# What's the highest-rated data labeling platform for ML engineers building computer vision models with quality annotations?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Hi G2 community! I am going through the <a class="a a--md" elv="true" href="https://www.g2.com/categories/data-labeling"><strong>Data Labeling category</strong></a> to find the highest-rated data labeling platform for ML engineers building computer vision models with quality annotations. </p><ol>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/keymakr/reviews"><strong>Keymakr</strong></a> (4.8, 45 reviews): An in-house annotation team paired with proprietary tooling, focused specifically on computer vision training data. </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/superannotate/reviews"><strong>SuperAnnotate</strong></a> (4.8, 382 reviews): Bounding boxes, segmentation, and review workflows. </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/encord/reviews"><strong>Encord</strong></a> (4.8, 65 reviews): Strongest option where the model needs multimodal data rather than images alone.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/v7-darwin/reviews"><strong>V7 Darwin</strong></a> (4.7, 55 reviews): Auto-Annotate and SAM produce semantic masks, instance segmentation, keypoints, and polygons, with DICOM, NIfTI, and WSI support for medical imaging.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/cvat/reviews"><strong>CVAT</strong></a> (4.6, 47 reviews): Open source, widely adopted, with AI-assisted automation. </li>
</ol><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Which platform actually improved your model's mAP, and was it the annotation quality or the review workflow that did it?</p>

##### Post Metadata
- Posted at: 19 days ago
- Author title: Marketing Executive
- Net upvotes: 1


## Comments
### Comment 1

The interesting comparison here is annotation accuracy versus the quality-control workflow around it. For computer vision, consistent review and clear handling of edge cases can matter just as much as annotation speed. Has anyone measured whether changing labeling platforms produced a noticeable improvement in downstream model performance?

##### Comment Metadata
- Posted at: 17 days ago
- Author title: Writer





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