using Fasterrcnn for regression or for only finding the bounding box in an image
Is it possible to use FasterRcnn in pytorch just for finding the bounding box without considering the classification part ?
And is it possible to change the loss of the classification part ( categorical cross entropy ) to a regression loss ( MSE ) ?

For the first part of your question, please have a look at the line where the bounding boxes are received in demo.

https://github.com/jwyang/faster-rcnn.pytorch/blob/31ae20687b1b3486155809a57eeb376259a5f5d4/demo.py#L297

rois, cls_prob, bbox_pred, \
      rpn_loss_cls, rpn_loss_box, \
      RCNN_loss_cls, RCNN_loss_bbox, \
      rois_label = fasterRCNN(im_data, im_info, gt_boxes, num_boxes)

      scores = cls_prob.data
      boxes = rois.data[:, :, 1:5]


For the second part, yes, it is possible. But you have to make sure that a regression loss such as is suitable for the task that you are planning to use the network for.
