MLOps

Track Experiments
Ship Better Models

Full reproducibility with metrics, parameters, and artifacts. Compare runs, collaborate with your team, and version models automatically.

Works with:

+more

See It In Action Documentation

Best Model

mAP: 0.91 • exp-003

Training on GPU

Logging

Log everything, miss nothing

Capture metrics, parameters, artifacts, and evaluations with a simple API. Full lineage tracking with minimal integration code.

Scalar

Single values (loss, accuracy)

Line

Time-series metrics

Image

Visualizations & samples

Table

Structured data

Histogram

Distributions

Confusion Matrix

Classification results

train.py

from picsellia import Client

client = Client()
project = client.get_project("my-project")
experiment = project.get_experiment(
  "my-experiment"
)

# Log hyperparameters
experiment.log_parameters({
  "learning_rate": 1e-4,
  "batch_size": 32
})

# Log metrics during training
for epoch in range(epochs):
  experiment.log(
    "train_loss",
    loss.item()
  )

Artifact Storage

Store checkpoints, configs, and outputs

experiment.store("model.pt")

Dataset Attachment

Link training data for reproducibility

experiment.attach_dataset(dataset_version)

Model Export

Push to model registry with one call

experiment.export_as_model("my-model")

Compare

Compare trainings instantly

See the exact training distribution, hyperparameters, and augmentations behind every performance change. Find your best model faster.

Side-by-side metric comparison

Parameter diff highlighting

Dataset version tracking

Collaborative comments

EXPERIMENT COMPARISON

Name	mAP	Loss	LR	Epochs	Status
exp-001	0.87	0.09	1e-4	100	completed
exp-002	0.82	0.12	1e-3	80	completed
exp-003	0.91	0.07	5e-5	150	running

CV Engine

Build training pipelines with ease

Picsellia CV Engine is a modular toolkit for constructing computer vision workflows. Composable steps, framework extensions, and CLI automation.

Training Pipelines

Data → Model → Results. Streamlined training processes with built-in logic.

UltralyticsPyTorch

Processing Pipelines

Dataset transformation, pre-annotation, and data cleaning operations.

SAM2CLIP

Framework Extensions

Pluggable architecture supporting multiple training libraries.

Grounding DINO

terminal

$ pip install picsellia-cv-engine

# Initialize a new training pipeline

$ pxl-pipeline init --type training

# Run locally for testing

$ pxl-pipeline test

# Deploy to Picsellia cloud

$ pxl-pipeline deploy --gpus 1

CLI + Python decoratorsView Documentation →

MODEL REGISTRYyolo-defect-detector

Ultralyticslatest

2 hours ago

0.91

42MB

Ultralytics

1 day ago

0.87

42MB

Ultralytics

3 days ago

0.82

41MB

Model Registry

Version your models automatically

Export experiments to the model registry with a single call. Track versions, compare performance, and deploy with confidence.

Automatic version incrementation

Framework metadata (TensorFlow, PyTorch, etc.)

Docker configuration for deployment

Lineage to training experiment

Evaluation

COCO metrics built-in

Add predictions, compare against ground truth, and compute standard evaluation metrics automatically.

0.91

mAP@50

Mean Average Precision

0.68

mAP@50:95

Strict mAP

0.89

Precision

True Positives / Predicted