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+# pytorch-detector
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+DATASET:
+  TRAIN: "/home/qiuqiu/Desktop/coco2017/train2017.txt"
+  VAL: "/home/qiuqiu/Desktop/coco2017/val2017.txt"
+  NAMES: "dataset/coco128/coco.names"
+MODEL:
+  NC: 80
+  INPUT_WIDTH: 512
+  INPUT_HEIGHT: 512
+TRAIN:
+  LR: 0.001
+  THRESH: 0.25
+  WARMUP: true
+  BATCH_SIZE: 64
+  END_EPOCH: 350
+  MILESTIONES:
+    - 150
+    - 250
+    - 300
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+# pytorch-detector
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+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
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+frisbee
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+snowboard
+sports ball
+kite
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+skateboard
+surfboard
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+cup
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+bed
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+toilet
+tvmonitor
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush
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+61 0.704239 0.520064 0.086783 0.117179
diff --git a/dataset/coco128/train/000000000562.jpg b/dataset/coco128/train/000000000562.jpg
new file mode 100644
index 0000000..6a04e0c
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diff --git a/dataset/coco128/train/000000000562.txt b/dataset/coco128/train/000000000562.txt
new file mode 100644
index 0000000..dff3118
--- /dev/null
+++ b/dataset/coco128/train/000000000562.txt
@@ -0,0 +1,4 @@
+79 0.423227 0.342477 0.185839 0.185453
+79 0.538463 0.263258 0.185343 0.346859
+79 0.248203 0.270102 0.19721 0.285391
+41 0.514492 0.532648 0.731111 0.823266
diff --git a/dataset/coco128/train/000000000564.jpg b/dataset/coco128/train/000000000564.jpg
new file mode 100644
index 0000000..b637c9b
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diff --git a/dataset/coco128/train/000000000564.txt b/dataset/coco128/train/000000000564.txt
new file mode 100644
index 0000000..db60667
--- /dev/null
+++ b/dataset/coco128/train/000000000564.txt
@@ -0,0 +1,16 @@
+0 0.585096 0.742031 0.283615 0.43625
+0 0.914212 0.391609 0.171577 0.146719
+38 0.672365 0.708156 0.202846 0.063781
+56 0.16275 0.383633 0.083577 0.034797
+56 0.252885 0.382375 0.087654 0.03975
+56 0.435712 0.380219 0.091346 0.040656
+56 0.525846 0.503047 0.108769 0.044188
+56 0.309221 0.427586 0.096404 0.047266
+56 0.219269 0.420742 0.097038 0.040328
+56 0.120962 0.425578 0.090231 0.037
+56 0.071904 0.385688 0.091808 0.035063
+56 0.851952 0.511687 0.118288 0.072719
+56 0.676125 0.453242 0.099096 0.033766
+56 0.632846 0.501867 0.098808 0.042609
+56 0.512096 0.417664 0.127808 0.120953
+56 0.340683 0.395742 0.093673 0.064891
diff --git a/dataset/coco128/train/000000000569.jpg b/dataset/coco128/train/000000000569.jpg
new file mode 100644
index 0000000..477b234
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diff --git a/dataset/coco128/train/000000000569.txt b/dataset/coco128/train/000000000569.txt
new file mode 100644
index 0000000..24e58cf
--- /dev/null
+++ b/dataset/coco128/train/000000000569.txt
@@ -0,0 +1,5 @@
+0 0.200297 0.712146 0.125281 0.302875
+33 0.318227 0.461302 0.046984 0.064896
+33 0.278828 0.485896 0.026031 0.067875
+33 0.245305 0.502354 0.035922 0.049375
+33 0.230844 0.523781 0.040094 0.048521
diff --git a/dataset/coco128/train/000000000572.jpg b/dataset/coco128/train/000000000572.jpg
new file mode 100644
index 0000000..499341c
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diff --git a/dataset/coco128/train/000000000572.txt b/dataset/coco128/train/000000000572.txt
new file mode 100644
index 0000000..ef2c6cd
--- /dev/null
+++ b/dataset/coco128/train/000000000572.txt
@@ -0,0 +1,3 @@
+0 0.497506 0.514227 0.340304 0.846516
+0 0.316557 0.668648 0.30178 0.541047
+28 0.838735 0.765313 0.322529 0.346531
diff --git a/dataset/coco128/train/000000000575.jpg b/dataset/coco128/train/000000000575.jpg
new file mode 100644
index 0000000..5af33ee
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diff --git a/dataset/coco128/train/000000000575.txt b/dataset/coco128/train/000000000575.txt
new file mode 100644
index 0000000..ccf6735
--- /dev/null
+++ b/dataset/coco128/train/000000000575.txt
@@ -0,0 +1 @@
+15 0.283203 0.423216 0.552687 0.691657
diff --git a/dataset/coco128/train/000000000581.jpg b/dataset/coco128/train/000000000581.jpg
new file mode 100644
index 0000000..4e6f31a
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diff --git a/dataset/coco128/train/000000000581.txt b/dataset/coco128/train/000000000581.txt
new file mode 100644
index 0000000..087be01
--- /dev/null
+++ b/dataset/coco128/train/000000000581.txt
@@ -0,0 +1 @@
+16 0.386237 0.56175 0.33167 0.51822
diff --git a/dataset/coco128/train/000000000584.jpg b/dataset/coco128/train/000000000584.jpg
new file mode 100644
index 0000000..807efe3
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diff --git a/dataset/coco128/train/000000000584.txt b/dataset/coco128/train/000000000584.txt
new file mode 100644
index 0000000..f639640
--- /dev/null
+++ b/dataset/coco128/train/000000000584.txt
@@ -0,0 +1,14 @@
+44 0.272852 0.08446 0.527703 0.16216
+50 0.536797 0.70669 0.098156 0.12108
+50 0.463555 0.580915 0.303047 0.253474
+51 0.625687 0.844707 0.142531 0.192934
+51 0.762328 0.686068 0.127344 0.181901
+51 0.784656 0.536467 0.115906 0.183545
+51 0.852633 0.665904 0.104766 0.179319
+51 0.392148 0.376291 0.058922 0.08446
+51 0.421758 0.199108 0.115141 0.154742
+45 0.5 0.507864 1 0.94831
+51 0.374008 0.492535 0.134422 0.182817
+51 0.263648 0.537782 0.104266 0.162746
+51 0.638195 0.15473 0.091734 0.104296
+51 0.602 0.503779 0.086625 0.128638
diff --git a/dataset/coco128/train/000000000589.jpg b/dataset/coco128/train/000000000589.jpg
new file mode 100644
index 0000000..f4ef425
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diff --git a/dataset/coco128/train/000000000589.txt b/dataset/coco128/train/000000000589.txt
