In machine learning, bbox (bounding box) detection acts like a digital treasure hunt within images. We train a model to unearth objects and mark their location with a rectangular box. Unlike image classification that just sorts images into categories, bbox detection pinpoints exactly where the object is. The model learns from a treasure map of sorts – a dataset with images where objects are surrounded by bounding boxes. Deep learning models like YOLO, SSD (Single Shot MultiBox Detector), and Faster R-CNN excel at this task, efficiently sifting through image data to find objects and mark their territory. After training, the model becomes an expert treasure hunter, uncovering objects and their locations in new images. This skill is crucial for tasks like autonomous vehicles that need to identify and react to objects around them, or medical imaging analysis where pinpointing specific regions is vital.

At our company, we take image training to the next level with our exceptional bbox detection labeling services. We empower your object detection models with precise bounding box annotations, the cornerstone for accurate object localization. Our highly skilled annotators meticulously hand-label each image, ensuring perfect box placement and clear class identification. Rigorous quality assurance processes guarantee that every box meets the strictest industry standards. Furthermore, our efficient workflows deliver exceptional results at competitive rates, making us the perfect partner for projects of all sizes. Whether you’re a budding startup or a seasoned enterprise, we customize our solutions to your specific needs. By choosing us, you gain more than just an annotation service; you gain a trusted partner in achieving superior object detection performance.

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Transparent Pricing Structure

We  follow a clear pricing structure for our BBox detection annotation services. Here’s a breakdown of the key pricing structure:

To initiate work, you prepay a portion of the total cost based on the batch size, tiered upfront payments are as follow:

After the annotations are complete, you pay the remaining balance based on the per-annotation cost.

Understanding Project Complexity

We understand that for some projects, especially those involving complex or subjective BBox detection tasks, accurately estimating the number of annotations upfront can be challenging. We offer flexibility in such cases:.

Transparency and Communication

To ensure a smooth and collaborative process we aim for clear communication, centralized information management, and the flexibility to adapt to the specific needs of your project:

Annotation Package - Option 1 (Range 0 to 10k)
For 0 to 10k annotations of BBox detection, there is no per annotation option, no matter the size, 100$ should be paid in full for each batch that you order.
Annotation Package - Option 2 (Range 10k to 50k)
For 10k to 50k annotations of BBox detection we use 0.01 per annotation option, at first 100$ should be paid before we initiate the work. Remaining should be paid after the work is done.
Annotation Package - Option 3 (Range 50k to 200k)
For 50k to 200k annotations of BBox detection we use 0.01 per annotation option, at first 500$ should be paid before we initiate the work. Remaining should be paid after the work is done.
Annotation Package - Option 4 (Range 200k to 400k)
For 200k to 400k annotations of BBox detection we use 0.01 per annotation option, at first 2000$ should be paid before we initiate the work. Remaining should be paid after the work is done.
Annotation Package - Option 5 (Range more than 400k)
For more than 400k annotations of BBox detection we use 0.01 per annotation option, in general you will pay approximately 25% beforehand and after the work is done you will pay the remaining.

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