cvmd 0.1.1

Computer Vision Models Deployment

CVMD

中文文档

A TorchScript-first computer vision inference toolkit with a unified NumPy API.

Why CVMD

• One inference interface: use the same model(image) pattern across YOLO and DETR-style models.
• Deployment-oriented: load TorchScript weights directly without carrying training repositories into production.
• Easy model switching: swap architectures with build(...) while keeping the same input/output convention.
• Practical scaling path: start with simple single-image inference, then expand to sliding-window or Ray-based distributed runs when needed.

Installation

pip install cvmd

Quick Start

import imageio.v3 as iio
from cvmd import build

model = build("yolov11det", weights="yolo11l.torchscript", device="cuda")
model.load_model()

image = iio.imread("image.jpg")
results = model(image)
# results: [x1, y1, x2, y2, confidence, class]

Supported Models

Model Series	Task	Registered Names
YOLOv12	Detection / Segmentation	yolov12det, yolov12seg
YOLOv11	Detection / Segmentation	yolov11det, yolov11seg
YOLOv8	Detection / Segmentation	yolov8det, yolov8seg
YOLOv5	Detection / Segmentation	yolov5det, yolov5seg
DETR	Detection	detr
RF-DETR	Detection	rfdetr, rfdetrdetect, rf-detr
Deformable DETR	Detection	deformabledetr, deformable_detr, deformable-detr

Core API

• build(model_name_or_cls, **kwargs): build a model instance by name or class.
• list_models(): list registered model names.
• register_model(*names): register a custom model class.

Detection models return:

# np.ndarray, shape=(N, 6)
# [x1, y1, x2, y2, confidence, class]

Segmentation models return:

# (detections, masks)
# detections: np.ndarray, shape=(N, 6)
# masks: np.ndarray, shape=(N, H, W)

More Docs

• Chinese README
• Guide
• Examples and tests

Development

git clone <this repository>
cd cvmd
uv sync --dev