Same-platform handoff
Share EthoAI Cloud with Lite Maze Tracking — when the model does not fit, move into labeling and retraining without leaving the platform.
Pose Labeling
Pose Labeler
EthoAI Edition
Cloud platform for AI pose estimation — labeling, training, fine-tuning, sharing, and analysis in one workflow, made accessible and practical.
No codeCloud modeling
Multi-speciesFlexible
GPUCloud training
OVERVIEW
Built to work with Lite Maze Tracking
When a model does not fit your scene, stay on the same EthoAI Cloud to label data and retrain — no tool switching or data hopping.
Data Labeling
Label samples in the browser — no local install or complex setup.
Efficient cloud training
Cloud GPUs automate training, optimization, and validation — no local high-end hardware needed.
Scene Fine-Tuning
Fine-tune on your data for lighting, background, and apparatus differences.
Model Sharing
Share model projects and datasets — build a team model library over time.
Analysis Pipeline
Trained models connect directly to tracking, visualization, and export.
WORKFLOW
Six steps from labeling to training
From video upload to model testing — build pose models entirely in the cloud.
01
Upload Video
Add raw video data as the starting point for labeling and training.
02
Video Processing
Extract frames and prepare samples ready for annotation.
03
Target Annotation
Annotate targets and keypoints to build training labels.
04
Link Project
Optionally link Lite Maze Tracking and other project data to reuse labeled assets.
05
Model Training
Train a dedicated pose model on all project data.
06
Model Testing
Review test results and validate scene adaptation.
AI LABELING
Label one frame; AI assists multi-frame keypoint prediction
After labeling keypoints on one frame, the system predicts positions on following frames to assist labeling and reduce frame-by-frame work.
Label one frame first
Manually finish keypoints on a key frame and confirm target-to-point mapping. That frame becomes the seed so AI-assisted labeling starts from a correct baseline.
AI predicts other frames
After one frame is labeled, AI predicts keypoint positions on other frames and provides assisted labels in batch — no repetitive fine labeling on every frame.
Adjust only when needed
Predicted labels are ready for training prep. When occlusion, abrupt pose changes, or drift appear, tweak locally — spend time on quality control, not repetitive work.
Much faster labeling
Replace frame-by-frame clicking with “label one, predict many.” Keep keypoint quality while handing repetitive work to AI assistance — datasets come together faster.
KEYPOINTS
Flexible labeling & training across species and paradigms
Freely define keypoint count and placement for diverse species and behavioral assays.
Define count and placement
Set keypoint count and placement to match your experiment — not locked to fixed templates.
Multi-species & paradigms
Supports mice, rats, zebrafish, flies, dogs, cats, pigs, monkeys, and assays like forced swim, novel object, and social.
FINE-TUNING
Iterative fine-tuning on an existing model
When an existing model no longer matches later data, add labels on top of it and retrain to improve scene adaptation.
Detect model–data drift
After lighting, background, apparatus, or strain changes, the old model may drift.
Add labels on the base model
No rebuild from scratch — add labels for the new scene on top of the existing model.
Strengthen via advanced training
Run advanced training on new labels to obtain a model better fit to current data.
Improve stability and accuracy
Reduce head/tail swaps, target loss, and weak cross-scene stability.
Try Pose Labeler EthoAI Edition
Sign in to EthoAI Cloud to try it, or contact us for a demo and technical support.