如何实现仅获取边界框的目标定位，无需目标分类？

Hey there! Let's tackle your two object localization needs clearly—both are totally achievable with a few targeted tweaks to standard workflows.

1. How to Get Only Bounding Boxes Without Object Classification #

If you don't care about what the object is, just where it is, you can simplify detection models or use specialized approaches:

• Modify existing detection models
  Take Faster R-CNN as an example: strip out the classification head entirely, leaving only the bounding box regression branch. Instead of training to predict both class probabilities and box offsets, you'll only optimize for box regression loss (like Smooth L1 Loss). This focuses the model solely on learning object positions, not categories.
• Use category-agnostic detection algorithms
  Algorithms like CornerNet or CenterNet are built around predicting key points (corners or centers of objects) rather than class labels. You can adapt these to output only the key points, then reconstruct bounding boxes from them—no classification branch needed. These are end-to-end and work well for general object localization.
• Traditional computer vision methods (for simple scenarios)
  If your objects have distinct visual features (color, texture, shape), you can use sliding window combined with threshold segmentation (e.g., color-based masking) or HOG feature matching. This is less robust than deep learning but works for straightforward use cases without needing training data.

2. Custom Dataset: Detect Object Presence + Get Bounding Boxes (No Classification) #

For your custom dataset (non-ImageNet/COCO), the key is adjusting your data annotation and model setup to focus on presence/position rather than categories:

• Adjust dataset annotations
  You don't need to label different object classes. Instead:
  • For images with objects: annotate all target bounding boxes, assigning a single universal label (e.g., "object") to every box.
  • For images without objects: mark them as negative samples (no boxes needed, or a single "background" label if your tool requires it). Tools like LabelImg make this easy—just reuse the same label for all objects.
• Adapt your model
  • YOLO Series: This is super straightforward. In the model config file (e.g., YOLOv5's yolov5s.yaml), set nc: 1 (number of classes = 1). Train using standard YOLO loss, but the classification part becomes a binary task (object vs. background). During inference, any box with confidence above your threshold means an object exists, and you just take those box coordinates.
  • Faster R-CNN: Set the number of classes to 2 (background + object). The model will learn to distinguish between background and your target objects, and you can ignore the class label during inference—just keep boxes with high confidence scores.
• Inference handling
  After inference, filter boxes by confidence threshold:
  • If there are boxes above the threshold: the image contains objects, and those are their bounding boxes.
  • If no boxes meet the threshold: the image has no objects.

Quick Tips #

• Make sure your dataset includes enough negative samples (images without objects) so the model learns to correctly identify empty scenes.
• Anchor-free models (like YOLOX's anchor-free variant) can be easier to tune for this use case, as you don't need to adjust anchor box sizes for your custom objects.
• When modifying model heads, double-check that you're only calculating loss for the regression (and binary classification, if needed) branches—don't leave unused classification loss functions in your code.

内容的提问来源于stack exchange，提问作者eulerx