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  • yolov3-tiny.conv.15

    2020-07-13 10:19:03
    pytorch yolov3 目标检测 yolov3-tiny.conv.15 yolov3 yolov3-tiny.conv.15 权重文件
  • yolov3-tiny.conv.15.rar

    2020-04-26 09:30:32
    yolov3-tiny.conv.15.rar yolov3-tiny.conv.15 for yolov3-tiny pretrain model
  • yolov3-tiny.conv.15.zip

    2020-06-10 09:46:51
    yolov3-tiny.conv.15预训练模型下载,/darknet partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15
  • yolov3-tiny.conv.15.tar.gz

    2020-05-09 17:07:27
    yolov3(pytorch)训练自己的数据集可参看本人blog。要使用的预训练权重:首先下载训练好的..../darknet partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15 即可得到该文件yolov3-tiny.conv.15
  • 利用darknet编译后生成,供yolov3自定义数据集学习训练使用
  • yolov3-tiny.conv.rar

    2019-10-23 17:05:51
    ./darknet partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15 结果 yolo3-tiny预训练模型
  • yolov3-tiny15.rar

    2019-07-30 16:14:06
    目标检测算法YOLOv3的预训练模型:较小版本的yolov3-tiny.conv.15
  • yolov3-tiny 训练。以及yolov3 画图。

    千次阅读 2019-10-12 10:30:09
    ./darknet partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15 先是获得训练好的yolov3-tiny的权重用来test: yolov3-tiny.weights这个文件需要自己下,下载地址如下。...

    训练tiny-yolov3和yolov3一样。只不过需要重新写一个权重文件。

    1.准备权重文件

    ./darknet partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15

    先是获得训练好的yolov3-tiny的权重用来test:

    yolov3-tiny.weights这个文件需要自己下,下载地址如下。

    wget https://pjreddie.com/media/files/yolov3-tiny.weights

    然后获得卷积层的权重用来训练自己的数据:这一步是配置权重文件,理论上并没有说提取多少层的特征合适,这里我们提取前15层当作与训练模型

    2.开始训练

    ./darknet detector train data/voc.data yolov3-tiny.cfg yolov3-tiny.conv.15 -gpu 0
    

    3.保存测试结果

    运行darknet官方代码中的detector valid指令,生成对测试集的检测结果。
    
     .\darknet detector valid <voc.data文件路径> <cfg文件路径> <weights文件路径> -out ""

    4.下载检测用脚本文件 reval_voc_py.py和voc_eval_py.py

    reval_voc_py3.py

    #!/usr/bin/env python
    
    # Adapt from ->
    # --------------------------------------------------------
    # Fast R-CNN
    # Copyright (c) 2015 Microsoft
    # Licensed under The MIT License [see LICENSE for details]
    # Written by Ross Girshick
    # --------------------------------------------------------
    # <- Written by Yaping Sun
    
    """Reval = re-eval. Re-evaluate saved detections."""
    
    import os, sys, argparse
    import numpy as np
    import _pickle as cPickle
    #import cPickle
    
    from voc_eval_py3 import voc_eval
    
    def parse_args():
        """
        Parse input arguments
        """
        parser = argparse.ArgumentParser(description='Re-evaluate results')
        parser.add_argument('output_dir', nargs=1, help='results directory',
                            type=str)
        parser.add_argument('--voc_dir', dest='voc_dir', default='data/VOCdevkit', type=str)
        parser.add_argument('--year', dest='year', default='2017', type=str)
        parser.add_argument('--image_set', dest='image_set', default='test', type=str)
        parser.add_argument('--classes', dest='class_file', default='data/voc.names', type=str)
    
        if len(sys.argv) == 1:
            parser.print_help()
            sys.exit(1)
    
        args = parser.parse_args()
        return args
    
    def get_voc_results_file_template(image_set, out_dir = 'results'):
        filename = 'comp4_det_' + image_set + '_{:s}.txt'
        path = os.path.join(out_dir, filename)
        return path
    
