8. Exporting the results¶

SLEAP stores labeled data, predictions, and all metadata in .slp files. These files use a custom format that is optimized for SLEAP workflows and contain metadata, but ultimately are just HDF5 files that implement our data model.

These .slp files are not intended for final use in analysis since they require SLEAP to be parsed appropriately.

Once you're ready to analyze your results, you have several options for different file formats for export. While these do not contain all the metadata used during the labeling and training stage, they are more convenient for analysis and portable for use in different downstream software and libraries.

We recommend exporting your proofread predictions to each of these formats so you can explore your data in the next step of the tutorial.

Comprehensive format documentation

For detailed format specifications and programmatic access, see the sleap-io formats documentation.

Quick Export from GUI¶

The easiest way to export is through the File menu:

Menu Option	Output Format	Best For
Export Analysis HDF5...	`.h5`	MATLAB, NumPy arrays
Export Analysis CSV...	`.csv`	Spreadsheets, pandas
Export NWB...	`.nwb`	Neuroscience data sharing

Export Formats¶

NWB¶

The Neurodata Without Borders (NWB) format provides a data standard for neural and behavioral data.

We strongly recommend NWB for its portability, standardization, and compatibility with the scientific ecosystem. NWB data can be hosted for free in the DANDI Archive.

SLEAP natively supports NWB through the ndx-pose extension.

GUI: File → Export NWB...

CLI:

sleap convert predictions.slp -o data.nwb

For reading NWB files, see the NWB reading guide.

Analysis HDF5¶

The HDF5 format stores pose data as dense NumPy arrays, ideal for MATLAB and Python.

GUI: File → Export Analysis HDF5... → Current Video...

CLI:

sleap export predictions.slp -o analysis.h5

Reading in MATLAB¶

tracks = h5read('analysis.h5', '/tracks');
occupancy = h5read('analysis.h5', '/track_occupancy');
node_names = h5read('analysis.h5', '/node_names');

% Get coordinates for track 1, node 1, frame 100
x = tracks(1, 1, 1, 100);
y = tracks(1, 2, 1, 100);

Reading in Python¶

import h5py

with h5py.File('analysis.h5', 'r') as f:
    tracks = f['tracks'][:]             # (n_tracks, 2, n_nodes, n_frames)
    occupancy = f['track_occupancy'][:]  # (n_frames, n_tracks)
    node_names = [n.decode() for n in f['node_names'][:]]

print(f"Tracks shape: {tracks.shape}")
print(f"Nodes: {node_names}")

For dataset schemas and axis ordering, see the sleap-io Analysis HDF5 documentation.

CSV¶

Comma-separated value (CSV) files are the simplest format for tabular data, compatible with Excel, Google Sheets, and pandas.

GUI: File → Export Analysis CSV... → Current Video...

CLI:

sleap export predictions.slp -o analysis.csv

Command-Line Export¶

For batch processing or scripting, use the sleap export command:

# Export to CSV (dense, with empty frames padded)
sleap export predictions.slp -o analysis.csv

# Export to Analysis HDF5
sleap export predictions.slp -o analysis.h5

# Export only frames with instances (sparse)
sleap export predictions.slp -o sparse.csv --no-empty-frames

# Export specific video from multi-video file
sleap export multi.slp -o video0.csv -v 0

# Export all videos from multi-video file
sleap export multi.slp -o batch.csv -v all
# Creates: batch.video0.csv, batch.video1.csv, ...

# Memory-efficient chunked export for large files
sleap export large.slp -o analysis.csv --chunk-size 10000

See sleap export --help or the sleap-io CLI documentation for all options.

Command-Line Conversion¶

For converting between label formats, use sleap convert:

# Convert to NWB (Neurodata Without Borders)
sleap convert predictions.slp -o data.nwb

# Convert to COCO format
sleap convert predictions.slp -o annotations.json --to coco

# Create a portable package with embedded frames
sleap convert labels.slp -o labels.pkg.slp --embed user

# Export to Ultralytics YOLO format
sleap convert labels.slp -o yolo_dataset/ --to ultralytics

See sleap convert --help or the sleap-io CLI documentation for all options.

All Supported Formats¶

Format	Extension	CLI	Python API	Documentation
Analysis HDF5	`.h5`	✅	✅	sleap-io docs
CSV	`.csv`	✅	✅	sleap-io docs
NWB	`.nwb`	✅	✅	sleap-io docs
COCO	`.json`	✅	✅	sleap-io docs
Label Studio	`.json`	✅	✅	sleap-io docs
Ultralytics	directory	✅	✅	sleap-io docs
JABS	`.h5`	✅	✅	sleap-io docs

sleap-io Python API¶

For programmatic export and conversion, use sleap-io:

import sleap_io as sio

# Load labels
labels = sio.load_file("predictions.slp")

# Export to different formats
sio.save_file(labels, "output.nwb")
sio.save_file(labels, "output.csv")

See the sleap-io documentation for full API details.

You did it!

Next up: I'm done SLEAPing, now what?