Without mmodlist , you cannot train a multi-class or ignore-aware CNN detector in dlib.
detector.save("mmod_detector.svm")
If you work with dlib’s C++ API:
options = dlib.simple_object_detector_training_options() options.add_left_right_image_flips = True options.C = 5 options.num_threads = 4 options.be_verbose = True mmodlist
def load_mmodlist_from_annotation(image_path, annotation_dict): img = dlib.load_rgb_image(image_path) rects = [] for obj in annotation_dict['objects']: r = dlib.rectangle( left=obj['x'], top=obj['y'], right=obj['x']+obj['w'], bottom=obj['y']+obj['h'] ) # label: 0 for 'car', 1 for 'pedestrian', etc. m = dlib.mmod_rect(r, label=obj['class_id'], ignore=obj.get('ignore', False)) rects.append(m) return rects
The ignore flag is critical for hard examples:
In the context of the Schrödinger suite, was the primary resource for peer-to-peer technical support. Because Schrödinger explicitly stated they would not provide benchmark results directly, users were encouraged to post requests to this independently administered list to get help from other experienced customers. Key Uses of the Mailing List Without mmodlist , you cannot train a multi-class
: Efficiently samples the potential energy surface for low-energy structures in systems ranging from small molecules to entire proteins.
Given the ambiguity, let's create a hypothetical use case for "mmodlist" in a Minecraft context, as this seems a plausible and interesting scenario:
Traditional object detectors (HOG + Linear SVM) use std::vector<rectangle> as truth. MMOD uses std::vector<mmod_rect> because: MMOD uses std::vector<
using namespace dlib;
# yolo line: class_id x_center y_center width height (normalized) def yolo_to_mmodlist(line, img_w, img_h, class_label): c, xc, yc, w, h = map(float, line.split()) l = int((xc - w/2) * img_w) t = int((yc - h/2) * img_h) r = int((xc + w/2) * img_w) b = int((yc + h/2) * img_h) rect = dlib.rectangle(l, t, r, b) return dlib.mmod_rect(rect, label=class_label, ignore=False)