Open SourceComputer VisionTutorial

AS-One: Unified
Computer Vision
Framework

AS-One is a Python wrapper that unifies detection, tracking, segmentation, OCR, and pose estimation into a single, easy-to-use interface — enabling developers to swap models with minimal code changes.

600+ GitHub Starspip install asoneGPU + CPU + EdgeYOLOv9 + SAM
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AS-One Unified Computer Vision Framework
Overview

What is AS-One?

Developed by Augmented Startups and maintained by AxcelerateAI, AS-One allows developers to experiment with modern computer vision models using standardised APIs. Instead of integrating different frameworks, pipelines, and dependencies for each model, AS-One provides one unified interface — switch from YOLOv5 to YOLOv9, or from ByteTrack to DeepSORT, with a single flag change.

Object Detection

YOLO v5–v9, YOLOX, YOLO-NAS with PyTorch, ONNX, CoreML

Object Tracking

ByteTrack, DeepSORT, NorFair, StrongSORT, OCSORT, MoTPy

Segmentation

SAM (Segment Anything Model) integration with YOLO detection

Text Detection & OCR

CRAFT text detection with EasyOCR recognition + tracking

Pose Estimation

YOLOv7-w6-pose, YOLOv8m-pose — keypoint detection & visualization

Edge & Mobile

CoreML support for M1/M2 Apple Silicon, mobile-optimized inference

Installation

Get Started in Minutes

Install AS-One with pip, or build from source for Windows support and custom environments.

Terminalbash
pip install asone

This installs AS-One and all dependencies. For GPU support, also install the correct PyTorch version for your CUDA version.

Quick Start

Run Your First Detection in 5 Lines

AS-One's unified API means you spend time on your application logic, not on framework integration boilerplate.

quick_start.py
python
import asone
from asone import ASOne

# Instantiate with tracker + detector
model = ASOne(
    tracker=asone.BYTETRACK,
    detector=asone.YOLOV9_C,
    use_cuda=True          # set False for CPU
)

# Track vehicles in a video
tracks = model.video_tracker(
    'data/sample_videos/test.mp4',
    filter_classes=['car', 'truck']
)

for model_output in tracks:
    # Draw annotations on each frame
    annotations = ASOne.draw(model_output, display=True)
    # model_output also contains bboxes, ids, classnames, scores

Switch tracker or detector by changing a single flag — no other code changes required. See all supported flags in the Usage section below.

Usage

All Capabilities, One API

Every capability follows the same instantiation pattern. Swap flags to change models with zero structural changes to your code.

Run Object Detection

python
import asone
from asone import ASOne

# Initialize detector (GPU)
model = ASOne(detector=asone.YOLOV9_C, use_cuda=True)
vid = model.read_video('data/sample_videos/test.mp4')

for img in vid:
    detection = model.detecter(img)
    annotations = ASOne.draw(detection, img=img, display=True)
    # detection contains: bboxes, class_ids, scores, class_names

Custom Trained Weights

python
# Use your own fine-tuned weights
model = ASOne(
    detector=asone.YOLOV9_C,
    weights='data/custom_weights/my_model.pt',
    use_cuda=True
)
for img in vid:
    detection = model.detecter(img)
    annotations = ASOne.draw(
        detection, img=img, display=True,
        class_names=['license_plate', 'vehicle']
    )

Switch Models

python
# Change detector with one flag
model = ASOne(detector=asone.YOLOX_S_PYTORCH, use_cuda=True)

# Apple Silicon (M1/M2) — CoreML models
model = ASOne(detector=asone.YOLOV8L_MLMODEL)  # no GPU needed
model = ASOne(detector=asone.YOLOV5X_MLMODEL)
model = ASOne(detector=asone.YOLOV7_MLMODEL)

Run from Terminal

bash
# GPU
python -m asone.demo_detector data/sample_videos/test.mp4

# CPU
python -m asone.demo_detector data/sample_videos/test.mp4 --cpu
Model Support

Everything in One Library

AS-One supports the most widely-used models in the detection, tracking, and segmentation ecosystem — and is continuously updated as new architectures are released.

Detectors
  • YOLOv5 (PyTorch, ONNX)
  • YOLOv7 (PyTorch, ONNX)
  • YOLOv8 (PyTorch, ONNX, CoreML)
  • YOLOv9-C (PyTorch)
  • YOLOX (PyTorch, ONNX)
  • YOLO-NAS
  • PP-YOLOE
Trackers
  • ByteTrack
  • DeepSORT
  • NorFair
  • StrongSORT
  • OC-SORT
  • MoTPy
Segmentation
  • SAM (Segment Anything)
OCR
  • CRAFT (text detection)
  • EasyOCR (recognition)
Pose Estimation
  • YOLOv7-w6-pose
  • YOLOv8m-pose
  • YOLOv8l-pose
Roadmap
  • ✅ YOLOv5 / v7 / v8 / v9
  • ✅ YOLO-NAS
  • ✅ SAM Integration
  • ✅ Apple M1/M2 CoreML
  • ✅ Pose Estimation
  • ✅ OCR & Text Tracking

Need a Production
Computer Vision System?

AS-One helps you experiment fast. AxcelerateAI helps you scale to production — with custom models, edge deployment, and enterprise SLAs.

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FAQ

Common Questions