2026年9月17日に公開 · 2026年9月20日時点で募集中であることを確認済みです
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I have a collection of images that I’m preparing for an object-detection model and I need each relevant object precisely framed with bounding boxes. All images require consistent, pixel-accurate annotation so the resulting labels can be fed straight into a training pipeline without extra cleanup. Here’s what I need from you: • Draw a tight, non-overlapping bounding box around every target object in each image (guidelines and class list will be provided once we start). • Export the annotations in a standard format—COCO JSON, Pascal VOC XML, or YOLO TXT are all acceptable as long as the file naming matches the source images. • Keep original image resolution untouched; store labels in a mirrored folder structure. • Double-check each bounding box for missed objects or off-target padding before delivery. If you already use tools like LabelImg, CVAT, or makesense.ai, let me know—efficiency matters. Accuracy is the priority, so please share any previous annotation samples or quality-control steps you follow.
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