Question

我想从Tensorflow Object Detection API训练一个ssd-inception-v2模型。我想要使用的训练数据集是一堆不同大小的裁剪图像，没有边界框，因为裁剪本身就是边界框。

我遵循create_pascal_tf_record.py示例，相应地替换了边界框和分类部分，以生成TFRecords，如下所示：

def dict_to_tf_example(imagepath, label):
    image = Image.open(imagepath)
    if image.format != 'JPEG':
         print("Skipping file: " + imagepath)
         return
    img = np.array(image)
    with tf.gfile.GFile(imagepath, 'rb') as fid:
        encoded_jpg = fid.read()
    # The reason to store image sizes was demonstrated
    # in the previous example -- we have to know sizes
    # of images to later read raw serialized string,
    # convert to 1d array and convert to respective
    # shape that image used to have.
    height = img.shape[0]
    width = img.shape[1]
    key = hashlib.sha256(encoded_jpg).hexdigest()
    # Put in the original images into array
    # Just for future check for correctness

    xmin = [5.0/100.0]
    ymin = [5.0/100.0]
    xmax = [95.0/100.0]
    ymax = [95.0/100.0]
    class_text = [label['name'].encode('utf8')]
    classes = [label['id']]
    example = tf.train.Example(features=tf.train.Features(feature={
        'image/height':dataset_util.int64_feature(height),
        'image/width': dataset_util.int64_feature(width),
        'image/filename': dataset_util.bytes_feature(imagepath.encode('utf8')),
        'image/source_id': dataset_util.bytes_feature(imagepath.encode('utf8')),
        'image/encoded': dataset_util.bytes_feature(encoded_jpg),
        'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),
        'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),        
        'image/object/class/text': dataset_util.bytes_list_feature(class_text),
        'image/object/class/label': dataset_util.int64_list_feature(classes),
        'image/object/bbox/xmin': dataset_util.float_list_feature(xmin),
        'image/object/bbox/xmax': dataset_util.float_list_feature(xmax),
        'image/object/bbox/ymin': dataset_util.float_list_feature(ymin),
        'image/object/bbox/ymax': dataset_util.float_list_feature(ymax)
    }))

    return example


def main(_):

  data_dir = FLAGS.data_dir
  output_path = os.path.join(data_dir,FLAGS.output_path + '.record')
  writer = tf.python_io.TFRecordWriter(output_path)
  label_map = label_map_util.load_labelmap(FLAGS.label_map_path)
  categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=80, use_display_name=True)
  category_index = label_map_util.create_category_index(categories)
  category_list = os.listdir(data_dir)
  gen = (category for category in categories if category['name'] in category_list)
  for category in gen:
    examples_path = os.path.join(data_dir,category['name'])
    examples_list = os.listdir(examples_path)
    for example in examples_list:
        imagepath = os.path.join(examples_path,example)

        tf_example = dict_to_tf_example(imagepath,category)
        writer.write(tf_example.SerializeToString())
 #       print(tf_example)

  writer.close()

边界框是硬编码的，包含整个图像。标签相应地给出其相应的目录。我使用mscoco_label_map.pbxt进行标记，使用ssd_inception_v2_pets.config作为管道的基础。

我训练并冻结模型以与jupyter笔记本示例一起使用。但是，最终结果是围绕整个图像的单个框。什么出错了？

Answer 1

对象检测算法/网络通常通过预测边界框和类的位置来工作。因此，训练数据通常需要包含边界框数据。通过使用一个始终与图像大小相同的边界框为训练数据提供模型，您可能会得到垃圾预测，包括一个总是勾勒出图像的框。

这听起来像您的训练数据有问题。你不应该给出裁剪的图像，而是用你的对象注释完整的图像/场景。此时你基本上都在训练分类器。

尝试使用未裁剪的正确图像样式进行训练，看看你是如何进行训练的。

TensorFlow对象检测API使用图像裁剪作为训练数据集

1 个答案: