⚡ VisiMac Studio
High-Performance Computer Vision & Dataset Preparation Engine
VisiMac Studio is an all-in-one workstation designed for computer vision engineers, AI researchers, and data scientists. It bridges the gap between raw data collection and production model deployment by integrating interactive dataset annotation, multi-unit image batch processing, grid tile splitting, synthetic data augmentation, AutoML hyperparameter optimization, YOLO training/metrics tracking, and model export.
🌟 Key Features
| Module | Features & Capabilities |
|---|---|
| 🖼️ Interactive Annotator | Multi-shape drawing (Polygon, Rectangle, Circle, Pentagon, Move/Select), Perspective Warp, Fixed/Custom Rotations, Point Crop, Image Sequence Frame-Copying, and AI Auto-Labeling (.pt weights). |
| 📐 Batch Scaling & Compression | Multi-unit conversion (px, cm, mm, in, pt), DPI target adjustments, aspect-ratio lock, and exact target file size binary-search quality compression (in KB). |
| ✂️ Tile Splitter Engine | Dynamic row/column tile splitting, structured subfolder grouping (part_1/, part_2/), and zero-padded naming schemas. |
| 🎨 Synthetic Augmentation | Real-time live simulation of YOLO augmentations (Mosaic, Mixup, HSV jitter, Shearing, Translation, Crop Fraction, Random Erasing) and batch synthetic image generation. |
| 🧬 YOLO Training & AutoML | Multi-backbone architecture support, genetic algorithm hyperparameter tuning (AutoML), dataset quality health inspection (unannotated/orphaned check), real-time loss graph rendering, and evaluation breakdown. |
| ⚡ Inference & Production Export | Batch directory inference with confidence thresholding and one-click model exporting to production formats (ONNX, TensorRT/Engine, OpenVINO, TorchScript, CoreML). |
🛠️ Installation
Standard Installation via PyPI
Install the pre-compiled binary package directly using pip:
pip install visimac
Launch the workstation GUI directly from your terminal:
visimac
🚀 Workstation Guide
1. Interactive Workspace & Annotator
- Load Directory: Load image folders (
.jpg,.png,.bmp,.webp) along with any existingclasses.txtor YOLO format.txtlabel files. - Transformation Tools: Perform non-destructive image rotation (fixed 90° CW/CCW or custom precision angles), point cropping, and 4-point perspective warping directly on the canvas.
- AI Assist & Frame Copying: Connect a pre-trained PyTorch YOLO model (
.pt) to automatically label target frames based on confidence thresholds, or copy shapes across sequential camera frames. - Dataset QA Health Inspector: Identify unannotated images and orphaned label files before running expensive model trainings.
2. Batch Resizer & Compression Engine
- Dimension Controls: Scale images by precise pixel counts or physical units (
cm,mm,in,pt) tied to custom target DPIs. - Size Constraint Search: Specify a target maximum file size in kilobytes (e.g.,
500 KB). The built-in binary search algorithm calculates the optimal compression quality factor per image without destroying aspect ratios or visual fidelity.
3. Tile Splitter Engine
- Decompose large high-resolution satellite, medical, or industrial images into structured image grids.
- Select target slice counts (e.g.,
4parts $\rightarrow$2 Rows x 2 Cols). - Optionally organize tiles into dedicated indexed subfolders (
part_1/,part_2/) for parallel model ingestion.
4. Synthetic Augmentation Studio
- Adjust HSV saturation/hue offsets, translation shifts, rotation ranges, mosaic probabilities, and cutout parameters.
- Click Simulate Augmentation Canvas to view how YOLO will transform your datasets during training.
- Batch-generate synthetic variants directly into mapped project directories.
5. Training, AutoML & Inference
- AutoML Optimization: Run genetic algorithms across mini-epochs to automatically determine optimal learning rates (
lr0,lrf) and augmentation parameters. - Live Loss Analytics: Monitor real-time training progress via live-rendered loss curves and metric breakdowns (
Precision,Recall,mAP50,mAP50-95). - One-Click Export: Convert standard
.ptmodel weights into edge-ready deployment binaries (ONNX,TensorRT/Engine,OpenVINO,TorchScript,CoreML).
💻 Cross-Platform Support
| Operating System | Binary Extension Type | Architecture |
|---|---|---|
| Windows | .pyd |
x86_64 / AMD64 |
| Linux | .so |
x86_64 |
| macOS | .so |
x86_64 / arm64 (Apple Silicon) |
1 maintainer
Metadata
1 maintainer
Release files for visimac 1.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 16.2 MB
Release files / visimac-1.0.1-cp312-cp312-win_amd64.whl
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| Size | 315.9 kB |
| Tags | CPython 3.12 Windows x86-64 |
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