first init

configs/yolov8_seg/yolov8_seg_x_500e_coco.yml

+_BASE_: [
+  '../datasets/coco_instance.yml',
+  '../runtime.yml',
+  '_base_/optimizer_500e_high.yml',
+  '_base_/yolov8_seg_cspdarknet.yml',
+  '_base_/yolov8_seg_reader_high_aug.yml',
+]
+depth_mult: 1.0 # not 1.33 as in YOLOv5
+width_mult: 1.25
+
+log_iter: 50
+snapshot_epoch: 10
+weights: output/yolov8_seg_x_500e_coco/model_final
+
+
+YOLOv8CSPDarkNet:
+  last_stage_ch: 512 # The actual channel is int(512 * width_mult), not int(1024 * width_mult) as in YOLOv5
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128
+
+EvalReader:
+  batch_size: 1

dataset/OpenImagesV7/label_list.txt

+  0: Accordion
+  1: Adhesive tape
+  2: Aircraft
+  3: Airplane
+  4: Alarm clock
+  5: Alpaca
+  6: Ambulance
+  7: Animal
+  8: Ant
+  9: Antelope
+  10: Apple
+  11: Armadillo
+  12: Artichoke
+  13: Auto part
+  14: Axe
+  15: Backpack
+  16: Bagel
+  17: Baked goods
+  18: Balance beam
+  19: Ball
+  20: Balloon
+  21: Banana
+  22: Band-aid
+  23: Banjo
+  24: Barge
+  25: Barrel
+  26: Baseball bat
+  27: Baseball glove
+  28: Bat (Animal)
+  29: Bathroom accessory
+  30: Bathroom cabinet
+  31: Bathtub
+  32: Beaker
+  33: Bear
+  34: Bed
+  35: Bee
+  36: Beehive
+  37: Beer
+  38: Beetle
+  39: Bell pepper
+  40: Belt
+  41: Bench
+  42: Bicycle
+  43: Bicycle helmet
+  44: Bicycle wheel
+  45: Bidet
+  46: Billboard
+  47: Billiard table
+  48: Binoculars
+  49: Bird
+  50: Blender
+  51: Blue jay
+  52: Boat
+  53: Bomb
+  54: Book
+  55: Bookcase
+  56: Boot
+  57: Bottle
+  58: Bottle opener
+  59: Bow and arrow
+  60: Bowl
+  61: Bowling equipment
+  62: Box
+  63: Boy
+  64: Brassiere
+  65: Bread
+  66: Briefcase
+  67: Broccoli
+  68: Bronze sculpture
+  69: Brown bear
+  70: Building
+  71: Bull
+  72: Burrito
+  73: Bus
+  74: Bust
+  75: Butterfly
+  76: Cabbage
+  77: Cabinetry
+  78: Cake
+  79: Cake stand
+  80: Calculator
+  81: Camel
+  82: Camera
+  83: Can opener
+  84: Canary
+  85: Candle
+  86: Candy
+  87: Cannon
+  88: Canoe
+  89: Cantaloupe
+  90: Car
+  91: Carnivore
+  92: Carrot
+  93: Cart
+  94: Cassette deck
+  95: Castle
+  96: Cat
+  97: Cat furniture
+  98: Caterpillar
+  99: Cattle
+  100: Ceiling fan
+  101: Cello
+  102: Centipede
+  103: Chainsaw
+  104: Chair
+  105: Cheese
+  106: Cheetah
+  107: Chest of drawers
+  108: Chicken
+  109: Chime
+  110: Chisel
+  111: Chopsticks
+  112: Christmas tree
+  113: Clock
+  114: Closet
+  115: Clothing
+  116: Coat
+  117: Cocktail
+  118: Cocktail shaker
+  119: Coconut
+  120: Coffee
+  121: Coffee cup
+  122: Coffee table
+  123: Coffeemaker
+  124: Coin
+  125: Common fig
+  126: Common sunflower
+  127: Computer keyboard
+  128: Computer monitor
+  129: Computer mouse
+  130: Container
+  131: Convenience store
+  132: Cookie
+  133: Cooking spray
+  134: Corded phone
+  135: Cosmetics
+  136: Couch
+  137: Countertop
+  138: Cowboy hat
+  139: Crab
+  140: Cream
+  141: Cricket ball
+  142: Crocodile
+  143: Croissant
+  144: Crown
+  145: Crutch
+  146: Cucumber
+  147: Cupboard
+  148: Curtain
+  149: Cutting board
+  150: Dagger
+  151: Dairy Product
+  152: Deer
+  153: Desk
+  154: Dessert
+  155: Diaper
+  156: Dice
+  157: Digital clock
+  158: Dinosaur
+  159: Dishwasher
+  160: Dog
+  161: Dog bed
+  162: Doll
+  163: Dolphin
+  164: Door
+  165: Door handle
+  166: Doughnut
+  167: Dragonfly
+  168: Drawer
+  169: Dress
+  170: Drill (Tool)
+  171: Drink
+  172: Drinking straw
+  173: Drum
+  174: Duck
+  175: Dumbbell
+  176: Eagle
+  177: Earrings
+  178: Egg (Food)
+  179: Elephant
+  180: Envelope
+  181: Eraser
+  182: Face powder
+  183: Facial tissue holder
+  184: Falcon
+  185: Fashion accessory
+  186: Fast food
+  187: Fax
+  188: Fedora
+  189: Filing cabinet
+  190: Fire hydrant
+  191: Fireplace
+  192: Fish
+  193: Flag
+  194: Flashlight
+  195: Flower
+  196: Flowerpot
+  197: Flute
+  198: Flying disc
+  199: Food
+  200: Food processor
+  201: Football
+  202: Football helmet
+  203: Footwear
+  204: Fork
+  205: Fountain
+  206: Fox
+  207: French fries
+  208: French horn
+  209: Frog
+  210: Fruit
+  211: Frying pan
+  212: Furniture
+  213: Garden Asparagus
+  214: Gas stove
+  215: Giraffe
+  216: Girl
+  217: Glasses
+  218: Glove
+  219: Goat
+  220: Goggles
+  221: Goldfish
+  222: Golf ball
+  223: Golf cart
+  224: Gondola
+  225: Goose
+  226: Grape
+  227: Grapefruit
+  228: Grinder
+  229: Guacamole
+  230: Guitar
+  231: Hair dryer
+  232: Hair spray
+  233: Hamburger
+  234: Hammer
+  235: Hamster
+  236: Hand dryer
+  237: Handbag
+  238: Handgun
+  239: Harbor seal
+  240: Harmonica
+  241: Harp
+  242: Harpsichord
+  243: Hat
+  244: Headphones
+  245: Heater
+  246: Hedgehog
+  247: Helicopter
+  248: Helmet
+  249: High heels
+  250: Hiking equipment
+  251: Hippopotamus
+  252: Home appliance
+  253: Honeycomb
+  254: Horizontal bar
+  255: Horse
+  256: Hot dog
+  257: House
+  258: Houseplant
+  259: Human arm
+  260: Human beard
+  261: Human body
+  262: Human ear
+  263: Human eye
+  264: Human face
+  265: Human foot
+  266: Human hair
+  267: Human hand
+  268: Human head
+  269: Human leg
+  270: Human mouth
+  271: Human nose
+  272: Humidifier
+  273: Ice cream
+  274: Indoor rower
+  275: Infant bed
+  276: Insect
+  277: Invertebrate
+  278: Ipod
+  279: Isopod
+  280: Jacket
+  281: Jacuzzi
+  282: Jaguar (Animal)
+  283: Jeans
+  284: Jellyfish
+  285: Jet ski
+  286: Jug
+  287: Juice
+  288: Kangaroo
+  289: Kettle
+  290: Kitchen & dining room table
+  291: Kitchen appliance
+  292: Kitchen knife
+  293: Kitchen utensil
+  294: Kitchenware
+  295: Kite
+  296: Knife
+  297: Koala
+  298: Ladder
+  299: Ladle
+  300: Ladybug
+  301: Lamp
+  302: Land vehicle
+  303: Lantern
+  304: Laptop
+  305: Lavender (Plant)
+  306: Lemon
+  307: Leopard
+  308: Light bulb
+  309: Light switch
+  310: Lighthouse
+  311: Lily
+  312: Limousine
+  313: Lion
+  314: Lipstick
+  315: Lizard
+  316: Lobster
+  317: Loveseat
+  318: Luggage and bags
+  319: Lynx
+  320: Magpie
+  321: Mammal
+  322: Man
+  323: Mango
+  324: Maple
+  325: Maracas
+  326: Marine invertebrates
+  327: Marine mammal
+  328: Measuring cup
+  329: Mechanical fan
+  330: Medical equipment
+  331: Microphone
+  332: Microwave oven
+  333: Milk
+  334: Miniskirt
+  335: Mirror
+  336: Missile
+  337: Mixer
+  338: Mixing bowl
+  339: Mobile phone
+  340: Monkey
+  341: Moths and butterflies
+  342: Motorcycle
+  343: Mouse
+  344: Muffin
+  345: Mug
+  346: Mule
+  347: Mushroom
+  348: Musical instrument
+  349: Musical keyboard
+  350: Nail (Construction)
+  351: Necklace
+  352: Nightstand
+  353: Oboe
+  354: Office building
+  355: Office supplies
+  356: Orange
+  357: Organ (Musical Instrument)
+  358: Ostrich
+  359: Otter
+  360: Oven
+  361: Owl
+  362: Oyster
+  363: Paddle
+  364: Palm tree
+  365: Pancake
+  366: Panda
+  367: Paper cutter
+  368: Paper towel
+  369: Parachute
+  370: Parking meter
+  371: Parrot
+  372: Pasta
+  373: Pastry
+  374: Peach
+  375: Pear
+  376: Pen
+  377: Pencil case
+  378: Pencil sharpener
+  379: Penguin
+  380: Perfume
+  381: Person
+  382: Personal care
+  383: Personal flotation device
+  384: Piano
+  385: Picnic basket
+  386: Picture frame
+  387: Pig
+  388: Pillow
+  389: Pineapple
+  390: Pitcher (Container)
+  391: Pizza
+  392: Pizza cutter
+  393: Plant
+  394: Plastic bag
+  395: Plate
+  396: Platter
+  397: Plumbing fixture
+  398: Polar bear
+  399: Pomegranate
+  400: Popcorn
+  401: Porch
+  402: Porcupine
+  403: Poster
+  404: Potato
+  405: Power plugs and sockets
+  406: Pressure cooker
+  407: Pretzel
+  408: Printer
+  409: Pumpkin
+  410: Punching bag
+  411: Rabbit
+  412: Raccoon
+  413: Racket
+  414: Radish
+  415: Ratchet (Device)
+  416: Raven
+  417: Rays and skates
+  418: Red panda
+  419: Refrigerator
+  420: Remote control
+  421: Reptile
+  422: Rhinoceros
+  423: Rifle
+  424: Ring binder
+  425: Rocket
+  426: Roller skates
+  427: Rose
+  428: Rugby ball
+  429: Ruler
+  430: Salad
+  431: Salt and pepper shakers
+  432: Sandal
+  433: Sandwich
+  434: Saucer
+  435: Saxophone
+  436: Scale
+  437: Scarf
+  438: Scissors
+  439: Scoreboard
+  440: Scorpion
+  441: Screwdriver
+  442: Sculpture
+  443: Sea lion
+  444: Sea turtle
+  445: Seafood
+  446: Seahorse
+  447: Seat belt
+  448: Segway
+  449: Serving tray
+  450: Sewing machine
+  451: Shark
+  452: Sheep
+  453: Shelf
+  454: Shellfish
+  455: Shirt
+  456: Shorts
+  457: Shotgun
+  458: Shower
+  459: Shrimp
+  460: Sink
+  461: Skateboard
+  462: Ski
+  463: Skirt
+  464: Skull
+  465: Skunk
+  466: Skyscraper
+  467: Slow cooker
+  468: Snack
+  469: Snail
+  470: Snake
+  471: Snowboard
+  472: Snowman
+  473: Snowmobile
+  474: Snowplow
+  475: Soap dispenser
+  476: Sock
+  477: Sofa bed
+  478: Sombrero
+  479: Sparrow
+  480: Spatula
+  481: Spice rack
+  482: Spider
+  483: Spoon
+  484: Sports equipment
+  485: Sports uniform
+  486: Squash (Plant)
+  487: Squid
+  488: Squirrel
+  489: Stairs
+  490: Stapler
+  491: Starfish
+  492: Stationary bicycle
+  493: Stethoscope
+  494: Stool
+  495: Stop sign
+  496: Strawberry
+  497: Street light
+  498: Stretcher
+  499: Studio couch
+  500: Submarine
+  501: Submarine sandwich
+  502: Suit
+  503: Suitcase
+  504: Sun hat
+  505: Sunglasses
+  506: Surfboard
+  507: Sushi
+  508: Swan
+  509: Swim cap
+  510: Swimming pool
+  511: Swimwear
+  512: Sword
+  513: Syringe
+  514: Table
+  515: Table tennis racket
+  516: Tablet computer
+  517: Tableware
+  518: Taco
+  519: Tank
+  520: Tap
+  521: Tart
+  522: Taxi
+  523: Tea
+  524: Teapot
+  525: Teddy bear
+  526: Telephone
+  527: Television
+  528: Tennis ball
+  529: Tennis racket
+  530: Tent
+  531: Tiara
+  532: Tick
+  533: Tie
+  534: Tiger
+  535: Tin can
+  536: Tire
+  537: Toaster
+  538: Toilet
+  539: Toilet paper
+  540: Tomato
+  541: Tool
+  542: Toothbrush
+  543: Torch
+  544: Tortoise
+  545: Towel
+  546: Tower
+  547: Toy
+  548: Traffic light
+  549: Traffic sign
+  550: Train
+  551: Training bench
+  552: Treadmill
+  553: Tree
+  554: Tree house
+  555: Tripod
+  556: Trombone
+  557: Trousers
+  558: Truck
+  559: Trumpet
+  560: Turkey
+  561: Turtle
+  562: Umbrella
+  563: Unicycle
+  564: Van
+  565: Vase
+  566: Vegetable
+  567: Vehicle
+  568: Vehicle registration plate
+  569: Violin
+  570: Volleyball (Ball)
+  571: Waffle
+  572: Waffle iron
+  573: Wall clock
+  574: Wardrobe
+  575: Washing machine
+  576: Waste container
+  577: Watch
+  578: Watercraft
+  579: Watermelon
+  580: Weapon
+  581: Whale
+  582: Wheel
+  583: Wheelchair
+  584: Whisk
+  585: Whiteboard
+  586: Willow
+  587: Window
+  588: Window blind
+  589: Wine
+  590: Wine glass
+  591: Wine rack
+  592: Winter melon
+  593: Wok
+  594: Woman
+  595: Wood-burning stove
+  596: Woodpecker
+  597: Worm
+  598: Wrench
+  599: Zebra
+  600: Zucchini

dataset/coco/download_coco.py

+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+import os.path as osp
+import logging
+# add python path of PadleDetection to sys.path
+parent_path = osp.abspath(osp.join(__file__, *(['..'] * 3)))
+if parent_path not in sys.path:
+    sys.path.append(parent_path)
+
+from ppdet.utils.download import download_dataset
+
+logging.basicConfig(level=logging.INFO)
+
+download_path = osp.split(osp.realpath(sys.argv[0]))[0]
+download_dataset(download_path, 'coco')

dataset/roadsign_voc/download_roadsign_voc.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+import os.path as osp
+import logging
+# add python path of PadleDetection to sys.path
+parent_path = osp.abspath(osp.join(__file__, *(['..'] * 3)))
+if parent_path not in sys.path:
+    sys.path.append(parent_path)
+
+from ppdet.utils.download import download_dataset
+
+logging.basicConfig(level=logging.INFO)
+
+download_path = osp.split(osp.realpath(sys.argv[0]))[0]
+download_dataset(download_path, 'roadsign_voc')

dataset/roadsign_voc/label_list.txt

+speedlimit
+crosswalk
+trafficlight
+stop
\ No newline at end of file

dataset/voc/create_list.py

+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+import os.path as osp
+import logging
+# add python path of PadleDetection to sys.path
+parent_path = osp.abspath(osp.join(__file__, *(['..'] * 3)))
+if parent_path not in sys.path:
+    sys.path.append(parent_path)
+
+from ppdet.utils.download import create_voc_list
+
+logging.basicConfig(level=logging.INFO)
+
+voc_path = osp.split(osp.realpath(sys.argv[0]))[0]
+create_voc_list(voc_path)

dataset/voc/download_voc.py

+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+import os.path as osp
+import logging
+# add python path of PadleDetection to sys.path
+parent_path = osp.abspath(osp.join(__file__, *(['..'] * 3)))
+if parent_path not in sys.path:
+    sys.path.append(parent_path)
+
+from ppdet.utils.download import download_dataset
+
+logging.basicConfig(level=logging.INFO)
+
+download_path = osp.split(osp.realpath(sys.argv[0]))[0]
+download_dataset(download_path, 'voc')

dataset/voc/label_list.txt

+aeroplane
+bicycle
+bird
+boat
+bottle
+bus
+car
+cat
+chair
+cow
+diningtable
+dog
+horse
+motorbike
+person
+pottedplant
+sheep
+sofa
+train
+tvmonitor

