Computer Vision / Deep Learning / MLOps

VisionSeg

Semantic Segmentation & Production ML Engineering

A configurable computer vision framework built around DeepLabV3 and ResNet-50, connecting pixel-level segmentation with reproducible evaluation and deployment-oriented engineering.

PyTorchDeepLabV3ResNet-50Computer VisionMLOps

Explore Architecture View Segmentation Demo

Configurable Deep Learning for Pixel-Level Visual Understanding

Conceptual demonstration — not model output

Original synthetic vehicle · illustrative semantic regions.

Beyond Model Training

Semantic segmentation assigns a class to every pixel. Reliable systems must also address inconsistent image dimensions, class imbalance, ambiguous boundaries, similar-looking classes, and dataset leakage.

VisionSeg is an independent framework design connecting modular preprocessing, DeepLabV3 training, evaluation, error analysis, and inference. Reproducibility and inference performance are engineering concerns throughout the lifecycle.

System Architecture

An image-only forward path, from spatial features to semantic classes. Paired labels support learning and offline evaluation separately.

Image input

Synthetic RGB image

Geometry + pixel values

Resize & pad

Preserve aspect ratio.
Tensor conversion + normalization.

Model-ready tensor

Image only at inference

Paired labels / learning & evaluation only. Class-index masks use nearest-neighbor resizing; class IDs remain discrete.

DeepLabV3 + ResNet-50

Standard Torchvision model · functional forward path

01 / Spatial representation

Residual feature extraction
Dilated later stages retain spatial detail.

Hierarchical features
Schematic dimensions

02 / Parallel context

One shared spatial feature map feeds all five branches.

Concatenate & project

Join branch features · 1 × 1 projection

03 / Dense prediction

3 × 3 convRefine features
1 × 1 class scoresPer-pixel logits
Bilinear resizeModel-input resolution

Segmentation outputs & evaluation

Argmax across class scores

One class index per pixel

BG / backgroundRD / rear doorFD / front door

Offline / labeled validation

Predicted class mapHeld-out class mask

Pixel confusion matrix

Class intersections & unions

Per-class IoU
Mean IoU
Foreground mean IoU

Metric utilities implemented.
Analysis runner remains a scaffold.

Atrous / r₂. A dilated kernel samples a wider neighborhood without another spatial downsampling step. Sampling grids are conceptual; dilation rates are symbolic.

Image / feature / prediction flowOffline comparison

Text architecture & implementation scope

RGB images are resized and padded without changing aspect ratio, converted to tensors and normalized. ResNet-50 extracts spatial features. DeepLabV3’s ASPP applies parallel pointwise, atrous and image-pooling branches, concatenates their outputs, and projects them before a segmentation classifier. Logits are resized to the model-input resolution; argmax assigns a semantic class per pixel.

Ground-truth masks are separate from inference inputs. Paired masks support supervised development; held-out masks and predicted class maps support offline confusion-matrix and IoU evaluation. The model factory supports an optional auxiliary head for training; this figure shows the main prediction path. Training and inference runners are scaffolds, so the figure does not assert a completed training run, a trained checkpoint, or a deployed system.

Dataset checks, transformations, model construction, class selection and metric calculations are verified in the local vehicle-part semantic segmentation implementation. ASPP and resizing follow the standard Torchvision architecture; no extra DeepLabV3+ decoder is shown. All images, feature maps and sampling patterns are original synthetic schematics, with no measured activations or performance values.

Functional architecture · synthetic illustrations · no model inference executed

Interactive Semantic Segmentation

A class for every pixel

Original SVG vehicle · synthetic semantic regions

Conceptual demonstration — not model output

Synthetic prediction regions over the original illustration.

The reference and prediction shapes are authored examples. Their differences illustrate boundary errors; no prediction accuracy is claimed.

Visualization mode

Semantic class

Show segmentation overlayHighlight selected class

Overlay opacity55%

Front Door

The front door region in the synthetic vehicle side view.