new file mode 100644
index 0000000..ca4cb23
--- /dev/null
+++ b/dataset/coco128/train/000000000589.txt
@@ -0,0 +1,2 @@
+29 0.73943 0.4845 0.029297 0.013125
+0 0.657227 0.57649 0.227234 0.383937
diff --git a/dataset/coco128/train/000000000590.jpg b/dataset/coco128/train/000000000590.jpg
new file mode 100644
index 0000000..b060cc7
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diff --git a/dataset/coco128/train/000000000590.txt b/dataset/coco128/train/000000000590.txt
new file mode 100644
index 0000000..168b8f9
--- /dev/null
+++ b/dataset/coco128/train/000000000590.txt
@@ -0,0 +1,2 @@
+71 0.289658 0.60159 0.208313 0.02314
+45 0.292017 0.61358 0.227066 0.0552
diff --git a/dataset/coco128/train/000000000595.jpg b/dataset/coco128/train/000000000595.jpg
new file mode 100644
index 0000000..bf0d4a5
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diff --git a/dataset/coco128/train/000000000595.txt b/dataset/coco128/train/000000000595.txt
new file mode 100644
index 0000000..01819f2
--- /dev/null
+++ b/dataset/coco128/train/000000000595.txt
@@ -0,0 +1 @@
+62 0.685258 0.735312 0.220297 0.233167
diff --git a/dataset/coco128/train/000000000597.jpg b/dataset/coco128/train/000000000597.jpg
new file mode 100644
index 0000000..e69f193
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diff --git a/dataset/coco128/train/000000000597.txt b/dataset/coco128/train/000000000597.txt
new file mode 100644
index 0000000..32017e4
--- /dev/null
+++ b/dataset/coco128/train/000000000597.txt
@@ -0,0 +1,7 @@
+20 0.559867 0.724113 0.063703 0.094597
+20 0.653281 0.685323 0.108 0.173468
+20 0.788078 0.691815 0.113875 0.174758
+20 0.365687 0.671008 0.117656 0.191048
+20 0.2565 0.693044 0.093031 0.139476
+20 0.167117 0.706694 0.054453 0.105403
+20 0.045711 0.692137 0.086203 0.134839
diff --git a/dataset/coco128/train/000000000599.jpg b/dataset/coco128/train/000000000599.jpg
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index 0000000..296f080
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diff --git a/dataset/coco128/train/000000000599.txt b/dataset/coco128/train/000000000599.txt
new file mode 100644
index 0000000..ff2f3f6
--- /dev/null
+++ b/dataset/coco128/train/000000000599.txt
@@ -0,0 +1,5 @@
+58 0.912555 0.304939 0.173953 0.582948
+15 0.323703 0.540553 0.644531 0.898649
+57 0.484102 0.497002 0.968203 0.985012
+65 0.826 0.594816 0.280094 0.215725
+65 0.763406 0.485356 0.309375 0.148649
diff --git a/dataset/coco128/train/000000000605.jpg b/dataset/coco128/train/000000000605.jpg
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index 0000000..9475f78
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diff --git a/dataset/coco128/train/000000000605.txt b/dataset/coco128/train/000000000605.txt
new file mode 100644
index 0000000..2c1709b
--- /dev/null
+++ b/dataset/coco128/train/000000000605.txt
@@ -0,0 +1,4 @@
+60 0.5 0.502448 1 0.995104
+41 0.63007 0.440312 0.351359 0.364875
+44 0.453117 0.52624 0.092078 0.204229
+41 0.751687 0.211229 0.222469 0.296625
diff --git a/dataset/coco128/train/000000000612.jpg b/dataset/coco128/train/000000000612.jpg
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index 0000000..565a71e
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diff --git a/dataset/coco128/train/000000000612.txt b/dataset/coco128/train/000000000612.txt
new file mode 100644
index 0000000..b331994
--- /dev/null
+++ b/dataset/coco128/train/000000000612.txt
@@ -0,0 +1,11 @@
+59 0.516891 0.739875 0.665531 0.5
+77 0.556156 0.65676 0.111062 0.182729
+77 0.455187 0.671396 0.096875 0.168958
+77 0.516672 0.633417 0.193719 0.225917
+77 0.612797 0.539677 0.114813 0.166896
+77 0.688164 0.55575 0.053766 0.079375
+77 0.363398 0.496229 0.161516 0.20125
+77 0.546492 0.523333 0.067453 0.03675
+77 0.655359 0.598187 0.090625 0.138
+77 0.737375 0.703396 0.216438 0.216042
+77 0.566086 0.431719 0.077953 0.119979
diff --git a/dataset/coco128/train/000000000620.jpg b/dataset/coco128/train/000000000620.jpg
new file mode 100644
index 0000000..def70df
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diff --git a/dataset/coco128/train/000000000620.txt b/dataset/coco128/train/000000000620.txt
new file mode 100644
index 0000000..754e5b6
--- /dev/null
+++ b/dataset/coco128/train/000000000620.txt
@@ -0,0 +1,2 @@
+72 0.753187 0.459555 0.493625 0.910109
+53 0.432146 0.508687 0.461667 0.226938
diff --git a/dataset/coco128/train/000000000623.jpg b/dataset/coco128/train/000000000623.jpg
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index 0000000..55b19db
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diff --git a/dataset/coco128/train/000000000623.txt b/dataset/coco128/train/000000000623.txt
new file mode 100644
index 0000000..7edc8ee
--- /dev/null
+++ b/dataset/coco128/train/000000000623.txt
@@ -0,0 +1,4 @@
+13 0.86172 0.38362 0.27656 0.47676
+77 0.4764 0.52584 0.946827 0.92584
+56 0.86248 0.36568 0.265653 0.47968
+0 0.71516 0.49351 0.564507 0.98182
diff --git a/dataset/coco128/train/000000000625.jpg b/dataset/coco128/train/000000000625.jpg
new file mode 100644
index 0000000..2c9c351
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diff --git a/dataset/coco128/train/000000000625.txt b/dataset/coco128/train/000000000625.txt
new file mode 100644
index 0000000..b36bda6
--- /dev/null
+++ b/dataset/coco128/train/000000000625.txt
@@ -0,0 +1,4 @@
+0 0.725 0.697578 0.1875 0.479821
+0 0.503906 0.646749 0.157812 0.607623
+0 0.361859 0.732578 0.144281 0.417982
+29 0.452195 0.275191 0.052734 0.048498
diff --git a/dataset/coco128/train/000000000626.jpg b/dataset/coco128/train/000000000626.jpg
new file mode 100644
index 0000000..a132b78
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diff --git a/dataset/coco128/train/000000000626.txt b/dataset/coco128/train/000000000626.txt
new file mode 100644
index 0000000..a2a458d
--- /dev/null
+++ b/dataset/coco128/train/000000000626.txt
@@ -0,0 +1,2 @@
+74 0.520594 0.303323 0.064562 0.088479
