github 地址:
https://github.com/xiaoxu1025/yolo
此代码这是一个学习记录，分享出来给新手参考更加理解其原理
yolo是把分类检测当成回归问题来设计的
其中yolov1 有几个缺点
靠近的物体会出现竞争，无法全部检测，因为每个网格值检测2个边框 处理的是同一个物体 并让与gt_box的iou较大的负责
就如论文中所说
YOLO predicts multiple bounding boxes per grid cell. At training time we only want one bounding box predictor to be responsible for each object. We assign one predictor to be “responsible” for predicting an object based on which prediction has the highest current IOU with the ground truth.
由于损失函数的设计，定位误差给大boxes和小boxes的权重值是一样的，这样明显不对，因为大的boxes对小的损失不敏感，小的boxes对一点损失就很敏感。论文也仅仅是让其取平方根进行简单处理，并没有实际解决这个问题
如论文中所属
Sum-squared error also equally weights errors in large boxes and small boxes. Our error metric should reflect that small deviations in large boxes matter less than in small boxes. To partially address this we predict the square root of the bounding box width and height instead of the width and height directly
下面简单分析损失函数的实现：
def yolo_loss(y_true, y_pred):
"""
:param y_true: [batch_size, 7, 7, 25]
:param y_pred: [batch_size, 7, 7, 30]
:return:
"""
# 类别标签
_classes = y_pred[..., 10:]
classes = y_true[..., 5:]
# (batch_size, 7, 7, 2)
_confidences = y_pred[..., 8:10]
# (batch_size, 7, 7, 1)
confidences = y_true[..., 4:5]
# (batch_size, 7, 7, 4)
bboxes = y_true[..., 0:4]
# (batch_size, 7, 7, 1, 4)
bboxes = tf.reshape(bboxes, (-1, cfg.CELL_SIZE, cfg.CELL_SIZE, 1, 4))
_bboxes = y_pred[..., 0:8]
# (batch_size, 7, 7, 2, 4)
_bboxes = tf.reshape(_bboxes, (-1, cfg.CELL_SIZE, cfg.CELL_SIZE, cfg.B, 4))
grid_x = tf.range(cfg.CELL_SIZE, dtype=tf.float32)
grid_y = tf.range(cfg.CELL_SIZE, dtype=tf.float32)
grid_x, grid_y = tf.meshgrid(grid_x, grid_y)
x_offset = tf.reshape(grid_x, (-1, 1))
y_offset = tf.reshape(grid_y, (-1, 1))
x_y_offset = tf.concat([x_offset, y_offset], axis=-1)
x_y_offset = tf.cast(tf.reshape(x_y_offset, [cfg.CELL_SIZE, cfg.CELL_SIZE, 1, 2]), tf.float32)
# 将_bboxes转到原图上
_bboxes_normal = tf.stack([
(_bboxes[..., 0] + x_y_offset[..., 0]) / cfg.CELL_SIZE,
(_bboxes[..., 1] + x_y_offset[..., 1]) / cfg.CELL_SIZE,
tf.square(_bboxes[..., 2]),
tf.square(_bboxes[..., 3]),
], axis=-1)
# bboxes_ious: (n, 7, 7, 2)
bboxes_ious = cal_iou(_bboxes_normal, bboxes)
object_mask = tf.reduce_max(bboxes_ious, axis=-1, keep_dims=True)
# 第i个cell第j个bbox负责产生损失
object_mask = tf.cast(bboxes_ious >= object_mask, dtype=tf.float32) * confidences
noobject_mask = tf.ones_like(object_mask, dtype=tf.float32) - object_mask
# _bboxes[..., 0:2] = (_bboxes[..., 0:2] + x_y_offset) / cfg.CELL_SIZE
# bboxes = bboxes[..., 0:2] * cfg.CELL_SIZE - x_y_offset
# bboxes = tf.sqrt(bboxes[..., 2:4])
bboxes_normal = tf.stack([
bboxes[..., 0] * cfg.CELL_SIZE - x_y_offset[..., 0],
bboxes[..., 1] * cfg.CELL_SIZE - x_y_offset[..., 1],
tf.sqrt(bboxes[..., 2]),
tf.sqrt(bboxes[..., 3]),
], axis=-1)
object_delta = object_mask * (_confidences - bboxes_ious)
object_loss = tf.reduce_mean(tf.reduce_sum(tf.square(object_delta), axis=[1, 2, 3])) * cfg.OBJECT_SCALE
onobject_delta = noobject_mask * _confidences
nobject_loss = tf.reduce_mean(tf.reduce_sum(tf.square(onobject_delta), axis=[1, 2, 3])) * cfg.NOOBJECT_SCALE
# 类别损失
cls_delta = confidences * (classes - _classes)
cls_loss = tf.reduce_mean(tf.reduce_sum(tf.square(cls_delta), axis=[1, 2, 3])) * cfg.CLASS_SCALE
# 边框损失
bbox_mask = tf.expand_dims(object_mask, axis=-1)
bboxes_xy_delta = bbox_mask * (_bboxes[..., 0:2] - bboxes_normal[..., 0:2])
bboxes_wh_delta = bbox_mask * (_bboxes[..., 2:4] - bboxes_normal[..., 2:4])
bboxes_loss = tf.reduce_mean(tf.reduce_sum(tf.square(bboxes_xy_delta), axis=[1, 2, 3, 4])) * cfg.BBOX_SCALE + \
tf.reduce_mean(tf.reduce_sum(tf.square(bboxes_wh_delta), axis=[1, 2, 3, 4])) * cfg.BBOX_SCALE
total_loss = cls_loss + object_loss + nobject_loss + bboxes_loss
return total_loss
其他的没有什么特别的，如果想了解更多请下载代码查看。
学无止境，与君共勉！！！