Machine Learning Dataset Management

Get to know the data that powers your AI with V7 Darwin

Built to help ML teams keep track of their most valuable asset.

Load any image or video format

Supporting JPG, PNG, TIF, MP4, MOV, SVS, DICOM and counting. Upload data for video understanding to ultra-high resolution histology slides and browse them from the same platform.

Track annotation progress

Image status dynamically changes as your project advances, allowing you to train models on completed sections at any time.

Rainforest airplane n2.png
New
Assigned
In Progress
Pending Review
In review
Complete
Amazon_rainforest_aerial.png
Complete
Amazon_rainfor.png
Complete

Search for the right image

Filter by upload date, annotator, tag, classes, and labeling progress. Preview images in a flash, powered by a high speed Elixir back-end.

Filter by annotator
Henry Truman
Filter by label
Brown Bear
Ferret
Shrubbery
Filter by upload
18/18/19
19/18/19

Balance your data

Classes by number of Instances
Classes by number of Images
Ascent Engine
Control Engine
Lunar Module
Booster
Tower Jettison Motor
Launch Escape System
Metal Panel Weld
Switch
Instrument Unit
Rivet

Take a glance at your dataset's composition. Troubleshoot your model's performance by taking a look at class representation and instance frequency.

Collaborate

All the data scientists you work with should know exactly what your ground truth looks like. Invite unlimited team members, annotators, and collaborate in real time.

Version control

Never lose track of your annotation versions as your dataset evolves. Reference each model to a dataset version.

3.0
20/05/2020
317k Images
14 Classes
2
12
2.0
13/04/2020
317k Images
12 Classes
2
10
1.1
28/3/2019
224k Images
6 Classes
1
5
1.0
12/01/2020
172k Images
6 Classes
1
5

Developer Kit Included

$ darwin create example-dataset
Dataset 'example-project' has been created.
Access at https://darwin.v7labs.com/datasets/example-project
$ darwin upload example-dataset -r path/to/images
Uploading: 100%|########################################################| 100000/100000 [00:01<00:00,  2.29it/s]
$ darwin pull example-project
Pulling project example-project:latest
Downloading: 100%|########################################################| 100000/100000 [00:03<00:00,  4.11it/s]
from darwin.client import Client

client = Client.local()
client.create_dataset(name="My New Dataset")
from darwin.client import Client

client = Client.default()
dataset = client.get_remote_dataset(slug="example-dataset")
progress = dataset.upload_files(["test.png", "test.mp4"])
for _ in progress():
   print("file uploaded")
from darwin.client import Client

client = Client.default()
dataset = client.get_remote_dataset(slug="example-dataset")
progress, _count = dataset.pull()
for _ in progress():
   print("file synced")
Load images and annotations directly into your favourite framework
from torch.utils.data import DataLoader
from darwin.torch.dataset import Dataset

# Instantiate your Darwin dataset
dataset = Dataset('team-name/dataset-name', validation=0.2, test=0.1)
# Use it in a simple PyTorch dataloader
dataloader = DataLoader(dataset, batch_size=16, shuffle=True)
Browse V7 Darwin's Python Library
Visit Darwin.Py

Ready to get started?

Schedule a demo with our team or discuss your project.