+74 0.635297 0.319094 0.032625 0.097729
diff --git a/dataset/coco128/train/000000000629.jpg b/dataset/coco128/train/000000000629.jpg
new file mode 100644
index 0000000..e0f6ffb
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diff --git a/dataset/coco128/train/000000000629.txt b/dataset/coco128/train/000000000629.txt
new file mode 100644
index 0000000..6807f1f
--- /dev/null
+++ b/dataset/coco128/train/000000000629.txt
@@ -0,0 +1 @@
+3 0.694297 0.786124 0.385562 0.367939
diff --git a/dataset/coco128/train/000000000634.jpg b/dataset/coco128/train/000000000634.jpg
new file mode 100644
index 0000000..83ee21b
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diff --git a/dataset/coco128/train/000000000634.txt b/dataset/coco128/train/000000000634.txt
new file mode 100644
index 0000000..f774da3
--- /dev/null
+++ b/dataset/coco128/train/000000000634.txt
@@ -0,0 +1,6 @@
+0 0.474906 0.577531 0.680351 0.328094
+36 0.551557 0.739773 0.3774 0.084484
+0 0.37534 0.049539 0.050773 0.048078
+0 0.896546 0.089906 0.206909 0.179375
+0 0.438841 0.045594 0.049204 0.03775
+0 0.212927 0.082453 0.059859 0.044656
diff --git a/dataset/coco128/train/000000000636.jpg b/dataset/coco128/train/000000000636.jpg
new file mode 100644
index 0000000..85252cd
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diff --git a/dataset/coco128/train/000000000636.txt b/dataset/coco128/train/000000000636.txt
new file mode 100644
index 0000000..099101b
--- /dev/null
+++ b/dataset/coco128/train/000000000636.txt
@@ -0,0 +1 @@
+61 0.444448 0.558563 0.552563 0.837844
diff --git a/dataset/coco128/train/000000000641.jpg b/dataset/coco128/train/000000000641.jpg
new file mode 100644
index 0000000..3c842b8
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diff --git a/dataset/coco128/train/000000000641.txt b/dataset/coco128/train/000000000641.txt
new file mode 100644
index 0000000..a71b1b1
--- /dev/null
+++ b/dataset/coco128/train/000000000641.txt
@@ -0,0 +1,12 @@
+1 0.200758 0.625491 0.040734 0.140093
+1 0.896867 0.644696 0.027328 0.050981
+5 0.548531 0.501121 0.652219 0.534813
+0 0.91232 0.560888 0.017484 0.030561
+0 0.896828 0.603061 0.027281 0.080327
+0 0.940219 0.559556 0.015219 0.030888
+0 0.873758 0.567886 0.010984 0.053388
+0 0.728437 0.557897 0.007875 0.061495
+0 0.953398 0.55868 0.011141 0.033762
+0 0.065008 0.511799 0.045922 0.053738
+0 0.021906 0.517243 0.031875 0.053645
+0 0.004938 0.538411 0.009875 0.044252
diff --git a/dataset/coco128/train/000000000643.jpg b/dataset/coco128/train/000000000643.jpg
new file mode 100644
index 0000000..fa5c81f
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diff --git a/dataset/coco128/train/000000000643.txt b/dataset/coco128/train/000000000643.txt
new file mode 100644
index 0000000..7ed5931
--- /dev/null
+++ b/dataset/coco128/train/000000000643.txt
@@ -0,0 +1,20 @@
+62 0.42217 0.716493 0.42062 0.529893
+39 0.18036 0.82772 0.09312 0.308133
+73 0.72236 0.389267 0.0116 0.126267
+73 0.85443 0.394213 0.09602 0.168427
+73 0.67057 0.39276 0.01246 0.128667
+73 0.68045 0.39272 0.01022 0.130347
+73 0.69228 0.395107 0.01284 0.126
+73 0.69397 0.759547 0.0587 0.152
+73 0.70329 0.39012 0.01758 0.130427
+73 0.73517 0.39176 0.1441 0.13056
+73 0.70365 0.946067 0.24438 0.085413
+77 0.43418 0.07944 0.09648 0.141707
+45 0.3492 0.22412 0.08104 0.063547
+64 0.23286 0.97588 0.07216 0.04728
+73 0.22994 0.248627 0.10952 0.014053
+73 0.22808 0.260147 0.11112 0.014053
+73 0.23099 0.273973 0.10746 0.01264
+77 0.81076 0.16444 0.07972 0.14712
+73 0.23152 0.286933 0.10744 0.013867
+39 0.43892 0.215467 0.05436 0.176373
diff --git a/dataset/coco128/train/000000000650.jpg b/dataset/coco128/train/000000000650.jpg
new file mode 100644
index 0000000..8f56d29
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diff --git a/dataset/coco128/train/000000000650.txt b/dataset/coco128/train/000000000650.txt
new file mode 100644
index 0000000..ff36a9b
--- /dev/null
+++ b/dataset/coco128/train/000000000650.txt
@@ -0,0 +1,2 @@
+15 0.519398 0.544087 0.476359 0.572061
+2 0.501859 0.820726 0.996281 0.332178
diff --git a/eval.py b/eval.py
new file mode 100644
index 0000000..2fc5697
--- /dev/null
+++ b/eval.py
@@ -0,0 +1,56 @@
+import os
+import torch
+import argparse
+from torchsummary import summary
+
+from utils.tool import *
+from utils.datasets import *
+from utils.evaluation import CocoDetectionEvaluator
+
+from module.detector import Detector
+
+# 指定后端设备CUDA&CPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+if __name__ == '__main__':
+    # 指定训练配置文件
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--yaml', type=str, default="", help='.yaml config')
+    parser.add_argument('--weight', type=str, default=None, help='.weight config')
+
+    opt = parser.parse_args()
+    assert os.path.exists(opt.yaml), "请指定正确的配置文件路径"
+    assert os.path.exists(opt.weight), "请指定正确的权重文件路径"
+
+    # 解析yaml配置文件
+    cfg = LoadYaml(opt.yaml)    
+    print(cfg) 
+
+    # 加载模型权重
+    print("load weight from:%s"%opt.weight)
+    model = Detector(cfg.category_num, True).to(device)
+    model.load_state_dict(torch.load(opt.weight))
+    model.eval()
+
+    # # 打印网络各层的张量维度
+    summary(model, input_size=(3, cfg.input_height, cfg.input_width))
+
+    # 定义验证函数
+    evaluation = CocoDetectionEvaluator(cfg.names, device)
+
+    # 数据集加载
+    val_dataset = TensorDataset(cfg.val_txt, cfg.input_width, cfg.input_height, False)
+
+    #验证集
+    val_dataloader = torch.utils.data.DataLoader(val_dataset,
+                                                 batch_size=cfg.batch_size,
+                                                 shuffle=False,
+                                                 collate_fn=collate_fn,
+                                                 num_workers=4,
+                                                 drop_last=False,
+                                                 persistent_workers=True
+                                                 )
+
+    # 模型评估
+    print("computer mAP...")