    def do_python_eval(devkit_path, year, image_set, classes, output_dir = 'results'):
        annopath = os.path.join(
            devkit_path,
            'VOC' + year,
            'Annotations',
            '{}.xml')
        imagesetfile = os.path.join(
            devkit_path,
            'VOC' + year,
            'ImageSets',
            'Main',
            image_set + '.txt')
        cachedir = os.path.join(devkit_path, 'annotations_cache')
        aps = []
        # The PASCAL VOC metric changed in 2010
        use_07_metric = True if int(year) < 2010 else False
        print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
        print('devkit_path=',devkit_path,', year = ',year)
    
        if not os.path.isdir(output_dir):
            os.mkdir(output_dir)
        for i, cls in enumerate(classes):
            if cls == '__background__':
                continue
            filename = get_voc_results_file_template(image_set).format(cls)
            rec, prec, ap = voc_eval(
                filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
                use_07_metric=use_07_metric)
            aps += [ap]
            print('AP for {} = {:.4f}'.format(cls, ap))
            with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
                cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
        print('Mean AP = {:.4f}'.format(np.mean(aps)))
        print('~~~~~~~~')
        print('Results:')
        for ap in aps:
            print('{:.3f}'.format(ap))
        print('{:.3f}'.format(np.mean(aps)))
        print('~~~~~~~~')
        print('')
        print('--------------------------------------------------------------')
        print('Results computed with the **unofficial** Python eval code.')
        print('Results should be very close to the official MATLAB eval code.')
        print('-- Thanks, The Management')
        print('--------------------------------------------------------------')
    
    if __name__ == '__main__':
        args = parse_args()
    
        output_dir = os.path.abspath(args.output_dir[0])
        with open(args.class_file, 'r') as f:
            lines = f.readlines()
    
        classes = [t.strip('\n') for t in lines]
    
        print('Evaluating detections')
        do_python_eval(args.voc_dir, args.year, args.image_set, classes, output_dir)
    
    

    reval_voc_py.py

    #!/usr/bin/env python
    
    # Adapt from ->
    # --------------------------------------------------------
    # Fast R-CNN
    # Copyright (c) 2015 Microsoft
    # Licensed under The MIT License [see LICENSE for details]
    # Written by Ross Girshick
    # --------------------------------------------------------
    # <- Written by Yaping Sun
    
    """Reval = re-eval. Re-evaluate saved detections."""
    
    import os, sys, argparse
    import numpy as np
    import cPickle
    
    from voc_eval import voc_eval
    
    def parse_args():
        """
        Parse input arguments
        """
        parser = argparse.ArgumentParser(description='Re-evaluate results')
        parser.add_argument('output_dir', nargs=1, help='results directory',
                            type=str)
        parser.add_argument('--voc_dir', dest='voc_dir', default='data/VOCdevkit', type=str)
        parser.add_argument('--year', dest='year', default='2017', type=str)
        parser.add_argument('--image_set', dest='image_set', default='test', type=str)
        parser.add_argument('--classes', dest='class_file', default='data/voc.names', type=str)
    
        if len(sys.argv) == 1:
            parser.print_help()
            sys.exit(1)
    
        args = parser.parse_args()
        return args
    
    def get_voc_results_file_template(image_set, out_dir = 'results'):
        filename = 'comp4_det_' + image_set + '_{:s}.txt'
        path = os.path.join(out_dir, filename)
        return path
    