deploy/BENCHMARK_INFER.md

+# 推理Benchmark
+
+## 一、环境准备
+- 1、测试环境:
+  - CUDA 10.1
+  - CUDNN 7.6
+  - TensorRT-6.0.1
+  - PaddlePaddle v2.0.1
+  - GPU分别为: Tesla V100和GTX 1080Ti和Jetson AGX Xavier
+- 2、测试方式:
+  - 为了方便比较不同模型的推理速度，输入采用同样大小的图片，为 3x640x640，采用 `demo/000000014439_640x640.jpg` 图片。
+  - Batch Size=1
+  - 去掉前100轮warmup时间，测试100轮的平均时间，单位ms/image，包括网络计算时间、数据拷贝至CPU的时间。
+  - 采用Fluid C++预测引擎: 包含Fluid C++预测、Fluid-TensorRT预测，下面同时测试了Float32 (FP32) 和Float16 (FP16)的推理速度。
+
+**注意：**  TensorRT中固定尺寸和动态尺寸区别请参考文档[TENSOR教程](TENSOR_RT.md)。由于固定尺寸下对两阶段模型支持不完善，所以faster rcnn模型采用动态尺寸测试。固定尺寸和动态尺寸支持融合的OP不完全一样，因此同一个模型在固定尺寸和动态尺寸下测试的性能可能会有一点差异。
+
+## 二、推理速度
+
+### 1、Linux系统
+#### （1）Tesla V100
+
+| 模型                            | backbone     | 是否固定尺寸 | 入网尺寸     | paddle_inference | trt_fp32 | trt_fp16 |
+|-------------------------------|--------------|--------|----------|------------------|----------|----------|
+| Faster RCNN FPN            | ResNet50    | 否      | 640x640  | 27.99            | 26.15    | 21.92    |
+| Faster RCNN FPN   | ResNet50    | 否      | 800x1312 | 32.49            | 25.54    | 21.70    |
+| YOLOv3           | Mobilenet\_v1 | 是      | 608x608  | 9.74             | 8.61     | 6.28     |
+| YOLOv3              | Darknet53    | 是      | 608x608  | 17.84            | 15.43    | 9.86     |
+| PPYOLO              | ResNet50    | 是      | 608x608  | 20.77            | 18.40    | 13.53    |
+| SSD              | Mobilenet\_v1 | 是      | 300x300  | 5.17             | 4.43     | 4.29     |
+| TTFNet              | Darknet53    | 是      | 512x512  | 10.14            | 8.71     | 5.55     |
+| FCOS              | ResNet50    | 是      | 640x640  | 35.47            | 35.02    | 34.24    |
+
+
+#### （2）Jetson AGX Xavier
+
+| 模型                            | backbone     | 是否固定尺寸 | 入网尺寸     | paddle_inference | trt_fp32 | trt_fp16 |
+|-------------------------------|--------------|--------|----------|------------------|----------|----------|
+| Faster RCNN FPN            | ResNet50     | 否      | 640x640  | 169.45           | 158.92   | 119.25   |
+| Faster RCNN FPN  | ResNet50     | 否      | 800x1312 | 228.07           | 156.39   | 117.03   |
+| YOLOv3           | Mobilenet\_v1 | 是      | 608x608  | 48.76            | 43.83    | 18.41    |
+| YOLOv3              | Darknet53    | 是      | 608x608  | 121.61           | 110.30   | 42.38    |
+| PPYOLO              | ResNet50     | 是      | 608x608  | 111.80           | 99.40    | 48.05    |
+| SSD              | Mobilenet\_v1 | 是      | 300x300  | 10.52            | 8.84     | 8.77     |
+| TTFNet              | Darknet53    | 是      | 512x512  | 73.77            | 64.03    | 31.46    |
+| FCOS              | ResNet50     | 是      | 640x640  | 217.11           | 214.38   | 205.78   |
+
+### 2、Windows系统
+#### （1）GTX 1080Ti
+
+| 模型                            | backbone     | 是否固定尺寸 | 入网尺寸     | paddle_inference | trt_fp32 | trt_fp16 |
+|-------------------------------|--------------|--------|----------|------------------|----------|----------|
+| Faster RCNN FPN           | ResNet50     | 否      | 640x640  | 50.74            | 57.17    | 62.08    |
+| Faster RCNN FPN  | ResNet50     | 否      | 800x1312 | 50.31            | 57.61    | 62.05    |
+| YOLOv3           | Mobilenet\_v1 | 是      | 608x608  | 14.51            | 11.23    | 11.13    |
+| YOLOv3             | Darknet53    | 是      | 608x608  | 30.26            | 23.92    | 24.02    |
+| PPYOLO              | ResNet50     | 是      | 608x608  | 38.06            | 31.40    | 31.94    |
+| SSD              | Mobilenet\_v1 | 是      | 300x300  | 16.47            | 13.87    | 13.76    |
+| TTFNet              | Darknet53    | 是      | 512x512  | 21.83            | 17.14    | 17.09    |
+| FCOS              | ResNet50     | 是      | 640x640  | 71.88            | 69.93    | 69.52    |