+    evaluation.compute_map(val_dataloader, model)
\ No newline at end of file
diff --git a/module/custom_layers.py b/module/custom_layers.py
new file mode 100644
index 0000000..12fbd7d
--- /dev/null
+++ b/module/custom_layers.py
@@ -0,0 +1,98 @@
+import torch
+import torch.nn as nn
+
+class Conv1x1(nn.Module):
+    def __init__(self, input_channels, output_channels):
+        super(Conv1x1, self).__init__()
+        self.conv1x1 =  nn.Sequential(nn.Conv2d(input_channels, output_channels, 1, stride=1, padding=0, bias=False),
+                                      nn.BatchNorm2d(output_channels),
+                                      nn.ReLU(inplace=True)
+                                     )
+    
+    def forward(self, x):
+        return self.conv1x1(x)
+
+class Head(nn.Module):
+    def __init__(self, input_channels, output_channels):
+        super(Head, self).__init__()
+        self.conv5x5 = nn.Sequential(nn.Conv2d(input_channels, input_channels, 5, 1, 2, groups = input_channels, bias = False),
+                                     nn.BatchNorm2d(input_channels),
+                                     nn.ReLU(inplace=True),
+
+                                     nn.Conv2d(input_channels, output_channels, 1, stride=1, padding=0, bias=False),
+                                     nn.BatchNorm2d(output_channels)
+                                    ) 
+    
+    def forward(self, x):
+        return self.conv5x5(x)
+
+class SPP(nn.Module):
+    def __init__(self, input_channels, output_channels):
+        super(SPP, self).__init__()
+        self.Conv1x1 = Conv1x1(input_channels, output_channels)
+
+        self.S1 =  nn.Sequential(nn.Conv2d(output_channels, output_channels, 5, 1, 2, groups = output_channels, bias = False),
+                                 nn.BatchNorm2d(output_channels),
+                                 nn.ReLU(inplace=True)
+                                 )
+
+        self.S2 =  nn.Sequential(nn.Conv2d(output_channels, output_channels, 5, 1, 2, groups = output_channels, bias = False),
+                                 nn.BatchNorm2d(output_channels),
+                                 nn.ReLU(inplace=True),
+
+                                 nn.Conv2d(output_channels, output_channels, 5, 1, 2, groups = output_channels, bias = False),
+                                 nn.BatchNorm2d(output_channels),
+                                 nn.ReLU(inplace=True)
+                                 )
+
+        self.S3 =  nn.Sequential(nn.Conv2d(output_channels, output_channels, 5, 1, 2, groups = output_channels, bias = False),
+                                 nn.BatchNorm2d(output_channels),
+                                 nn.ReLU(inplace=True),
+
+                                 nn.Conv2d(output_channels, output_channels, 5, 1, 2, groups = output_channels, bias = False),
+                                 nn.BatchNorm2d(output_channels),
+                                 nn.ReLU(inplace=True),
+
+                                 nn.Conv2d(output_channels, output_channels, 5, 1, 2, groups = output_channels, bias = False),
+                                 nn.BatchNorm2d(output_channels),
+                                 nn.ReLU(inplace=True)
+                                 )
+
+        self.output = nn.Sequential(nn.Conv2d(output_channels * 3, output_channels, 1, 1, 0, bias = False),
+                                    nn.BatchNorm2d(output_channels),
+                                   )
+                                   
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):    
+        x = self.Conv1x1(x)
+
+        y1 = self.S1(x)
+        y2 = self.S2(x)
+        y3 = self.S3(x)
+
+        y = torch.cat((y1, y2, y3), dim=1)
+        y = self.relu(x + self.output(y))
+
+        return y
+
+class DetectHead(nn.Module):
+    def __init__(self, input_channels, category_num):
+        super(DetectHead, self).__init__()
+        self.conv1x1 =  Conv1x1(input_channels, input_channels)
+
+        self.obj_layers = Head(input_channels, 1)
+        self.reg_layers = Head(input_channels, 4)
+        self.cls_layers = Head(input_channels, category_num)
+
+        self.sigmoid = nn.Sigmoid()
+        self.softmax = nn.Softmax(dim=1)
+        
+    def forward(self, x):
+        x = self.conv1x1(x)
+        
+        obj = self.sigmoid(self.obj_layers(x))
+        reg = self.reg_layers(x)
+        cls = self.softmax(self.cls_layers(x))
+
+        return torch.cat((obj, reg, cls), dim =1)
\ No newline at end of file
diff --git a/module/detector.py b/module/detector.py
new file mode 100644
index 0000000..c9e23e2
--- /dev/null
+++ b/module/detector.py
@@ -0,0 +1,40 @@
+import torch
+import torch.nn as nn
+
+from .shufflenetv2 import ShuffleNetV2
+from .custom_layers import DetectHead, SPP
+
+class Detector(nn.Module):
+    def __init__(self, category_num, load_param):
+        super(Detector, self).__init__()
+
+        self.stage_repeats = [4, 8, 4]
+        self.stage_out_channels = [-1, 24, 48, 96, 192]
+        self.backbone = ShuffleNetV2(self.stage_repeats, self.stage_out_channels, load_param)
+
+        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
+        self.avg_pool = nn.AvgPool2d(kernel_size=3, stride=2, padding=1)
+        self.SPP = SPP(sum(self.stage_out_channels[-3:]), self.stage_out_channels[-2])
+         
+        self.detect_head = DetectHead(self.stage_out_channels[-2], category_num)
+
+    def forward(self, x):
+        P1, P2, P3 = self.backbone(x)
+        P3 = self.upsample(P3)
+        P1 = self.avg_pool(P1)
+        P = torch.cat((P1, P2, P3), dim=1)
+
+        y = self.SPP(P)
+
+        return self.detect_head(y)
+
+if __name__ == "__main__":
+    model = Detector(80, False)
+    test_data = torch.rand(1, 3, 352, 352)
+    torch.onnx.export(model,                    #model being run
+                     test_data,                 # model input (or a tuple for multiple inputs)
+                     "./test.onnx",             # where to save the model (can be a file or file-like object)
+                     export_params=True,        # store the trained parameter weights inside the model file
+                     opset_version=11,          # the ONNX version to export the model to
+                     do_constant_folding=True)  # whether to execute constant folding for optimization
+
diff --git a/module/loss.py b/module/loss.py
new file mode 100644
index 0000000..9658eb9
--- /dev/null
+++ b/module/loss.py
@@ -0,0 +1,148 @@
+import math
+import torch
+import torch.nn as nn
+
+class DetectorLoss(nn.Module):
+    def __init__(self, device):    
+        super(DetectorLoss, self).__init__()
+        self.device = device
+
+    def bbox_iou(self, box1, box2, eps=1e-7):
+        # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
+        box1 = box1.t()
+        box2 = box2.t()
+
+        b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2
+        b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2
+        b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2
+        b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2
+
+        # Intersection area
+        inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
+                (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
+
+        # Union Area
+        w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
+        w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
+        union = w1 * h1 + w2 * h2 - inter + eps
+        iou = inter / union
+
+        cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1)  # convex (smallest enclosing box) width
+        ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1)  # convex height
+
+        # SIoU Loss https://arxiv.org/pdf/2205.12740.pdf
+        s_cw = (b2_x1 + b2_x2 - b1_x1 - b1_x2) * 0.5
+        s_ch = (b2_y1 + b2_y2 - b1_y1 - b1_y2) * 0.5
+        sigma = torch.pow(s_cw ** 2 + s_ch ** 2, 0.5)
+        sin_alpha_1 = torch.abs(s_cw) / sigma
+        sin_alpha_2 = torch.abs(s_ch) / sigma
+        threshold = pow(2, 0.5) / 2
+        sin_alpha = torch.where(sin_alpha_1 > threshold, sin_alpha_2, sin_alpha_1)
+        angle_cost = torch.cos(torch.arcsin(sin_alpha) * 2 - math.pi / 2)
+        rho_x = (s_cw / cw) ** 2
+        rho_y = (s_ch / ch) ** 2
+        gamma = angle_cost - 2
+        distance_cost = 2 - torch.exp(gamma * rho_x) - torch.exp(gamma * rho_y)
+        omiga_w = torch.abs(w1 - w2) / torch.max(w1, w2)
+        omiga_h = torch.abs(h1 - h2) / torch.max(h1, h2)
+        shape_cost = torch.pow(1 - torch.exp(-1 * omiga_w), 4) + torch.pow(1 - torch.exp(-1 * omiga_h), 4)
+        iou = iou - 0.5 * (distance_cost + shape_cost)
+
+        return iou
+        
+    def build_target(self, preds, targets):
+        N, C, H, W = preds.shape
+        # batch存在标注的数据
+        gt_box, gt_cls, ps_index = [], [], []
+        # 每个网格的四个顶点为box中心点会归的基准点
+        quadrant = torch.tensor([[0, 0], [1, 0], 
+                                 [0, 1], [1, 1]], device=self.device)
+
+        if targets.shape[0] > 0:
+            # 将坐标映射到特征图尺度上
+            scale = torch.ones(6).to(self.device)
+            scale[2:] = torch.tensor(preds.shape)[[3, 2, 3, 2]]
+            gt = targets * scale
+
+            # 扩展维度复制数据
+            gt = gt.repeat(4, 1, 1)
+
+            # 过滤越界坐标
+            quadrant = quadrant.repeat(gt.size(1), 1, 1).permute(1, 0, 2)
+            gij = gt[..., 2:4].long() + quadrant
+            j = torch.where(gij < H, gij, 0).min(dim=-1)[0] > 0 
+
+            # 前景的位置下标
+            gi, gj = gij[j].T
+            batch_index = gt[..., 0].long()[j]
+            ps_index.append((batch_index, gi, gj))
+
+            # 前景的box
+            gbox = gt[..., 2:][j]
+            gt_box.append(gbox)
+            
+            # 前景的类别
+            gt_cls.append(gt[..., 1].long()[j])
+
+        return gt_box, gt_cls, ps_index
+
+        
+    def forward(self, preds, targets):
+        # 初始化loss值
+        ft = torch.cuda.FloatTensor if preds[0].is_cuda else torch.Tensor
+        cls_loss, iou_loss, obj_loss = ft([0]), ft([0]), ft([0])
+
+        # 定义obj和cls的损失函数
+        BCEcls = nn.NLLLoss() 
+        BCEobj = nn.BCELoss(reduction='none')
+        
+        # 构建ground truth
+        gt_box, gt_cls, ps_index = self.build_target(preds, targets)
+
+        pred = preds.permute(0, 2, 3, 1)
+        # 前背景分类分支
+        pobj = pred[:, :, :, 0]
+        # 检测框回归分支
+        preg = pred[:, :, :, 1:5]
+        # 目标类别分类分支
+        pcls = pred[:, :, :, 5:]
+
+        N, H, W, C = pred.shape
+        tobj = torch.zeros_like(pobj) 
+        factor = torch.ones_like(pobj) * 0.75
+
+        if len(gt_box[0]) > 0:
+            # 计算检测框回归loss
+            b, gx, gy = ps_index[0]
+            ptbox = torch.ones((preg[b, gy, gx].shape)).to(self.device)
+            ptbox[:, 0] = preg[b, gy, gx][:, 0].tanh() + gx
+            ptbox[:, 1] = preg[b, gy, gx][:, 1].tanh() + gy
+            ptbox[:, 2] = preg[b, gy, gx][:, 2].sigmoid() * W
+            ptbox[:, 3] = preg[b, gy, gx][:, 3].sigmoid() * H
+
+            # 计算检测框IOU loss
+            iou = self.bbox_iou(ptbox, gt_box[0])
+            # Filter
+            f = iou > iou.mean()
+            b, gy, gx = b[f], gy[f], gx[f]
+
+            # 计算iou loss
+            iou = iou[f]
+            iou_loss =  (1.0 - iou).mean() 
+
+            # 计算目标类别分类分支loss
+            ps = torch.log(pcls[b, gy, gx])
+            cls_loss = BCEcls(ps, gt_cls[0][f])
+
+            tobj[b, gy, gx] = 1.0
+            # 统计每个图片正样本的数量
+            n = torch.bincount(b)
+            factor[b, gy, gx] =  (1. / (n[b] / (H * W))) * 0.25
+
+        # 计算前背景分类分支loss
+        obj_loss = (BCEobj(pobj, tobj) * factor).mean()
+
+        # 计算总loss
+        loss = (iou_loss * 8) + (obj_loss * 16) + cls_loss                      
+              
+        return iou_loss, obj_loss, cls_loss, loss
\ No newline at end of file
diff --git a/module/shufflenetv2.pth b/module/shufflenetv2.pth
new file mode 100644
index 0000000..a58585f
Binary files /dev/null and b/module/shufflenetv2.pth differ
diff --git a/module/shufflenetv2.py b/module/shufflenetv2.py
new file mode 100644
index 0000000..89d0eb7
--- /dev/null
+++ b/module/shufflenetv2.py
@@ -0,0 +1,112 @@
+import torch
+import torch.nn as nn
+
+class ShuffleV2Block(nn.Module):
+    def __init__(self, inp, oup, mid_channels, *, ksize, stride):
+        super(ShuffleV2Block, self).__init__()
+        self.stride = stride
+        assert stride in [1, 2]
+
+        self.mid_channels = mid_channels
+        self.ksize = ksize
+        pad = ksize // 2
+        self.pad = pad
+        self.inp = inp
+
+        outputs = oup - inp
+
+        branch_main = [
+            # pw
+            nn.Conv2d(inp, mid_channels, 1, 1, 0, bias=False),
+            nn.BatchNorm2d(mid_channels),
+            nn.ReLU(inplace=True),
+            # dw
+            nn.Conv2d(mid_channels, mid_channels, ksize, stride, pad, groups=mid_channels, bias=False),
+            nn.BatchNorm2d(mid_channels),
+            # pw-linear
+            nn.Conv2d(mid_channels, outputs, 1, 1, 0, bias=False),
+            nn.BatchNorm2d(outputs),
+            nn.ReLU(inplace=True),
+        ]
+        self.branch_main = nn.Sequential(*branch_main)
+
+        if stride == 2:
+            branch_proj = [
+                # dw
+                nn.Conv2d(inp, inp, ksize, stride, pad, groups=inp, bias=False),
+                nn.BatchNorm2d(inp),
+                # pw-linear
+                nn.Conv2d(inp, inp, 1, 1, 0, bias=False),
+                nn.BatchNorm2d(inp),
+                nn.ReLU(inplace=True),
+            ]
+            self.branch_proj = nn.Sequential(*branch_proj)
+        else:
+            self.branch_proj = None
+
+    def forward(self, old_x):
+        if self.stride==1:
+            x_proj, x = self.channel_shuffle(old_x)
+            return torch.cat((x_proj, self.branch_main(x)), 1)
+        elif self.stride==2:
+            x_proj = old_x
+            x = old_x
+            return torch.cat((self.branch_proj(x_proj), self.branch_main(x)), 1)
+
+    def channel_shuffle(self, x):
+        batchsize, num_channels, height, width = x.data.size()
+        assert (num_channels % 4 == 0)
+        x = x.reshape(batchsize * num_channels // 2, 2, height * width)
+        x = x.permute(1, 0, 2)
+        x = x.reshape(2, -1, num_channels // 2, height, width)
+        return x[0], x[1]
+
+class ShuffleNetV2(nn.Module):
+    def __init__(self, stage_repeats, stage_out_channels, load_param):
+        super(ShuffleNetV2, self).__init__()
+
+        self.stage_repeats = stage_repeats
+        self.stage_out_channels = stage_out_channels
+
+        # building first layer
+        input_channel = self.stage_out_channels[1]
+        self.first_conv = nn.Sequential(
+            nn.Conv2d(3, input_channel, 3, 2, 1, bias=False),
+            nn.BatchNorm2d(input_channel),
+            nn.ReLU(inplace=True),
+        )
+
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        stage_names = ["stage2", "stage3", "stage4"]
+        for idxstage in range(len(self.stage_repeats)):
+            numrepeat = self.stage_repeats[idxstage]
+            output_channel = self.stage_out_channels[idxstage+2]
+            stageSeq = []
+            for i in range(numrepeat):
+                if i == 0:
+                    stageSeq.append(ShuffleV2Block(input_channel, output_channel, 
+                                                mid_channels=output_channel // 2, ksize=3, stride=2))
+                else:
+                    stageSeq.append(ShuffleV2Block(input_channel // 2, output_channel, 
+                                                mid_channels=output_channel // 2, ksize=3, stride=1))
+                input_channel = output_channel
+            setattr(self, stage_names[idxstage], nn.Sequential(*stageSeq))
+        
+        if load_param == False:
+            self._initialize_weights()
+        else:
+            print("load param...")
