flashseg.solutions import BackgroundRemover, LaneDetector, SceneParser, AreaCalculator

predictor = Predictor(model_path="best.pth", num_classes=21)

# Remove background
remover = BackgroundRemover(predictor, foreground_classes=[15])
result = remover.remove(image)

# Detect lanes
lanes = LaneDetector(predictor, lane_class_id=1)

# Parse scenes
parser = SceneParser(predictor, class_names=["bg", "road", "building", ...])
stats = parser.parse(image)  # {"road": 35.2, "building": 12.1, ...}

# Calculate areas
calc = AreaCalculator(predictor, pixels_per_meter=10.0)
areas = calc.calculate(image)  # {class_id: area_m2, ...}

Solution	Description
BackgroundRemover	Remove/replace backgrounds, generate alpha mattes
LaneDetector	Detect road lanes from segmentation
SceneParser	Break scene into labeled regions with area stats
AreaCalculator	Measure real-world areas from masks

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Training

Standard

flashseg train --model-size m --epochs 100 --num-classes 21 --device cuda

LoRA Fine-Tuning

flashseg train --model-size m --lora --config configs/flashseg_m_512_lora.yaml

Mixed Precision

flashseg train --model-size m --amp --device cuda

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Examples

Script	What it does
train_voc.py	Train on Pascal VOC
predict_image.py	Segment a single image
background_removal.py	Remove image background
export_onnx.py	Export to ONNX
benchmark_model.py	Measure FPS and latency

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Project Structure

FlashSeg/
├── flashseg/                  # Main package
│   ├── cfg/                   # Configuration + YAML loading
│   ├── data/                  # Datasets, transforms
│   ├── engine/                # Trainer, Predictor, Exporter, Validator
│   ├── models/                # ShuffleNetV2, FPN, SegHead
│   ├── losses/                # CE, Dice, Focal, Combined
│   ├── nn/                    # ConvBnRelu, ASPP, DepthwiseSeparable
│   ├── utils/                 # Metrics, visualization
│   ├── solutions/             # Background removal, lanes, scene parsing
│   └── analytics/             # Benchmark, profiler
├── configs/                   # YAML configs (pick & train)
├── examples/                  # Ready-to-run scripts
├── tests/                     # Unit tests
├── docker/                    # Dockerfile + compose
├── pyproject.toml             # Package config
└── LICENSE                    # MIT

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Docker

docker build -t flashseg -f docker/Dockerfile .
docker run --gpus all -v $(pwd)/data:/app/data flashseg predict --model best.pth --source data/

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Contributing

git clone https://github.com/FlashVision/FlashSeg.git
cd FlashSeg
pip install -e ".[dev,all]"
ruff check flashseg/
pytest tests/ -v

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License

MIT — see LICENSE.

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FlashVision — Open-source lightweight AI