Une IA qui s'adapte aux flux de déchets changeants

Les emballages changent. Les matériaux évoluent. Votre IA aussi. Construisez des modèles de tri qui apprennent et s'adaptent en continu.

Model Updates

  • Weekly
  • Classification: 95.2%
  • Drift Monitoring: 24/7
  • 50M+ Items classified daily
  • 95% Sorting accuracy
  • 40% Cost reduction
  • <50ms Classification speed

The Challenge

Votre training data is already outdated

Static models don't work in waste management. New packaging hits shelves every week. Seasonal products come and go. Brand redesigns happen overnight. Your sorting AI needs to adapt — or accuracy plummets.

New packaging every week

Brands redesign, seasonal editions launch, new materials enter the market. Your model trained on last year's packaging is already outdated.

Regional variations

Products in France differ from Germany. Local brands, different recycling symbols, varying material compositions across regions.

Mixed & damaged items

Crushed cans, torn labels, multi-layer packaging. Real-world waste looks nothing like pristine training images.

Model drift over time

A model that worked great in January underperforms by June. Without monitoring, you won't know until sorting quality drops.

73% of waste AI projects fail due to model degradation over time.

  • Model trained: January
  • New products launched: +2,400/month
  • Accuracy by June: ↓ 15%
  • Without continuous learning, your sorting line misclassifies thousands of items daily.

The Solution

Continuous learning, not one-time training

Picsellia creates a closed loop: monitor production, catch drift, retrain, and redeploy — automatically.

  1. Deploy & Monitor

Models run on your sorting line. Every prediction is logged with confidence scores.

  1. Detect Drift

When confidence drops or new item types appear, the system flags them automatically.

  1. Smart Labeling

Low-confidence samples are routed to human reviewers. AI pre-labels, experts validate.

  1. Retrain & Redeploy

Updated models are trained on new data and deployed without downtime.

Continuous loop — models improve every week.

Applications

Built for sorting facilities

From MRFs to collection points, Picsellia powers waste classification across the recycling chain.

  • 95% Classification accuracy

Automated Waste Sorting

Classify materials on conveyor belts in real-time. PET, HDPE, cardboard, aluminum — your model learns to distinguish them all, even as packaging evolves.

  • 60% Less contamination

Contamination Detection

Spot contaminated recyclables before they ruin an entire batch. Identify food residue, mixed materials, and non-recyclable items in the stream.

  • 30% Fewer truck runs

Fill Level Monitoring

Track container fill levels across your network. Optimize collection routes based on actual need, not fixed schedules.

  • 100% Traceability

Material Composition

Analyze waste streams to understand what's coming through. Generate reports for regulatory compliance and recycling optimization.

Platform

Tools for continuous improvement

Every feature in Picsellia is designed for iteration. Collect new samples, retrain fast, and push to production.

Model Monitoring

Catch drift before it hurts.

  • Track prediction confidence in real-time. When your model sees packaging it wasn't trained on, you'll know immediately — not weeks later when sorting quality drops.
  • Confidence score tracking
  • Drift detection alerts
  • Performance dashboards
  • Anomaly detection

Active Learning

Label smarter, not harder.

  • Low-confidence predictions are automatically queued for review. Your team labels only the samples that matter — the edge cases your model struggles with.
  • Uncertainty sampling
  • Pre-annotation
  • Review workflows
  • Quality assurance

Automated Pipelines

Retrain on schedule or trigger.

  • Set up pipelines that retrain models weekly, or trigger retraining when enough new samples accumulate. Deploy updated models without stopping your line.
  • Scheduled retraining
  • Trigger-based updates
  • A/B model testing
  • Zero-downtime deployment

The result? Models that never go stale.

Keep 95%+ accuracy year-round, even as packaging changes weekly.

  • 52 Retraining cycles/year
  • <1% Accuracy variance

Integration

Connects to your sorting infrastructure

Picsellia integrates with existing camera systems, conveyor controls, and sorting robots. Deploy models to edge devices or process in the cloud — your choice.

  • Camera Integration: Works with industrial cameras, line-scan sensors, and hyperspectral imaging
  • Edge Deployment: Run inference on NVIDIA Jetson, industrial PCs, or embedded systems
  • PLC & SCADA: Send classification signals to sorting actuators and robotics
  • Data Pipelines: Automatic upload of production images for retraining datasets
  • MES Integration: Feed classification data into manufacturing execution systems
  • Edge Performance: Optimized for industrial throughput

<50ms Latency
20 FPS Throughput
ONNX/TRT Models
99.9% Uptime

CONTINUOUS_LEARNING

Ready to future-proof your sorting AI?

See how Picsellia keeps waste classification accurate — even as packaging evolves every week.

  • 50M+ Items/day
  • 95%+ Accuracy maintained
  • Weekly Model updates
  • <50ms Classification