+
+    def forward(self, x):
+        x = self.first_conv(x)
+        x = self.maxpool(x)
+        P1 = self.stage2(x)
+        P2 = self.stage3(P1)
+        P3 = self.stage4(P2)
+
+        return P1, P2, P3
+
+    def _initialize_weights(self):
+        print("Initialize params from:%s"%"./module/shufflenetv2.pth")
+        self.load_state_dict(torch.load("./module/shufflenetv2.pth"), strict = True)
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..3343b6b
--- /dev/null
+++ b/test.py
@@ -0,0 +1,88 @@
+import os
+import cv2
+import time
+import argparse
+
+import torch
+from utils.tool import *
+from module.detector import Detector
+
+# 指定后端设备CUDA&CPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+if __name__ == '__main__':
+    # 指定训练配置文件
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--yaml', type=str, default="", help='.yaml config')
+    parser.add_argument('--weight', type=str, default=None, help='.weight config')
+    parser.add_argument('--img', type=str, default='', help='The path of test image')
+    parser.add_argument('--thresh', type=float, default=0.8, help='The path of test image')
+    parser.add_argument('--onnx', action="store_true", default=False, help='Export onnx file')
+
+    opt = parser.parse_args()
+    assert os.path.exists(opt.yaml), "请指定正确的配置文件路径"
+    assert os.path.exists(opt.weight), "请指定正确的模型路径"
+    assert os.path.exists(opt.img), "请指定正确的测试图像路径"
+
+    # 解析yaml配置文件
+    cfg = LoadYaml(opt.yaml)    
+    print(cfg) 
+
+    # 模型加载
+    print("load weight from:%s"%opt.weight)
+    model = Detector(cfg.category_num, True).to(device)
+    model.load_state_dict(torch.load(opt.weight))
+    #sets the module in eval node
+    model.eval()
+    
+    # 数据预处理
+    ori_img = cv2.imread(opt.img)
+    res_img = cv2.resize(ori_img, (cfg.input_width, cfg.input_height), interpolation = cv2.INTER_LINEAR) 
+    img = res_img.reshape(1, cfg.input_height, cfg.input_width, 3)
+    img = torch.from_numpy(img.transpose(0, 3, 1, 2))
+    img = img.to(device).float() / 255.0
+
+    # 导出onnx模型
+    if opt.onnx:
+        torch.onnx.export(model,                    #model being run
+                          img,                 # model input (or a tuple for multiple inputs)
+                          "./FastestDet.onnx",             # where to save the model (can be a file or file-like object)
+                          export_params=True,        # store the trained parameter weights inside the model file
+                          opset_version=11,          # the ONNX version to export the model to
+                          do_constant_folding=True)  # whether to execute constant folding for optimization
+
+    # 模型推理
+    start = time.perf_counter()
+    preds = model(img)
+    end = time.perf_counter()
+    time = (end - start) * 1000.
+    print("forward time:%fms"%time)
+
+    # 特征图后处理
+    output = handel_preds(preds, device, opt.thresh)
+
+    # 加载label names
+    LABEL_NAMES = []
+    with open(cfg.names, 'r') as f:
+	    for line in f.readlines():
+	        LABEL_NAMES.append(line.strip())
+    
+    H, W, _ = ori_img.shape
+    scale_h, scale_w = H / cfg.input_height, W / cfg.input_width
+
+    # 绘制预测框
+    for box in output[0]:
+        print(box)
+        box = box.tolist()
+       
+        obj_score = box[4]
+        category = LABEL_NAMES[int(box[5])]
+
+        x1, y1 = int(box[0] * W), int(box[1] * H)
+        x2, y2 = int(box[2] * W), int(box[3] * H)
+
+        cv2.rectangle(ori_img, (x1, y1), (x2, y2), (255, 255, 0), 2)
+        cv2.putText(ori_img, '%.2f' % obj_score, (x1, y1 - 5), 0, 0.7, (0, 255, 0), 2)	
+        cv2.putText(ori_img, category, (x1, y1 - 25), 0, 0.7, (0, 255, 0), 2)
+
+    cv2.imwrite("result.png", ori_img)
\ No newline at end of file
diff --git a/train.py b/train.py
new file mode 100644
index 0000000..93271f1
--- /dev/null
+++ b/train.py
@@ -0,0 +1,134 @@
+import os
+import math
+import torch
+import argparse
+from tqdm import tqdm
+from torch import optim
+from torchsummary import summary
+
+from utils.tool import *
+from utils.datasets import *
+from utils.evaluation import CocoDetectionEvaluator
+
+from module.loss import DetectorLoss
+from module.detector import Detector
+
+# 指定后端设备CUDA&CPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+class FastestDet:
+    def __init__(self):
+        # 指定训练配置文件
+        parser = argparse.ArgumentParser()
+        parser.add_argument('--yaml', type=str, default="", help='.yaml config')
+        parser.add_argument('--weight', type=str, default=None, help='.weight config')
+
+        opt = parser.parse_args()
+        assert os.path.exists(opt.yaml), "请指定正确的配置文件路径"
+
+        # 解析yaml配置文件
+        self.cfg = LoadYaml(opt.yaml)    
+        print(self.cfg) 
+
+        # 初始化模型结构
+        if opt.weight is not None:
+            print("load weight from:%s"%opt.weight)
+            self.model = Detector(self.cfg.category_num, True).to(device)
+            self.model.load_state_dict(torch.load(opt.weight))
+        else:
+            self.model = Detector(self.cfg.category_num, False).to(device)
+
+        # # 打印网络各层的张量维度
+        summary(self.model, input_size=(3, self.cfg.input_height, self.cfg.input_width))
+
+        #构建优化器
+        print("use SGD optimizer")
+        self.optimizer = optim.SGD(params=self.model.parameters(),
+                                   lr=self.cfg.learn_rate,
+                                   momentum=0.949,
+                                   weight_decay=0.0005,
+                                   )
+        # 学习率衰减策略
+        self.scheduler = optim.lr_scheduler.MultiStepLR(self.optimizer,
+                                                        milestones=self.cfg.milestones,
+                                                        gamma=0.1)
+
+        # 定义损失函数
+        self.loss_function = DetectorLoss(device)
+        
+        # 定义验证函数
+        self.evaluation = CocoDetectionEvaluator(self.cfg.names, device)
+
+        # 数据集加载
+        val_dataset = TensorDataset(self.cfg.val_txt, self.cfg.input_width, self.cfg.input_height, False)
+        train_dataset = TensorDataset(self.cfg.train_txt, self.cfg.input_width, self.cfg.input_height, True)
+
+        #验证集
+        self.val_dataloader = torch.utils.data.DataLoader(val_dataset,
+                                                          batch_size=self.cfg.batch_size,
+                                                          shuffle=False,
+                                                          collate_fn=collate_fn,
+                                                          num_workers=4,
+                                                          drop_last=False,
+                                                          persistent_workers=True
+                                                          )
+        # 训练集
+        self.train_dataloader = torch.utils.data.DataLoader(train_dataset,
+                                                            batch_size=self.cfg.batch_size,
+                                                            shuffle=True,
+                                                            collate_fn=collate_fn,
+                                                            num_workers=4,
+                                                            drop_last=True,
+                                                            persistent_workers=True
+                                                            )
+
+    def train(self):
+        # 迭代训练
+        batch_num = 0
+        print('Starting training for %g epochs...' % self.cfg.end_epoch)
+        for epoch in range(self.cfg.end_epoch + 1):
+            self.model.train()
+            pbar = tqdm(self.train_dataloader)
+            for imgs, targets in pbar:
+                # 数据预处理
+                imgs = imgs.to(device).float() / 255.0
+                targets = targets.to(device)
+                # 模型推理
+                preds = self.model(imgs)
+
+                # loss计算
+                iou, obj, cls, total = self.loss_function(preds, targets)
+                # 反向传播求解梯度
+                total.backward()
+                # 更新模型参数
+                self.optimizer.step()
+                self.optimizer.zero_grad()
+
+                # 学习率预热
+                for g in self.optimizer.param_groups:
+                    warmup_num =  5 * len(self.train_dataloader)
+                    if batch_num <= warmup_num:
+                        scale = math.pow(batch_num/warmup_num, 4)
+                        g['lr'] = self.cfg.learn_rate * scale
+                    lr = g["lr"]
+
+                # 打印相关训练信息
+                info = "Epoch:%d LR:%f IOU:%f Obj:%f Cls:%f Total:%f" % (
+                        epoch, lr, iou, obj, cls, total)
+                pbar.set_description(info)
+                batch_num += 1
+
+            # 模型验证及保存
+            if epoch % 10 == 0 and epoch > 0:
+                # 模型评估
+                self.model.eval()
+                print("computer mAP...")
