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Viet-Anh on Software

AI & Software Engineering

Beginner11 lessons11 lessons

AnyLabeling: AI-Powered Data Annotation

Master data annotation with AnyLabeling — from manual labeling to AI-assisted workflows with SAM and YOLO, custom model integration, export pipelines, and active learning strategies for production ML teams.

AnyLabelingData AnnotationSegment AnythingYOLOComputer VisionMachine Learning
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AnyLabeling: AI-Powered Data Annotation

Course Curriculum

11 Lessons
01

Lesson 01: Why Annotation Matters

Understand why data annotation is the highest-leverage activity in applied ML, how the tool landscape has evolved, and where AnyLabeling fits.

March 12, 2026
02

Lesson 02: Installation & First Labels

Install AnyLabeling via pip, binary, or GPU-accelerated package, tour the interface, and annotate your first image in under five minutes.

March 12, 2026
03

Lesson 03: Manual Annotation Deep Dive

Master every annotation type in AnyLabeling — rectangles, polygons, circles, lines, points, and rotated bounding boxes — with the techniques that make manual labeling fast and precise.

March 12, 2026
04

Lesson 04: SAM Auto-Labeling

Use Segment Anything models (SAM, SAM 2, SAM 2.1, SAM 3, MobileSAM) for one-click segmentation — point prompts, rectangle prompts, text prompts, and the workflow that makes it fast.

March 12, 2026
05

Lesson 05: YOLO Auto-Labeling

Use YOLOv5 and YOLOv8 models to auto-generate bounding boxes and segmentation masks, then review and correct the results for production-quality datasets.

March 12, 2026
06

Lesson 06: Text, OCR & Key Information Extraction

Annotate text in images — detection regions, transcription, and structured Key Information Extraction (KIE) for documents, receipts, and scene text.

March 12, 2026
07

Lesson 07: Export Formats & Pipelines

Export annotations to COCO, YOLO, Pascal VOC, and CreateML formats — understand when to use each one and build conversion scripts for your training pipeline.

March 12, 2026
08

Lesson 08: Custom Models for Auto-Labeling

Load your own ONNX models into AnyLabeling — convert from PyTorch or Ultralytics, write the config.yaml, and use domain-specific models as auto-labeling backends.

March 12, 2026
09

Lesson 09: Writing Annotation Guidelines

Write annotation guidelines that eliminate ambiguity, handle edge cases, and produce consistent labels across annotators — the most underrated skill in applied ML.

March 12, 2026
10

Lesson 10: Active Learning Pipelines

Build a label-train-relabel loop that improves both your model and your dataset with every iteration — confidence-based sampling, model-in-the-loop annotation, and knowing when to stop.

March 12, 2026
11

Lesson 11: Scaling Annotation for Teams

Scale annotation beyond a single person — multi-annotator workflows, quality assurance reviews, conflict resolution, and dataset versioning for production ML teams.

March 12, 2026

Course Details

Lessons11
Duration11 lessons
Difficultybeginner

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