Image Bounding Box Annotation

I need a reliable hand to draw clean, tight bounding boxes on a set of images so I can feed them straight into an object-detection pipeline. Every occurrence of the target object in each frame must be boxed precisely—no clipped edges or excessive margins—so the model learns from accurate ground-truth data. You are free to work in any mainstream annotation tool you prefer (LabelImg, CVAT, Labelbox, VGG, makes no difference to me) as long as the final export matches the format I’ll specify before we kick off—COCO JSON or YOLO-style TXT are both fine. Please keep category labels consistent across the entire batch. Deliverables • Fully annotated image set with bounding boxes around every instance of the specified object(s) • Corresponding annotation files in the agreed-upon format • A short note flagging any ambiguous images or edge cases you encounter Acceptance criteria • Boxes tightly enclose each object without touching adjacent items • Zero empty or mislabeled files • Random spot-check accuracy ≥ 98 % If this fits your skill set, let’s move forward—once I confirm your tooling preference I’ll share the image package and category list.