+                mAP05 = self.evaluation.compute_map(self.val_dataloader, self.model)
+                torch.save(self.model.state_dict(), "checkpoint/weight_AP05:%f_%d-epoch.pth"%(mAP05, epoch))
+
+            # 学习率调整
+            self.scheduler.step()
+
+if __name__ == "__main__":
+    model = FastestDet()
+    model.train()
\ No newline at end of file
diff --git a/utils/datasets.py b/utils/datasets.py
new file mode 100644
index 0000000..886b992
--- /dev/null
+++ b/utils/datasets.py
@@ -0,0 +1,110 @@
+import os
+import cv2
+import numpy as np
+
+import torch
+import random
+
+def random_scale(image, boxes):
+    height, width, _ = image.shape
+    # random crop imgage
+    cw, ch = random.randint(int(width * 0.75), width), random.randint(int(height * 0.75), height)
+    cx, cy = random.randint(0, width - cw), random.randint(0, height - ch)
+
+    roi = image[cy:cy + ch, cx:cx + cw]
+    roi_h, roi_w, _ = roi.shape
+    
+    output = []
+    for box in boxes:
+        index, category = box[0], box[1]
+        bx, by = box[2] * width, box[3] * height
+        bw, bh = box[4] * width, box[5] * height
+
+        bx, by = (bx - cx)/roi_w, (by - cy)/roi_h
+        bw, bh = bw/roi_w, bh/roi_h
+
+        output.append([index, category, bx, by, bw, bh])
+
+    output = np.array(output, dtype=float)
+
+    return roi, output
+
+def collate_fn(batch):
+    img, label = zip(*batch)
+    for i, l in enumerate(label):
+        if l.shape[0] > 0:
+            l[:, 0] = i
+    return torch.stack(img), torch.cat(label, 0)
+
+class TensorDataset():
+    def __init__(self, path, img_width, img_height, aug=False):
+        assert os.path.exists(path), "%s文件路径错误或不存在" % path
+
+        self.aug = aug
+        self.path = path
+        self.data_list = []
+        self.img_width = img_width
+        self.img_height = img_height
+        self.img_formats = ['bmp', 'jpg', 'jpeg', 'png']
+
+        # 数据检查
+        with open(self.path, 'r') as f:
+            for line in f.readlines():
+                data_path = line.strip()
+                if os.path.exists(data_path):
+                    img_type = data_path.split(".")[-1]
+                    if img_type not in self.img_formats:
+                        raise Exception("img type error:%s" % img_type)
+                    else:
+                        self.data_list.append(data_path)
+                else:
+                    raise Exception("%s is not exist" % data_path)
+
+    def __getitem__(self, index):
+        img_path = self.data_list[index]
+        label_path = img_path.split(".")[0] + ".txt"
+
+        # 加载图片
+        img = cv2.imread(img_path)
+        # 加载label文件
+        if os.path.exists(label_path):
+            label = []
+            with open(label_path, 'r') as f:
+                for line in f.readlines():
+                    l = line.strip().split(" ")
+                    label.append([0, l[0], l[1], l[2], l[3], l[4]])
+            label = np.array(label, dtype=np.float32)
+
+            if label.shape[0]:
+                assert label.shape[1] == 6, '> 5 label columns: %s' % label_path
+                #assert (label >= 0).all(), 'negative labels: %s'%label_path
+                #assert (label[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels: %s'%label_path
+        else:
+            raise Exception("%s is not exist" % label_path) 
+
+        # 是否进行数据增强
+        if self.aug:
+            img, label = random_scale(img, label)
+
+        img = cv2.resize(img, (self.img_width, self.img_height), interpolation = cv2.INTER_LINEAR) 
+
+        # debug
+        # for box in label:
+        #     bx, by, bw, bh = box[2], box[3], box[4], box[5]
+        #     x1, y1 = int((bx - 0.5 * bw) * self.img_width), int((by - 0.5 * bh) * self.img_height)
+        #     x2, y2 = int((bx + 0.5 * bw) * self.img_width), int((by + 0.5 * bh) * self.img_height)
+        #     cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        # cv2.imwrite("debug.jpg", img)
+
+        img = img.transpose(2,0,1)
+        
+        return torch.from_numpy(img), torch.from_numpy(label)
+
+    def __len__(self):
+        return len(self.data_list)
+
+if __name__ == "__main__":
+    data = TensorDataset("/home/xuehao/Desktop/TMP/pytorch-yolo/widerface/train.txt")
+    img, label = data.__getitem__(0)
+    print(img.shape)
+    print(label.shape)
\ No newline at end of file
diff --git a/utils/evaluation.py b/utils/evaluation.py
new file mode 100644
index 0000000..f00f5cc
--- /dev/null
+++ b/utils/evaluation.py
@@ -0,0 +1,100 @@
+import torch
+import numpy as np
+from tqdm import tqdm
+from utils.tool import *
+
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+
+class CocoDetectionEvaluator():
+    def __init__(self, names, device):
+        self.device = device
+        self.classes = []
+        with open(names, 'r') as f:
+            for line in f.readlines():
+                self.classes.append(line.strip())
+    
+    def coco_evaluate(self, gts, preds):
+        # Create Ground Truth
+        coco_gt = COCO()
+        coco_gt.dataset = {}
+        coco_gt.dataset["images"] = []
+        coco_gt.dataset["annotations"] = []
+        k = 0
+        for i, gt in enumerate(gts):
+            for j in range(gt.shape[0]):
+                k += 1
+                coco_gt.dataset["images"].append({"id": i})
+                coco_gt.dataset["annotations"].append({"image_id": i, "category_id": gt[j, 0],
+                                                    "bbox": np.hstack([gt[j, 1:3], gt[j, 3:5] - gt[j, 1:3]]),
+                                                    "area": np.prod(gt[j, 3:5] - gt[j, 1:3]),
+                                                    "id": k, "iscrowd": 0})
+                
+        coco_gt.dataset["categories"] = [{"id": i, "supercategory": c, "name": c} for i, c in enumerate(self.classes)]
+        coco_gt.createIndex()
+
+        # Create preadict 
+        coco_pred = COCO()
+        coco_pred.dataset = {}
+        coco_pred.dataset["images"] = []
+        coco_pred.dataset["annotations"] = []
+        k = 0
+        for i, pred in enumerate(preds):
+            for j in range(pred.shape[0]):
+                k += 1
+                coco_pred.dataset["images"].append({"id": i})
+                coco_pred.dataset["annotations"].append({"image_id": i, "category_id": np.int(pred[j, 0]),
+                                                        "score": pred[j, 1], "bbox": np.hstack([pred[j, 2:4], pred[j, 4:6] - pred[j, 2:4]]),
+                                                        "area": np.prod(pred[j, 4:6] - pred[j, 2:4]),