    def do_python_eval(devkit_path, year, image_set, classes, output_dir = 'results'):
        annopath = os.path.join(
            devkit_path,
            'VOC' + year,
            'Annotations',
            '{:s}.xml')
        imagesetfile = os.path.join(
            devkit_path,
            'VOC' + year,
            'ImageSets',
            'Main',
            image_set + '.txt')
        cachedir = os.path.join(devkit_path, 'annotations_cache')
        aps = []
        # The PASCAL VOC metric changed in 2010
        use_07_metric = True if int(year) < 2010 else False
        print 'VOC07 metric? ' + ('Yes' if use_07_metric else 'No')
        if not os.path.isdir(output_dir):
            os.mkdir(output_dir)
        for i, cls in enumerate(classes):
            if cls == '__background__':
                continue
            filename = get_voc_results_file_template(image_set).format(cls)
            rec, prec, ap = voc_eval(
                filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
                use_07_metric=use_07_metric)
            aps += [ap]
            print('AP for {} = {:.4f}'.format(cls, ap))
            with open(os.path.join(output_dir, cls + '_pr.pkl'), 'w') as f:
                cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
        print('Mean AP = {:.4f}'.format(np.mean(aps)))
        print('~~~~~~~~')
        print('Results:')
        for ap in aps:
            print('{:.3f}'.format(ap))
        print('{:.3f}'.format(np.mean(aps)))
        print('~~~~~~~~')
        print('')
        print('--------------------------------------------------------------')
        print('Results computed with the **unofficial** Python eval code.')
        print('Results should be very close to the official MATLAB eval code.')
        print('-- Thanks, The Management')
        print('--------------------------------------------------------------')
    
    if __name__ == '__main__':
        args = parse_args()
    
        output_dir = os.path.abspath(args.output_dir[0])
        with open(args.class_file, 'r') as f:
            lines = f.readlines()
    
        classes = [t.strip('\n') for t in lines]
    
        print 'Evaluating detections'
        do_python_eval(args.voc_dir, args.year, args.image_set, classes, output_dir)
    

    voc_eval_py.py

    voc_eval_py.py
    # --------------------------------------------------------
    # Fast/er R-CNN
    # Licensed under The MIT License [see LICENSE for details]
    # Written by Bharath Hariharan
    # --------------------------------------------------------
    
    import xml.etree.ElementTree as ET #读取xml。
    import os
    import cPickle #序列化存储模块。
    import numpy as np
    
    def parse_rec(filename):#解析读取xml函数。
        """ Parse a PASCAL VOC xml file """
        tree = ET.parse(filename)
        objects = []
        for obj in tree.findall('object'):
            obj_struct = {}
            obj_struct['name'] = obj.find('name').text
            obj_struct['pose'] = obj.find('pose').text
            obj_struct['truncated'] = int(obj.find('truncated').text)
            obj_struct['difficult'] = int(obj.find('difficult').text)
            bbox = obj.find('bndbox')
            obj_struct['bbox'] = [int(bbox.find('xmin').text),
                                  int(bbox.find('ymin').text),
                                  int(bbox.find('xmax').text),
                                  int(bbox.find('ymax').text)]
            objects.append(obj_struct)
    
        return objects
    
    def voc_ap(rec, prec, use_07_metric=False): #单个测量AP的函数。
        """ ap = voc_ap(rec, prec, [use_07_metric])
        Compute VOC AP given precision and recall.
        If use_07_metric is true, uses the
        VOC 07 11 point method (default:False).
        """
        if use_07_metric:
            # 11 point metric
            ap = 0.
            for t in np.arange(0., 1.1, 0.1):
                if np.sum(rec >= t) == 0:
                    p = 0
                else:
                    p = np.max(prec[rec >= t])
                ap = ap + p / 11.
        else:
            # correct AP calculation
            # first append sentinel values at the end
            mrec = np.concatenate(([0.], rec, [1.]))
            mpre = np.concatenate(([0.], prec, [0.]))
    
            # compute the precision envelope
            for i in range(mpre.size - 1, 0, -1):
                mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
    
            # to calculate area under PR curve, look for points
            # where X axis (recall) changes value
            i = np.where(mrec[1:] != mrec[:-1])[0]
    
