原文我就不貼了，說一下感受吧！
從檢測方法出來，自我感覺一直不協調，現有的方法如fast系列一直比較複雜，強大的神經網絡應該是簡單高效的，one-stage從yolo出來後感覺好了很多，但是在最後的map上做roi anchor一直感覺特別冗餘，還好corner出來了，但其也存在的問題，不同的pool結構，還有總感覺這種方式怪怪的。然後FCOS出來了，完全感受到了高效和簡單，在此膜拜一下大神，感覺神經網絡就應該這樣，以最簡單的方式，取得很好的效果。
FCOS: Fully Convolutional One-Stage Object Detection
簡單介紹一下，方法很簡單，好文。
看到上圖了嗎，沒錯，就是這麼粗暴，像素級的預測t,l,r,b,當然，上面存在着一個問題，就是一個點包含兩個框，怎麼搞，當然選小的了，不不不，FPN啊：
稍微解釋一下最後的框框，x4是4層卷積，class分類，距離中心的loss，迴歸，
然後是中心度的定義，二進制交叉熵做loss。
ok，講完了， 簡單吧，粗暴吧，開始實現吧！
更新：
非常好用個點FCOS得配點代碼不是，近期研究mmdetection,感覺很好用，特來吧源碼解釋一下
def loss(self,
cls_scores,
bbox_preds,
centernesses,
gt_bboxes,
gt_labels,
img_metas,
cfg,
gt_bboxes_ignore=None):
labels, bbox_targets = self.fcos_target(all_level_points, gt_bboxes,
gt_labels) #生成label
flatten_cls_scores = [
cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels)
for cls_score in cls_scores
]
flatten_bbox_preds = [
bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
for bbox_pred in bbox_preds
]
flatten_centerness = [
centerness.permute(0, 2, 3, 1).reshape(-1)
for centerness in centernesses
]
flatten_cls_scores = torch.cat(flatten_cls_scores)
flatten_bbox_preds = torch.cat(flatten_bbox_preds)
flatten_centerness = torch.cat(flatten_centerness)
flatten_labels = torch.cat(labels)
flatten_bbox_targets = torch.cat(bbox_targets)
loss_cls = self.loss_cls(
flatten_cls_scores, flatten_labels,
avg_factor=num_pos + num_imgs) # cls loss
pos_bbox_preds = flatten_bbox_preds[pos_inds]
pos_bbox_targets = flatten_bbox_targets[pos_inds]
pos_centerness = flatten_centerness[pos_inds]
pos_centerness_targets = self.centerness_target(pos_bbox_targets)
pos_points = flatten_points[pos_inds]
pos_decoded_bbox_preds = distance2bbox(pos_points, pos_bbox_preds)
pos_decoded_target_preds = distance2bbox(pos_points,
pos_bbox_targets)
# centerness weighted iou loss
loss_bbox = self.loss_bbox(
pos_decoded_bbox_preds,
pos_decoded_target_preds,
weight=pos_centerness_targets,
avg_factor=pos_centerness_targets.sum()) #llox loss
loss_centerness = self.loss_centerness(pos_centerness,
pos_centerness_targets) #center loss 交叉熵loss
return dict(
loss_cls=loss_cls,
loss_bbox=loss_bbox,
loss_centerness=loss_centerness)
其中生成中心點啊方法，和原文一樣：
def centerness_target(self, pos_bbox_targets):
# only calculate pos centerness targets, otherwise there may be nan
left_right = pos_bbox_targets[:, [0, 2]]
top_bottom = pos_bbox_targets[:, [1, 3]]
centerness_targets = (
left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) * (
top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0])
return torch.sqrt(centerness_targets)
具體loss參考mmdet
focalloss+IOUloss+ CEloss
bbox_head=dict(
type='FCOSHead',
num_classes=81,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 16, 32, 64, 128],
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='IoULoss', loss_weight=1.0),
loss_centerness=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)))