+                                                        "id": k})
+                
+        coco_pred.dataset["categories"] = [{"id": i, "supercategory": c, "name": c} for i, c in enumerate(self.classes)]
+        coco_pred.createIndex()
+
+        coco_eval = COCOeval(coco_gt, coco_pred, "bbox")
+        coco_eval.evaluate()
+        coco_eval.accumulate()
+        coco_eval.summarize()
+        mAP05 = coco_eval.stats[1]
+        return mAP05
+
+    def compute_map(self, val_dataloader, model):
+        gts, pts = [], []
+        pbar = tqdm(val_dataloader)
+        for i, (imgs, targets) in enumerate(pbar):
+            # 数据预处理
+            imgs = imgs.to(self.device).float() / 255.0
+            with torch.no_grad():
+                # 模型预测
+                preds = model(imgs)
+                # 特征图后处理
+                output = handel_preds(preds, self.device, 0.001)
+
+            # 检测结果
+            N, _, H, W = imgs.shape
+            for p in output:
+                pbboxes = []
+                for b in p:
+                    b = b.cpu().numpy()
+                    score = b[4]
+                    category = b[5]
+                    x1, y1, x2, y2 = b[:4] * [W, H, W, H]
+                    pbboxes.append([category, score, x1, y1, x2, y2])
+                pts.append(np.array(pbboxes))
+
+            # 标注结果
+            for n in range(N):
+                tbboxes = []
+                for t in targets:
+                    if t[0] == n:
+                        t = t.cpu().numpy()
+                        category = t[1]
+                        bcx, bcy, bw, bh = t[2:] * [W, H, W, H]
+                        x1, y1 = bcx - 0.5 * bw, bcy - 0.5 * bh
+                        x2, y2 = bcx + 0.5 * bw, bcy + 0.5 * bh
+                        tbboxes.append([category, x1, y1, x2, y2])
+                gts.append(np.array(tbboxes))
+                
+        mAP05 = self.coco_evaluate(gts, pts)
+
+        return mAP05
diff --git a/utils/tool.py b/utils/tool.py
new file mode 100644
index 0000000..82380b0
--- /dev/null
+++ b/utils/tool.py
@@ -0,0 +1,120 @@
+import yaml
+import torch
+import torchvision
+
+# 解析yaml配置文件
+class LoadYaml:
+    def __init__(self, path):
+        with open(path, encoding='utf8') as f:
+            data = yaml.load(f, Loader=yaml.FullLoader)
+
+        self.val_txt = data["DATASET"]["VAL"]
+        self.train_txt = data["DATASET"]["TRAIN"]
+        self.names = data["DATASET"]["NAMES"]
+
+        self.learn_rate = data["TRAIN"]["LR"]
+        self.batch_size = data["TRAIN"]["BATCH_SIZE"]
+        self.milestones = data["TRAIN"]["MILESTIONES"]
+        self.end_epoch = data["TRAIN"]["END_EPOCH"]
+        
+        self.input_width = data["MODEL"]["INPUT_WIDTH"]
+        self.input_height = data["MODEL"]["INPUT_HEIGHT"]
+        
+        self.category_num = data["MODEL"]["NC"]
+        
+        print("Load yaml sucess...")
+
+class EMA():
+    def __init__(self, model, decay):
+        self.model = model
+        self.decay = decay
+        self.shadow = {}
+        self.backup = {}
+
+    def register(self):
+        for name, param in self.model.named_parameters():
+            if param.requires_grad:
+                self.shadow[name] = param.data.clone()
+
+    def update(self):
+        for name, param in self.model.named_parameters():
+            if param.requires_grad:
+                assert name in self.shadow
+                new_average = (1.0 - self.decay) * param.data + self.decay * self.shadow[name]
+                self.shadow[name] = new_average.clone()
+
+    def apply_shadow(self):
+        for name, param in self.model.named_parameters():
+            if param.requires_grad:
+                assert name in self.shadow
+                self.backup[name] = param.data
+                param.data = self.shadow[name]
+
+    def restore(self):
+        for name, param in self.model.named_parameters():
+            if param.requires_grad:
+                assert name in self.backup
+                param.data = self.backup[name]
+        self.backup = {}
+
+# 后处理(归一化后的坐标)
+def handel_preds(preds, device, conf_thresh=0.25, nms_thresh=0.35):
+    total_bboxes, output_bboxes  = [], []
+    # 将特征图转换为检测框的坐标
+    N, C, H, W = preds.shape
+    bboxes = torch.zeros((N, H, W, 6))
+    pred = preds.permute(0, 2, 3, 1)
+    # 前背景分类分支
+    pobj = pred[:, :, :, 0].unsqueeze(dim=-1)
+    # 检测框回归分支
+    preg = pred[:, :, :, 1:5]
+    # 目标类别分类分支
+    pcls = pred[:, :, :, 5:]
+
+    # 检测框置信度
+    bboxes[..., 4] = pobj.squeeze(-1) * pcls.max(dim=-1)[0]
+    bboxes[..., 5] = pcls.argmax(dim=-1)
+
+    # 检测框的坐标
+    gy, gx = torch.meshgrid([torch.arange(H), torch.arange(W)])
+    bw, bh = preg[..., 2].sigmoid(), preg[..., 3].sigmoid() 
+    bcx = (preg[..., 0].tanh() + gx.to(device)) / W
+    bcy = (preg[..., 1].tanh() + gy.to(device)) / H
+
+    # cx,cy,w,h = > x1,y1,x2,y1
+    x1, y1 = bcx - 0.5 * bw, bcy - 0.5 * bh
+    x2, y2 = bcx + 0.5 * bw, bcy + 0.5 * bh
+
+    bboxes[..., 0], bboxes[..., 1] = x1, y1
+    bboxes[..., 2], bboxes[..., 3] = x2, y2
+    bboxes = bboxes.reshape(N, H*W, 6)
+    total_bboxes.append(bboxes)
+        
+    batch_bboxes = torch.cat(total_bboxes, 1)
+
+    # 对检测框进行NMS处理
+    for p in batch_bboxes:
+        output, temp = [], []
+        b, s, c = [], [], []
+        # 阈值筛选
+        t = p[:, 4] > conf_thresh
+        pb = p[t]
+        for bbox in pb:
+            obj_score = bbox[4]
+            category = bbox[5]
+            x1, y1 = bbox[0], bbox[1]
+            x2, y2 = bbox[2], bbox[3]
+            s.append([obj_score])
+            c.append([category])
+            b.append([x1, y1, x2, y2])
+            temp.append([x1, y1, x2, y2, obj_score, category])
+        # Torchvision NMS
+        if len(b) > 0:
+            b = torch.Tensor(b).to(device)
+            c = torch.Tensor(c).squeeze(1).to(device)
+            s = torch.Tensor(s).squeeze(1).to(device)
+            keep = torchvision.ops.batched_nms(b, s, c, nms_thresh)
+            for i in keep:
+                output.append(temp[i])
+        output_bboxes.append(torch.Tensor(output))
+    return output_bboxes
\ No newline at end of file
diff --git a/weights/README.md b/weights/README.md
new file mode 100644
index 0000000..af634f0
--- /dev/null
+++ b/weights/README.md
@@ -0,0 +1 @@
+# pytorch-detector
\ No newline at end of file
diff --git a/weights/weight_AP05:0.248723_280-epoch.pth b/weights/weight_AP05:0.248723_280-epoch.pth
new file mode 100644
index 0000000..9733fc9
Binary files /dev/null and b/weights/weight_AP05:0.248723_280-epoch.pth differ