test: ../smart_construction_site_dataset/images/test2017/ nc: 10 # number of classes names: ['Helmet', 'No Helmet', 'Precast Component', 'Transport Vehicle', 'Dump Truck', 'Concrete Mixer', 'Excavator', 'Roller', 'Bulldozer', 'Loader'] # 类别名称 # COCOannotationstrain_ann: ../sma...
3.3 ./yolox/data/datasets/coco_classes.py 修改成自己的类别 4 训练 python tools/train.py -f exps/example/custom/yolox_s.py -d 4 -b 64 --fp16 -o -c weights/yolox_s.pth --cache 变量解释: -d: number of gpu devices -b: total batch size, the recommended number for -b is num...
is_coco = isinstance(data.get('val'), str) and data['val'].endswith('coco/val2017.txt') # COCO dataset nc = 1 if single_cls else int(data['nc']) # number of classes 修改为: model.eval() is_coco = True nc = 1 if single_cls else int(data['nc']) # number of classes ...
int batch_size = detections.size(0); // number of classes, e.g. 80 for coco dataset auto num_classes = detections.size(2) - item_attr_size; // get candidates which object confidence > threshold auto conf_mask = detections.select(2, 4).ge(conf_thres).unsqueeze(2); std::vector<std...
IMAGES_PER_GPU = 1 # Uncomment to train on 8 GPUs (default is 1) GPU_COUNT = 1 # Number of classes (including background) NUM_CLASSES = 1 + 80 # COCO has 80 classes config = CocoConfig() config.display() dataset_path = '/home/tensorboy/AI/walmart/DATA' dataset_train = CocoDa...
dataset_dir: The root directory of the COCO dataset. subset: What to load (train, val, minival, valminusminival) year: What dataset year to load (2014, 2017) as a string, not an integer class_ids: If provided, only loads images that have the given classes. class_map: TODO: Not imp...
# number of classes nc: 80 # class names names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', ...
'''#show all classes in cococlasses = id2name(coco)print(classes)#[1, 2, 3, 4, 6, 8]classes_ids = coco.getCatIds(catNms=classes_names)print(classes_ids)forclsinclasses_names:#Get ID number of this classcls_id=coco.getCatIds(catNms=[cls]) ...
# number of classes nc: 80 # class names names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',...
First of all, it leads to an extremely large number of classes, many having only a handful of examples. This makes most classes unusable for recognition purposes, as observed in [38, 63]. Furthermore, different annotators typically use several synonyms to indicate the same class. These need...