            # and sum (\Delta recall) * prec
            ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
        return ap
    
    def voc_eval(detpath,  ######主函数
                 annopath,
                 imagesetfile,
                 classname,
                 cachedir,
                 ovthresh=0.5,
                 use_07_metric=False):
        """rec, prec, ap = voc_eval(detpath,
                                    annopath,
                                    imagesetfile,
                                    classname,
                                    [ovthresh],
                                    [use_07_metric])
        Top level function that does the PASCAL VOC evaluation.
        detpath: Path to detections
            detpath.format(classname) should produce the detection results file. #产生的txt文件,里面是一张图片的各个detection。
        annopath: Path to annotations
            annopath.format(imagename) should be the xml annotations file. #xml 文件与对应的图像相呼应。
        imagesetfile: Text file containing the list of images, one image per line. #一个txt文件,里面是每个图片的地址,每行一个地址。
        classname: Category name (duh) #种类的名字,即类别。
        cachedir: Directory for caching the annotations #缓存标注的目录。
        [ovthresh]: Overlap threshold (default = 0.5) #重叠的多少大小。
        [use_07_metric]: Whether to use VOC07's 11 point AP computation 
            (default False) #是否使用VOC07的11点AP计算。
        """
        # assumes detections are in detpath.format(classname)
        # assumes annotations are in annopath.format(imagename)
        # assumes imagesetfile is a text file with each line an image name
        # cachedir caches the annotations in a pickle file
    
        # first load gt 加载ground truth。
        if not os.path.isdir(cachedir):
            os.mkdir(cachedir)
        cachefile = os.path.join(cachedir, 'annots.pkl') #即将新建文件的路径。
        # read list of images
        with open(imagesetfile, 'r') as f:
            lines = f.readlines() #读取文本里的所以文本行,作为众多文图片的路径。
        imagenames = [x.strip() for x in lines] #所有文件名字。
    
        if not os.path.isfile(cachefile): #如果cachefile文件不存在,则
            # load annots
            recs = {}
            for i, imagename in enumerate(imagenames):
                recs[imagename] = parse_rec(annopath.format(imagename)) #这里的format不知道啥意思
                if i % 100 == 0:
                    print 'Reading annotation for {:d}/{:d}'.format(
                        i + 1, len(imagenames)) #进度条。
            # save
            print 'Saving cached annotations to {:s}'.format(cachefile)
            with open(cachefile, 'w') as f:
                cPickle.dump(recs, f) #写入cPickle文件里面。写入的是一个字典,左侧为xml文件名,右侧为文件里面个各个参数。
        else:
            # load
            with open(cachefile, 'r') as f:
                recs = cPickle.load(f) #如果已经有了这个cPickle文件,则加载一下。
    
        # extract gt objects for this class #对每张图片的xml获取函数指定类的bbox等。
        class_recs = {}
        npos = 0
        for imagename in imagenames:
            R = [obj for obj in recs[imagename] if obj['name'] == classname] #获取每个文件中某种类别的物体。
            bbox = np.array([x['bbox'] for x in R]) #抽取bbox
            difficult = np.array([x['difficult'] for x in R]).astype(np.bool) #different基本都为0.
    
            det = [False] * len(R) #list中形参len(R)个False。
            npos = npos + sum(~difficult) #自增,sum求得的值基本都为0。
            class_recs[imagename] = {'bbox': bbox,
                                     'difficult': difficult,
                                     'det': det}
    
        # read dets 
        detfile = detpath.format(classname)
        with open(detfile, 'r') as f:
            lines = f.readlines()
    
        splitlines = [x.strip().split(' ') for x in lines]
        image_ids = [x[0] for x in splitlines] #图片index。
        confidence = np.array([float(x[1]) for x in splitlines]) #类别置信度
        BB = np.array([[float(z) for z in x[2:]] for x in splitlines]) #变为浮点型的bbox。
    
        # sort by confidence
        sorted_ind = np.argsort(-confidence) #对confidence的index根据值大小进行降序排列。
        sorted_scores = np.sort(-confidence) #降序排列。
        BB = BB[sorted_ind, :] #重排bbox,由大概率到小概率。
        image_ids = [image_ids[x] for x in sorted_ind] 对图片进行重排。
    
        # go down dets and mark TPs and FPs 
        nd = len(image_ids)
        tp = np.zeros(nd) 
        fp = np.zeros(nd) #归零。
        for d in range(nd):
            R = class_recs[image_ids[d]]
            bb = BB[d, :].astype(float)
            ovmax = -np.inf
            BBGT = R['bbox'].astype(float)
    
            if BBGT.size > 0:
                # compute overlaps
                # intersection
                ixmin = np.maximum(BBGT[:, 0], bb[0])
                iymin = np.maximum(BBGT[:, 1], bb[1])
                ixmax = np.minimum(BBGT[:, 2], bb[2])
                iymax = np.minimum(BBGT[:, 3], bb[3])
                iw = np.maximum(ixmax - ixmin + 1., 0.)
                ih = np.maximum(iymax - iymin + 1., 0.)
                inters = iw * ih
    
