4032 × 3024
production
validated

All Your Visual Data. One Place.

Aggregate, organize, and explore billions of images and videos from any source. One unified repository for all your computer vision data.

See It In Action Documentation

How it works under the hood

Connects to S3, GCP, or Azure. Ingests any image or video format. Indexes everything so you can query it later.

DataTags

trainingproductionvalidatededge-case

Storage

2.4 TB
AWS S3 connected

Architecture

Sources

AWS S3
GCP
Azure

DATALAKE

2.4M
assets indexed
847GB
storage
12ms
latency

Outputs

Datasets: 24
Experiments: 156
Deployments: 8

upload.py
from picsellia import Client

client = Client()
datalake = client.get_datalake()

# Upload with metadata
datalake.upload_data(
  filepaths="./images/*.jpg",
  tags=["production", "batch-42"],
  metadata={"reference": "factory-A"}
)

# Query with filters
data = datalake.list_data(
  tags=["production"]
)

Python SDK v6.9.0
Auto EXIF extraction
Batch upload

query.py
# Query with tags
data = datalake.list_data(
  tags=["defects"]
)
# ✓ 2,847 results

# Query with custom_metadata filter
data = datalake.list_data(
  custom_metadata={"location": "factory-A"}
)
# ✓ 1,245 results

# Combine tags and dimensions
data = datalake.list_data(
  tags=["production", "validated"],
  limit=1000
)

Python SDK

tagsmetadatafilters

Image & Video Format Support

Ingest standard visual data formats

  • .jpg image
  • .png image
  • .tiff image
  • .webp image
  • .bmp image
  • .gif image
  • .mp4 video
  • .mov video

Processing Pipeline

Embeddings generation & database indexing

Live

  • Embedding Generation: 156 vec/sec
  • DB Indexing: 12ms/img
  • Ingestion Rate: 2,847 img/min
  • Storage Sync: 99.9%

Powerful Data Querying

Query your datalake programmatically with the Python SDK. Filter by tags, metadata, and more with full type hints and auto-completion.

list_data() PARAMS

  • tags: List[str]
  • custom_metadata: Dict[str, Any]
  • limit: int
  • offset: int
  • order_by: str

TAG OPERATIONS

  • add_tags(): add to data
  • remove_tags(): remove from data
  • list_tags(): get all tags
  • create_tag(): create new tag

FILTERABLE

  • tags: DataTags
  • custom_metadata: custom fields
  • filename: asset name
  • created_at: timestamps
  • type: image/video
# Advanced data query
# Advanced data query
data = datalake.list_data(
  # Filter by tags
  tags=["production", "validated"],
  # Filter by custom_metadata
  custom_metadata={
    "location": "factory-A"
  },
  limit=1000
)

for item in data:
  print(item.filename)

EXECUTION

  • 2,847 results
  • 23ms query time
  • 847MB scanned

MATCHED TAGS

  • production (1,892)
  • validated (2,103)
  • factory-A (1,245)
  • factory-B (892)

Visual Search

Find similar images instantly

OpenCLIP embeddings turn your images into vectors. Search by similarity, cluster by content, and spot outliers without writing a single query.

Default Model: ViT-B/16

  • Vector Size: 512-dim
  • Search Latency: <10ms

Similarity Search

Image → Images

cosine similarity > 0.85
847 matches

Text-to-Image Search

Text → Images
"damaged surface with rust"
156 results • 8ms

Anomaly Detection

Isolation Forest

  • contamination: 0.01
  • 23 corrupted
  • 89 outliers

Fine-tune Your Own CLIP Model

Generic embeddings not cutting it? Fine-tune a CLIP model on your own data. Search and clustering get much better when the model knows your domain.

  • Better accuracy: +40%

Learn more

DataTags & Metadata Schema

Multi-dimensional organization with flexible tagging and comprehensive metadata support. Structure your data without moving files.

DATATAGS SYSTEM

  • organization tags

AVAILABLE TAGS

  • factory-A(1,245)
  • factory-B(892)
  • production(1,892)
  • training(3,456)
  • edge-case(234)
  • validated(2,103)

inspection_042.tiff

4032x3024 - 12.4MB
factory-A production validated Q1-2024

METADATA FIELDS

{
  // Location & Acquisition
  "latitude": 48.8566,
  "longitude": 2.3522,
  "altitude": 35.2,
  "acquired_at": "2024-03-15T14:32:00Z",
  "acquired_by": "drone-unit-7",
  "weather": "clear, 18C",

// Camera & Sensor
  "focal_length": 24.0,
  "sensor_width": 36.0,
  "manufacturer": "DJI",
  "yaw": 127.5,
  "pitch": -45.0,
  "roll": 0.0,

// Reference Fields
  "reference": "INS-2024-0042",
  "custom_id": "B-789"
}

Auto-extracted from EXIF with fill_metadata=True

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