Understanding Where Segmentation Fails

mIoU: Mean intersection over union across classes.
Foreground mIoU: Mean IoU excluding background.
Per-Class IoU: Segmentation quality for individual categories.

Illustrative confusion matrix

Authored, row-normalized percentages. These synthetic values are not experiment results or a VisionSeg benchmark.

Rows: actual class · columns: predicted class
Select a cell; use arrow keys to explore.

Illustrative confusion matrix, six semantic classes, synthetic row-normalized percentages
Actual ↓ Predicted →	BG	FD	RD	FF	RF	DH
Background
Front Door
Rear Door
Front Fender
Rear Fender
Door Handle

Scroll horizontally to inspect every class →

Front Door Rear Door

Illustrative: 12% of pixels labeled Front Door are assigned Rear Door in this synthetic matrix. Visually similar components can share features. Review class-level confusion and difficult boundaries.

This matrix explains an evaluation method. It does not report independently reproduced VisionSeg performance.

Error review workflow design

Validation Predictions
Per-Class Evaluation
Confusion Analysis
Difficult Example Review
Improvement Planning

Scroll to explore the pipeline

Small objects

Inspect handles and other fine details that can blend into surrounding regions.

Similar-looking classes

Review front/rear panel confusion rather than relying on aggregate scores.

Boundary errors

Inspect transitions between adjacent classes and ambiguous edges.

Engineering Beyond Accuracy

Architectural decisions. These capabilities are described as designs where implementation is not independently verified.

Aspect-Ratio Preservation

Design preprocessing to resize and pad images without distorting their original geometry.

Mask-Safe Preprocessing

Use nearest-neighbor interpolation to preserve discrete segmentation class IDs.

Foreground-Aware Evaluation

Measure foreground segmentation separately to avoid misleading background-dominated metrics.

Reproducible Experiments

Use configuration-driven training and consistent evaluation procedures.

Engineering Roadmap

Implemented building blocks, unfinished runners, and planned integrations. Status reflects inspected source code; it does not imply a completed training run or production deployment.

VisionSeg engineering capabilities and source-verified implementation status
Capability	Engineering purpose	Status
Paired data & preprocessingValidate image–mask pairs; preserve geometry and class IDs.dataset.py · transforms.py	Validate image–mask pairs; preserve geometry and class IDs.dataset.py · transforms.py	Implemented
DeepLabV3 forward pathConstruct the model and select a class per pixel.model.py · model tests	Construct the model and select a class per pixel.model.py · model tests	Implemented
Segmentation metric utilitiesAccumulate confusion; compute per-class, mean and foreground IoU.metrics.py · metric tests	Accumulate confusion; compute per-class, mean and foreground IoU.metrics.py · metric tests	Implemented
Training & evaluation runnersConnect configured training, error review and model selection.train.py · analysis scripts are empty	Connect configured training, error review and model selection.train.py · analysis scripts are empty	Scaffold
Image-to-mask inference CLIPackage image validation, preprocessing and prediction output.inference.py is empty	Package image validation, preprocessing and prediction output.inference.py is empty	Scaffold
FastAPI servingExpose a prediction interface.Portfolio integration target	Expose a prediction interface.Portfolio integration target	Planned
Docker & ONNXPackage a runtime and investigate model export.Portfolio integration targets	Package a runtime and investigate model export.Portfolio integration targets	Planned
CI/CD automationAutomate engineering checks with GitHub Actions.Portfolio integration target	Automate engineering checks with GitHub Actions.Portfolio integration target	Planned

Scaffold = entry point exists without an implemented workflow. FastAPI, Docker, ONNX and GitHub Actions remain integration targets.

Technology Stack

Deep Learning

PyTorch
Torchvision
DeepLabV3
ResNet-50

Data Processing

NumPy
Pillow

Evaluation

mIoU
Per-Class IoU
Confusion Matrix

Visualization

Matplotlib

Engineering

Python
YAML
Pytest

Planned Integrations

FastAPI · Planned
Docker · Planned
ONNX · Planned
GitHub Actions · Planned