                # union
                uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
                       (BBGT[:, 2] - BBGT[:, 0] + 1.) *
                       (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)
    
                overlaps = inters / uni
                ovmax = np.max(overlaps)
                jmax = np.argmax(overlaps)
    
            if ovmax > ovthresh:
                if not R['difficult'][jmax]:
                    if not R['det'][jmax]:
                        tp[d] = 1.
                        R['det'][jmax] = 1
                    else:
                        fp[d] = 1.
            else:
                fp[d] = 1.
    
        # compute precision recall
        fp = np.cumsum(fp)
        tp = np.cumsum(tp)
        rec = tp / float(npos)
        # avoid divide by zero in case the first detection matches a difficult
        # ground truth
        prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
        ap = voc_ap(rec, prec, use_07_metric)
    
        return rec, prec, ap

    5.使用reval_voc_py.py计算出mAP值并且生成pkl文件

    python reval_voc_py3.py --voc_dir <voc文件路径> --year <年份> --image_set <验证集文件名> --classes <类名文件路径> <输出文件夹名

     先将第三部生成的results文件夹移动到当前脚本文件所在的位置,然后执行上述指令。

    首先python表示运行python代码

    reval_voc_py3.py表示当前运行的脚本文件名,python3的话就用这个,python2的话用reval_voc.py。

    voc文件路径就是当时训练用的VOC数据集的路径,比如windows下 d:\darknet\scripts\VOCdevkit,linux就是 \home\xxx\darknet\scripts\VOCdevkit,这里只是打个比方,读者请替换成自己需要的路径

    年份就是VOC数据集里VOC文件名里的时间,比如2007、2012这样的。

    验证集文件名一般是VOCdevkit\VOC2017\ImageSets\Main中的文件中txt文件名,比如train.txt,把需要测试的图片名全部塞进去就可以了,如果没有的话自行创建(不过没有的话怎么训练的呢)。注意:这里只需要填文件名,txt后缀都不需要的。

    类名文件路径就是voc.names文件的路径,在voc.data文件里面是有的,第4行names那里。

    输出文件夹名就自己随便写了,比如我这里写的testForCsdn。

    参数全部替换好就可以跑了,大概画风如下所示:

    这时会在脚本当前目录生成一个存放了pkl文件的文件夹,名字就是刚才输入的输出文件夹名。(这里的名字不需要和我的一样,如果你有多个类的话,就会生成多个文件,文件名就是你的类名)

    注意,这时已经能看到mAP值了。(我这里的验证集较小,目标较简单,所以mAP大了些,不用在意)
    6 用matplotlib绘制PR曲线
     

    展开全文
  • yolov3-tiny

    2020-12-29 09:11:10
    ../configs/yolov3-tiny.cfg"; config_v3.file_model_weights = "../configs/yolov3-tiny.weights"; config_v3.calibration_image_list_file_txt = "../configs/calibration_images.txt"...
  • 获得yolov3-tiny预训练模型

    千次阅读 2019-04-18 19:39:11
    首先下载yolov3_tiny.weights wget ... 然后在darknet中执行 ubuntu: ./darknet partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15 window...

    首先下载yolov3_tiny.weights

    wget https://pjreddie.com/media/files/yolov3_tiny.weights

    然后在darknet中执行

    ubuntu:   

     ./darknet partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15

    windows:

    darknet.exe partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15

    展开全文
  • user-pc:~/darknet$ ./darknet detector test cfg/coco.data cfg/yolov4-tiny.cfg weights/yolov4-tiny.weights data/dog.jpg Device IDs: 1 Device ID: 0 Device name: Ellesmere Device vendor: Advanced Micro ...
  • darknet-tiny 训练命令

    2019-10-29 15:23:46
    ./darknet partial AXI3/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15 ./darknet detector train AXI3/axi.data yolov3-tiny.cfg yolov3-tiny.conv.15 > tiny.txt