deploy/BENCHMARK_INFER_en.md

+# Inference Benchmark
+
+## 一、Prepare the Environment
+- 1、Test Environment:
+  - CUDA 10.1
+  - CUDNN 7.6
+  - TensorRT-6.0.1
+  - PaddlePaddle v2.0.1
+  - The GPUS are Tesla V100 and GTX 1080 Ti and Jetson AGX Xavier
+- 2、Test Method:
+  - In order to compare the inference speed of different models, the input shape is 3x640x640, use `demo/000000014439_640x640.jpg`.
+  - Batch_size=1
+  - Delete the warmup time of the first 100 rounds and test the average time of 100 rounds in ms/image, including network calculation time and data copy time to CPU.
+  - Using Fluid C++ prediction engine: including Fluid C++ prediction, Fluid TensorRT prediction, the following test Float32 (FP32) and Float16 (FP16) inference speed.
+
+**Attention:**  For TensorRT, please refer to the [TENSOR tutorial](TENSOR_RT.md) for the difference between fixed and dynamic dimensions. Due to the imperfect support for the two-stage model under fixed size, dynamic size test was adopted for the Faster RCNN model. Fixed size and dynamic size do not support exactly the same OP for fusion, so the performance of the same model tested at fixed size and dynamic size may differ slightly.
+
+
+## 二、Inferring Speed
+
+### 1、Linux System
+#### （1）Tesla V100
+
+| Model           | backbone      | Fixed size or not | The net size | paddle_inference | trt_fp32 | trt_fp16 |
+| --------------- | ------------- | ----------------- | ------------ | ---------------- | -------- | -------- |
+| Faster RCNN FPN | ResNet50      | no                | 640x640      | 27.99            | 26.15    | 21.92    |
+| Faster RCNN FPN | ResNet50      | no                | 800x1312     | 32.49            | 25.54    | 21.70    |
+| YOLOv3          | Mobilenet\_v1 | yes               | 608x608      | 9.74             | 8.61     | 6.28     |
+| YOLOv3          | Darknet53     | yes               | 608x608      | 17.84            | 15.43    | 9.86     |
+| PPYOLO          | ResNet50      | yes               | 608x608      | 20.77            | 18.40    | 13.53    |
+| SSD             | Mobilenet\_v1 | yes               | 300x300      | 5.17             | 4.43     | 4.29     |
+| TTFNet          | Darknet53     | yes               | 512x512      | 10.14            | 8.71     | 5.55     |
+| FCOS            | ResNet50      | yes               | 640x640      | 35.47            | 35.02    | 34.24    |
+
+
+#### （2）Jetson AGX Xavier
+
+| Model           | backbone      | Fixed size or not | The net size | paddle_inference | trt_fp32 | trt_fp16 |
+| --------------- | ------------- | ----------------- | ------------ | ---------------- | -------- | -------- |
+| Faster RCNN FPN | ResNet50      | no                | 640x640      | 169.45           | 158.92   | 119.25   |
+| Faster RCNN FPN | ResNet50      | no                | 800x1312     | 228.07           | 156.39   | 117.03   |
+| YOLOv3          | Mobilenet\_v1 | yes               | 608x608      | 48.76            | 43.83    | 18.41    |
+| YOLOv3          | Darknet53     | yes               | 608x608      | 121.61           | 110.30   | 42.38    |
+| PPYOLO          | ResNet50      | yes               | 608x608      | 111.80           | 99.40    | 48.05    |
+| SSD             | Mobilenet\_v1 | yes               | 300x300      | 10.52            | 8.84     | 8.77     |
+| TTFNet          | Darknet53     | yes               | 512x512      | 73.77            | 64.03    | 31.46    |
+| FCOS            | ResNet50      | yes               | 640x640      | 217.11           | 214.38   | 205.78   |
+
+### 2、Windows System
+#### （1）GTX 1080Ti
+
+| Model           | backbone      | Fixed size or not | The net size | paddle_inference | trt_fp32 | trt_fp16 |
+| --------------- | ------------- | ----------------- | ------------ | ---------------- | -------- | -------- |
+| Faster RCNN FPN | ResNet50      | no                | 640x640      | 50.74            | 57.17    | 62.08    |
+| Faster RCNN FPN | ResNet50      | no                | 800x1312     | 50.31            | 57.61    | 62.05    |
+| YOLOv3          | Mobilenet\_v1 | yes               | 608x608      | 14.51            | 11.23    | 11.13    |
+| YOLOv3          | Darknet53     | yes               | 608x608      | 30.26            | 23.92    | 24.02    |
+| PPYOLO          | ResNet50      | yes               | 608x608      | 38.06            | 31.40    | 31.94    |
+| SSD             | Mobilenet\_v1 | yes               | 300x300      | 16.47            | 13.87    | 13.76    |
+| TTFNet          | Darknet53     | yes               | 512x512      | 21.83            | 17.14    | 17.09    |
+| FCOS            | ResNet50      | yes               | 640x640      | 71.88            | 69.93    | 69.52    |

deploy/EXPORT_MODEL.md

+# PaddleDetection模型导出教程
+
+## 一、模型导出
+本章节介绍如何使用`tools/export_model.py`脚本导出模型。
+### 1、导出模输入输出说明
+- 输入变量以及输入形状如下：
+
+  | 输入名称 | 输入形状 | 表示含义 |
+  | :---------: | ----------- | ---------- |
+  | image |  [None, 3, H, W] | 输入网络的图像，None表示batch维度，如果输入图像大小为变长，则H,W为None |
+  | im_shape | [None, 2] | 图像经过resize后的大小，表示为H,W, None表示batch维度 |
+  | scale_factor | [None, 2] | 输入图像大小比真实图像大小，表示为scale_y, scale_x |
+
+**注意**具体预处理方式可参考配置文件中TestReader部分。
+
+
+- PaddleDetection中动转静导出模型输出统一为：
+
+  - bbox, NMS的输出，形状为[N, 6], 其中N为预测框的个数，6为[class_id, score, x1, y1, x2, y2]。
+  - bbox\_num, 每张图片对应预测框的个数，例如batch_size为2，输出为[N1, N2], 表示第一张图包含N1个预测框，第二张图包含N2个预测框，并且预测框的总个数和NMS输出的第一维N相同
+  - mask，如果网络中包含mask，则会输出mask分支
+
+**注意**模型动转静导出不支持模型结构中包含numpy相关操作的情况。
+
+
+### 2、启动参数说明
+
+|      FLAG      |      用途      |    默认值    |                 备注                      |
+|:--------------:|:--------------:|:------------:|:-----------------------------------------:|
+|       -c       |  指定配置文件  |     None     |                                           |
+|  --output_dir  |  模型保存路径  |  `./output_inference`  |  模型默认保存在`output/配置文件名/`路径下 |
+
+### 3、使用示例
+
+使用训练得到的模型进行试用，脚本如下
+
+```bash
+# 导出YOLOv3模型
+python tools/export_model.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml --output_dir=./inference_model \
+ -o weights=weights/yolov3_darknet53_270e_coco.pdparams
+```
+
+预测模型会导出到`inference_model/yolov3_darknet53_270e_coco`目录下，分别为`infer_cfg.yml`, `model.pdiparams`,  `model.pdiparams.info`, `model.pdmodel`。
+
+
+### 4、设置导出模型的输入大小
+
+使用Fluid-TensorRT进行预测时，由于<=TensorRT 5.1的版本仅支持定长输入，保存模型的`data`层的图片大小需要和实际输入图片大小一致。而Fluid C++预测引擎没有此限制。设置TestReader中的`image_shape`可以修改保存模型中的输入图片大小。示例如下:
+
+```bash
+# 导出YOLOv3模型，输入是3x640x640
+python tools/export_model.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml --output_dir=./inference_model \
+ -o weights=weights/yolov3_darknet53_270e_coco.pdparams TestReader.inputs_def.image_shape=[3,640,640]
+```

