diff --git a/models/tf.py b/models/tf.py
index 010fafdfa..1c6da43ad 100644
--- a/models/tf.py
+++ b/models/tf.py
@@ -28,7 +28,7 @@ import torch
 import torch.nn as nn
 from tensorflow import keras
 
-from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad, C3
+from models.common import Bottleneck, BottleneckCSP, Concat, Conv, C3, DWConv, Focus, SPP, SPPF, autopad
 from models.experimental import CrossConv, MixConv2d, attempt_load
 from models.yolo import Detect
 from utils.general import make_divisible, print_args, set_logging
@@ -183,6 +183,22 @@ class TFSPP(keras.layers.Layer):
         return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3))
 
 
+class TFSPPF(keras.layers.Layer):
+    # Spatial pyramid pooling-Fast layer
+    def __init__(self, c1, c2, k=5, w=None):
+        super(TFSPPF, self).__init__()
+        c_ = c1 // 2  # hidden channels
+        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
+        self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2)
+        self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding='SAME')
+
+    def call(self, inputs):
+        x = self.cv1(inputs)
+        y1 = self.m(x)
+        y2 = self.m(y1)
+        return self.cv2(tf.concat([x, y1, y2, self.m(y2)], 3))
+
+
 class TFDetect(keras.layers.Layer):
     def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None):  # detection layer
         super(TFDetect, self).__init__()
@@ -272,7 +288,7 @@ def parse_model(d, ch, model, imgsz):  # model_dict, input_channels(3)
                 pass
 
         n = max(round(n * gd), 1) if n > 1 else n  # depth gain
-        if m in [nn.Conv2d, Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]:
+        if m in [nn.Conv2d, Conv, Bottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]:
             c1, c2 = ch[f], args[0]
             c2 = make_divisible(c2 * gw, 8) if c2 != no else c2