    ./darknet partial AXI3/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15
    ./darknet detector train AXI3/axi.data yolov3-tiny.cfg yolov3-tiny.conv.15 > tiny.txt

    展开全文
  • yolov3预训练权重模型darknet53.conv.74和yolov3-tiny.conv.15
  • 首先下载yolov3_tiny.weights ...然后在darknet中执行 ... ./darknet partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15 windows: darknet.exe partial cfg/yolov3-tiny.cfg yolov3-tiny.weights

    首先下载yolov3_tiny.weights

    wget https://pjreddie.com/media/files/yolov3_tiny.weights

    然后在darknet中执行

    ubuntu:

     ./darknet partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15
    

    windows:

    darknet.exe partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15
    

    生成map
    https://blog.csdn.net/zhou4411781/article/details/105112058?utm_medium=distribute.pc_relevant.none-task-blog-baidujs_title-0&spm=1001.2101.3001.4242

    展开全文
  • 参考这篇文章...前面部分都还算比较好理解 但是大家都遇到了一个问题 ./darknet partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15 一运行就报darknet是一...
  • YOLOv3编译错误(2):No such file or ...执行命令:python train.py --data data/voc2007.data --weights weights/yolov3-tiny.conv.15 --cfg cfg/yolov3-tiny.cfg --epochs 10 --device 1 错误提示:FileNotFo.
  • YOLOv3 训练的各种config文件以及weights文件。

    万次阅读 多人点赞 2019-03-21 18:07:18
    YOLOv3训练过程中的各种文件。包括配置文件,权重文件。 yolov3.pt yolov3.weights ...yolov3-tiny.conv.15 yolov3-tiny.pt yolov3-tiny.weighs https://drive.google.com/open?id=1uxgUBemJVw9w...
  • pytorch YOLOv3 目标检测需要的文件 ...yolov3-tiny.conv.15:https://download.csdn.net/download/TangLingBo/12608770 yolov3.weights:https://download.csdn.net/download/TangLingBo/12608772 yolov3-ti
  • YOLOv3编译错误(3):AssertionError:No labels ...执行命令:python train.py --data data/voc2007.data --weights weights/yolov3-tiny.conv.15 --cfg cfg/yolov3-tiny.cfg --epochs 10 --device 1 错误提示:Asse.
  • 网上有许多关于预训练权重的分享,但大部分都要收费。官网下载又太慢了,自己就破费一把。 本着一人收费,大家共享的原则,将自己花钱下载的...yolov3-tiny.conv.15 darknet53.conv.74 希望各位看官大佬能 ...
  • YOLOv3编译错误(1):ValueError: not enough ...执行命令:python train.py --data data/voc2007.data --weights weights/yolov3-tiny.conv.15 --cfg cfg/yolov3-tiny.cfg --epochs 10 --device 1 错误提示:Valu.
  • YOLOv3编译错误(4):RuntimeError:shape '[16, 3, 14, 16, 16]' is ...执行命令:python train.py --data data/voc2007.data --weights weights/yolov3-tiny.conv.15 --cfg cfg/yolov3-tiny.cfg --epochs 10 --d.
  • 1.绘制loss、IOU、avg Recall等的曲线图 ..../darknet detector train cfg/voc.data cfg/yolov3-tiny.cfg yolov3-tiny.conv.15 -gpus 0,1 2>1 | tee visualization/tiny_yolov3.log 在使用脚本绘制变...
  • 该类型文件后缀有3类,一类是“.weight”,一类是“.backup”,还有一类是数字(文件名如“darknet53.conv.74”、“yolov3-tiny.conv.15”)。 第一类:后缀“.weight”和“.backup”文件 它们其实是一类文件,
  • Yolov3-Tiny-Prn 33.1% 416X416 %ms %ms 3.5BFlops 4.7M 18.8M Yolov4-Tiny 40.2% 416X416 23.67ms 40.14ms 6.9 BFlops 5.77M 23.1M Test platform Mi 11 Snapdragon 888 CPU,Based on NCNN COCO 2017 Val ...

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