Mmodlist < Must Watch >

1. What is mmodlist ? mmodlist (short for Max-Margin Object Detection List ) is not a standalone class but a specific data format used by dlib’s fhog_object_detector and loss_mmod_ layers. It is a list of mmod_rect objects.

options = dlib.simple_object_detector_training_options() options.C = 1 options.num_threads = 8 options.add_left_right_image_flips = True mmodlist

mmodlist = detector.run(image) # returns mmod_rects rects = [m.rect for m in mmodlist] # just boxes labels = [m.label for m in mmodlist] # class predictions ignored = [m.ignore for m in mmodlist] # usually False for output | Problem | Likely cause | |--------|---------------| | Training crashes with “empty mmodlist” | An image has zero mmod_rect s. That’s allowed, but check that your dataset isn't entirely empty. | | Loss stays high | ignore=True used incorrectly on positive samples. | | Detector outputs wrong class | Mismatch between training labels and test-time expectations. | | Memory explosion | Too many mmod_rect s per image (e.g., 1000+ small objects). Use ignore for tiny or edge objects. | 8. mmodlist in C++ (for custom dlib extensions) If you work with dlib’s C++ API: It is a list of mmod_rect objects

#include <dlib/image_processing.h> using namespace dlib; | | Loss stays high | ignore=True used

Under the hood, train_simple_object_detector converts mmodlist into the internal MMOD loss format. The ignore flag is critical for hard examples:

detector = dlib.train_simple_object_detector(images, mmodlists, options) img = dlib.load_rgb_image("test.jpg") dets = detector(img) for det in dets: print(f"Class det.label at det.rect, score det.detection_confidence")