deploy/EXPORT_MODEL_en.md

+# PaddleDetection Model Export Tutorial
+
+## 一、Model Export
+This section describes how to use the `tools/export_model.py` script to export models.
+### Export model input and output description
+- Input variables and input shapes are as follows:
+
+  |  Input Name  | Input Shape     | Meaning                                                                                                                   |
+  | :----------: | --------------- | ------------------------------------------------------------------------------------------------------------------------- |
+  |    image     | [None, 3, H, W] | Enter the network image. None indicates the Batch dimension. If the input image size is variable length, H and W are None |
+  |   im_shape   | [None, 2]       | The size of the image after resize is expressed as H,W, and None represents the Batch dimension                           |
+  | scale_factor | [None, 2]       | The input image size is larger than the real image size, denoted byscale_y, scale_x                                       |
+
+**Attention**For details about the preprocessing method, see the Test Reader section in the configuration file.
+
+
+-The output of the dynamic and static derived model in Paddle Detection is unified as follows:
+
+  - bbox, the output of NMS, in the shape of [N, 6], where N is the number of prediction boxes, and 6 is [class_id, score, x1, y1, x2, y2].
+  - bbox\_num, Each picture corresponds to the number of prediction boxes. For example, batch size is 2 and the output is [N1, N2], indicating that the first picture contains N1 prediction boxes and the second picture contains N2 prediction boxes, and the total number of prediction boxes is the same as the first dimension N output by NMS
+  - mask, If the network contains a mask, the mask branch is printed
+
+**Attention**The model-to-static export does not support cases where numpy operations are included in the model structure.
+
+
+### 2、Start Parameters
+
+|     FLAG     |               USE               |       DEFAULT        |                                 NOTE                                  |
+| :----------: | :-----------------------------: | :------------------: | :-------------------------------------------------------------------: |
+|      -c      | Specifying a configuration file |         None         |                                                                       |
+| --output_dir |         Model save path         | `./output_inference` | The model is saved in the `output/default_file_name/` path by default |
+
+### 3、Example
+
+Using the trained model for trial use, the script is as follows:
+
+```bash
+# The YOLOv3 model is exported
+python tools/export_model.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml --output_dir=./inference_model \
+ -o weights=weights/yolov3_darknet53_270e_coco.pdparams
+```
+The prediction model will be exported to the `inference_model/yolov3_darknet53_270e_coco` directory. `infer_cfg.yml`, `model.pdiparams`,  `model.pdiparams.info`, `model.pdmodel` respectively.
+
+
+### 4、Sets the input size of the export model
+When using Fluid TensorRT for prediction, since <= TensorRT 5.1 only supports fixed-length input, the image size of the `data` layer of the saved model needs to be the same as the actual input image size. Fluid C++ prediction engine does not have this limitation. Setting `image_shape` in Test Reader changes the size of the input image in the saved model. The following is an example:
+
+
+```bash
+#Export the YOLOv3 model with the input 3x640x640
+python tools/export_model.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml --output_dir=./inference_model \
+ -o weights=weights/yolov3_darknet53_270e_coco.pdparams TestReader.inputs_def.image_shape=[3,640,640]
+```

