Live · v2026.05
NEXEN logoNEXEN
★ Training-Ready Data Infrastructure™

train/modelsskip·datasetengineering.

Your team should be training models, not cleaning datasets. We deliver training-ready computer vision datasets with built-in QA, dedup, long-tail balancing, validation splits, and hard-negative curation.Download → Train → Deploy.

— no annotation chaos · no relabeling
training-ready★dedup'd✦long-tail balanced★hard negatives✦predefined splits★zero-prep before training✦
training-ready★dedup'd✦long-tail balanced★hard negatives✦predefined splits★zero-prep before training✦
02 / Position

You aren't buying "annotated data". You're buying six months of hidden data engineering pain removed from your roadmap.

The pipeline.

03 / Stack
/ours — full pipeline

From raw clips to training-ready.

Raw → Collect → Label → Validate → Enrich → Analyze → Optimize → Predict → Train

Raw → Collect
  • 01
    Raw data ingest
    Video, image, multi-source intake at any scale.
  • 02
    Collection planning
    Scenario coverage, geographies, device mix.
Label
  • 03
    Annotation
    Boxes, polygons, segmentation, keypoints, tracking.
  • 04
    Multi-layer QA
    Reviewer + auto-checks + measurable error rates.
Validate
  • 05
    Deduplication
    Frame, clip and near-duplicate removal.
  • 06
    Metadata enrichment
    Weather, lighting, occlusion, scene context.
  • 07
    Difficulty tagging
    Easy / medium / hard sample stratification.
  • 08
    Long-tail balancing
    Rare-class oversampling, head-class capping.
  • 09
    Predefined train / val / test
    Leakage-safe splits ready to load.
  • 10
    Training-ready dataset
    Drop-in artifact, zero prep before epoch 1.
Enrich
  • 11
    Dataset Health & EDA Reports
    Class distribution, imbalance detection, leakage checks, quality diagnostics.
  • 12
    Smart Preprocessing
    Dynamic crop, object isolation, tiling, grayscale, denoise, normalization.
  • 13
    Adaptive Augmentation
    Weather, blur, occlusion, lighting, viewpoint, scenario-aware augmentation.
  • 14
    Hard Negative Curation
    Reflections, mannequins, posters, screens, confusing lookalikes.
  • 15
    Duplicate & Near-Duplicate Clustering
    Similarity analysis and burst-frame detection — not just removal.
Analyze
  • 16
    Embedding Generation
    CLIP / DINO embeddings for similarity search and dataset comparison.
  • 17
    Data Quality Scoring
    Per-image quality, blur, visibility, confidence, anomaly score.
  • 18
    Dataset Readiness Score™
    Completeness, diversity, balance, leakage risk.
Predict
  • 19
    Failure Prediction Analysis
    Likely model blind spots surfaced before training begins.
  • 20
    Edge Case Discovery
    Rare conditions, long-tail scenarios, unseen environments.
Optimize → Train
  • 21
    Synthetic Data Expansion
    Generate missing scenarios and underrepresented classes.
  • 22
    Dataset Versioning & Lineage
    Track every change across iterations.
  • 23
    Multi-format Export
    COCO, YOLO, segmentation masks, polygons, JSON.
  • 24
    Smart Sampling
    Suggested next samples to collect based on uncertainty.
  • 25
    Dataset Cards & Bias Reporting
    Source, demographics, geography, limitations, known gaps.
/competitors — stops here

Raw → Label → Deliver.

Then your ML team spends the next 6 months cleaning, balancing, deduping, splitting and re-labeling before a single epoch runs.

  1. Raw
  2. Label
  3. Deliver
26 / Privacy

Privacy & Data Sanitization

Built into every delivery. Compliance-ready out of the box.

  • ✓Face blurring
  • ✓License plate masking
  • ✓PII redaction
  • ✓Text redaction
  • ✓Logo masking
  • ✓Screen content masking
  • ✓Body / identity anonymization
  • ✓Object removal
  • ✓Segmentation-based selective blur
  • ✓Video tracking + persistent masking across frames
  • ✓Audio PII removal
  • ✓Custom sensitive-region masking
04 / Deliverables

What you receive.

A drop-in artifact. Unzip, point your trainer at it, run. No prep, no glue scripts, no surprises at epoch 12.

✓Training-ready train / val / test split
✓QA reports with measurable metrics
✓Hard-negative examples
✓Occlusion, weather, lighting tags
✓Duplicate detection & removal
✓Embeddings for similarity search
✓COCO, YOLO, JSON exports
✓Dataset card & bias reporting
✓Long-tail class balancing
✓Zero preparation before training

Why models
fail.

05 / Truth

Models rarely fail because of architecture. They fail because of the data underneath.

Duplicated video framessolved pre-train
Train-test leakagesolved pre-train
Weak edge casessolved pre-train
Class imbalancesolved pre-train
Poor hard negativessolved pre-train
Annotation inconsistencysolved pre-train

→ We solve these before training starts.

06 / Velocity

Reduce iteration cycles.

Most teams burn weeks per loop. Tighten the loop and the model converges before the deck is even shipped.

Typical workflow4–8 weeks

Collect → label → train → discover failures → recollect → relabel

With NEXENDays

Collect → train.

07 / End

zero·prep
vision data

No annotation chaos. No relabeling. No months fixing data. Just train.

Onboarding ML teams weekly