How to use the gluoncv.utils.metrics.voc_detection.VOC07MApMetric function in gluoncv

To help you get started, we’ve selected a few gluoncv examples, based on popular ways it is used in public projects.

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zzdang / cascade_rcnn_gluon / gluoncv / utils / metrics / voc_detection.py View on Github

def __init__(self, *args, **kwargs):
        super(VOC07MApMetric, self).__init__(*args, **kwargs)

dmlc / gluon-cv / gluoncv / utils / metrics / voc_detection.py View on Github

def __init__(self, *args, **kwargs):
        super(VOC07MApMetric, self).__init__(*args, **kwargs)

dmlc / gluon-cv / scripts / detection / faster_rcnn / eval_faster_rcnn.py View on Github

def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval',
                                         cleanup=not args.save_json)
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    return val_dataset, val_metric

sufeidechabei / gluon-mobilenet-yolov3 / train_yolo3.py View on Github

def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        train_dataset = gdata.VOCDetection(
            splits=[(2007, 'trainval'), (2012, 'trainval')])
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(
            iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        train_dataset = gdata.COCODetection(
            splits='instances_train2017', use_crowd=False)
        val_dataset = gdata.COCODetection(
            splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(
            val_dataset, args.save_prefix + '_eval', cleanup=True,
            data_shape=(args.data_shape, args.data_shape))
    else:
        raise NotImplementedError(
            'Dataset: {} not implemented.'.format(dataset))
    if args.num_samples &lt; 0:
        args.num_samples = len(train_dataset)
    if args.mixup:
        from gluoncv.data import MixupDetection

zzdang / cascade_rcnn_gluon / scripts / detection / faster_rcnn / train_faster_rcnn_2.py View on Github

def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        train_dataset = gdata.VOCDetection(
            splits=[(2007, 'trainval'), (2012, 'trainval')])
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        train_dataset = gdata.COCODetection(splits='instances_train2017')
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval', cleanup=True)
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    return train_dataset, val_dataset, val_metric

zzdang / cascade_rcnn_gluon / scripts / detection / cascade_rcnn / eval_cascade_rfcn_mAP.py View on Github

def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.75, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval',
                                         cleanup=not args.save_json)
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    return val_dataset, val_metric

dmlc / gluon-cv / scripts / detection / center_net / eval_center_net.py View on Github

def get_dataset(dataset, data_shape):
    if dataset.lower() == 'voc':
        val_dataset = gdata.VOCDetection(splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(
            val_dataset, args.save_prefix + '_eval', cleanup=True,
            data_shape=(data_shape, data_shape), post_affine=get_post_transform)
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    return val_dataset, val_metric

dmlc / gluon-cv / scripts / detection / faster_rcnn / train_faster_rcnn.py View on Github

def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        train_dataset = gdata.VOCDetection(
            splits=[(2007, 'trainval'), (2012, 'trainval')])
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        train_dataset = gdata.COCODetection(splits='instances_train2017', use_crowd=False)
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval', cleanup=True)
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    if args.mixup:
        from gluoncv.data.mixup import detection
        train_dataset = detection.MixupDetection(train_dataset)
    return train_dataset, val_dataset, val_metric

zzdang / cascade_rcnn_gluon / scripts / detection / cascade_rcnn / train_cascade_rcnn.py View on Github

def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        train_dataset = gdata.VOCDetection(
            splits=[(2007, 'trainval'), (2012, 'trainval')])
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        train_dataset = gdata.COCODetection(splits='instances_train2017')
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval', cleanup=True)
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    return train_dataset, val_dataset, val_metric

dmlc / gluon-cv / scripts / detection / ssd / train_ssd.py View on Github

def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        train_dataset = gdata.VOCDetection(
            splits=[(2007, 'trainval'), (2012, 'trainval')])
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        train_dataset = gdata.COCODetection(root=args.dataset_root + "/coco", splits='instances_train2017')
        val_dataset = gdata.COCODetection(root=args.dataset_root + "/coco", splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(
            val_dataset, args.save_prefix + '_eval', cleanup=True,
            data_shape=(args.data_shape, args.data_shape))
        # coco validation is slow, consider increase the validation interval
        if args.val_interval == 1:
            args.val_interval = 10
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    return train_dataset, val_dataset, val_metric

How to use the gluoncv.utils.metrics.voc_detection.VOC07MApMetric function in gluoncv

To help you get started, we’ve selected a few gluoncv examples, based on popular ways it is used in public projects.

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