deploy/EXPORT_ONNX_MODEL.md

+# PaddleDetection模型导出为ONNX格式教程
+
+PaddleDetection模型支持保存为ONNX格式，目前测试支持的列表如下
+| 模型  | OP版本 | 备注 |
+| :---- | :----- | :--- |
+| YOLOv3 |  11   |  仅支持batch=1推理；模型导出需固定shape |
+| PP-YOLO | 11 | 仅支持batch=1推理；MatrixNMS将被转换NMS，精度略有变化；模型导出需固定shape |
+| PP-YOLOv2 | 11 | 仅支持batch=1推理；MatrixNMS将被转换NMS，精度略有变化；模型导出需固定shape |
+| PP-YOLO Tiny | 11 | 仅支持batch=1推理；模型导出需固定shape |
+| PP-YOLOE | 11 | 仅支持batch=1推理；模型导出需固定shape |
+| PP-PicoDet | 11 | 仅支持batch=1推理；模型导出需固定shape |
+| FCOS | 11 |仅支持batch=1推理 |
+| PAFNet | 11 |- |
+| TTFNet | 11 |-|
+| SSD | 11 |仅支持batch=1推理 |
+| PP-TinyPose | 11 | - |
+| Faster RCNN | 16 | 仅支持batch=1推理, 依赖0.9.7及以上版本|
+| Mask RCNN | 16 | 仅支持batch=1推理, 依赖0.9.7及以上版本|
+| Cascade RCNN | 16 | 仅支持batch=1推理, 依赖0.9.7及以上版本|
+| Cascade Mask RCNN | 16 | 仅支持batch=1推理, 依赖0.9.7及以上版本|
+
+保存ONNX的功能由[Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX)提供，如在转换中有相关问题反馈，可在Paddle2ONNX的Github项目中通过[ISSUE](https://github.com/PaddlePaddle/Paddle2ONNX/issues)与工程师交流。
+
+## 导出教程
+
+### 步骤一、导出PaddlePaddle部署模型
+
+
+导出步骤参考文档[PaddleDetection部署模型导出教程](./EXPORT_MODEL.md), 导出示例如下
+
+- 非RCNN系列模型, 以YOLOv3为例
+```
+cd PaddleDetection
+python tools/export_model.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml \
+                             -o weights=https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams \
+                             TestReader.inputs_def.image_shape=[3,608,608] \
+                             --output_dir inference_model
+```
+导出后的模型保存在`inference_model/yolov3_darknet53_270e_coco/`目录中，结构如下
+```
+yolov3_darknet
+  ├── infer_cfg.yml          # 模型配置文件信息
+  ├── model.pdiparams        # 静态图模型参数
+  ├── model.pdiparams.info   # 参数额外信息，一般无需关注
+  └── model.pdmodel          # 静态图模型文件
+```
+> 注意导出时的参数`TestReader.inputs_def.image_shape`，对于YOLO系列模型注意导出时指定该参数，否则无法转换成功
+
+- RCNN系列模型，以Faster RCNN为例
+
+RCNN系列模型导出ONNX模型时，需要去除模型中的控制流，因此需要额外添加`export_onnx=True` 字段
+```
+cd PaddleDetection
+python tools/export_model.py -c configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.yml \
+                             -o weights=https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_fpn_1x_coco.pdparams \
+                             export_onnx=True \
+                             --output_dir inference_model
+```
+
+导出的模型保存在`inference_model/faster_rcnn_r50_fpn_1x_coco/`目录中，结构如下
+```
+faster_rcnn_r50_fpn_1x_coco
+  ├── infer_cfg.yml          # 模型配置文件信息
+  ├── model.pdiparams        # 静态图模型参数
+  ├── model.pdiparams.info   # 参数额外信息，一般无需关注
+  └── model.pdmodel          # 静态图模型文件
+```
+
+### 步骤二、将部署模型转为ONNX格式
+安装Paddle2ONNX（高于或等于0.9.7版本)
+```
+pip install paddle2onnx
+```
+使用如下命令转换
+```
+# YOLOv3
+paddle2onnx --model_dir inference_model/yolov3_darknet53_270e_coco \
+            --model_filename model.pdmodel \
+            --params_filename model.pdiparams \
+            --opset_version 11 \
+            --save_file yolov3.onnx
+
+# Faster RCNN
+paddle2onnx --model_dir inference_model/faster_rcnn_r50_fpn_1x_coco \
+            --model_filename model.pdmodel \
+            --params_filename model.pdiparams \
+            --opset_version 16 \
+            --save_file faster_rcnn.onnx
+```
+转换后的模型即为在当前路径下的`yolov3.onnx`和`faster_rcnn.onnx`
+
+### 步骤三、使用onnxruntime进行推理
+安装onnxruntime
+```
+pip install onnxruntime
+```
+推理代码示例在[deploy/third_engine/onnx](./third_engine/onnx)下
+
+使用如下命令进行推理：
+```
+# YOLOv3
+python deploy/third_engine/onnx/infer.py
+            --infer_cfg inference_model/yolov3_darknet53_270e_coco/infer_cfg.yml \
+            --onnx_file yolov3.onnx \
+            --image_file demo/000000014439.jpg
+
+# Faster RCNN
+python deploy/third_engine/onnx/infer.py
+            --infer_cfg inference_model/faster_rcnn_r50_fpn_1x_coco/infer_cfg.yml \
+            --onnx_file faster_rcnn.onnx \
+            --image_file demo/000000014439.jpg
+```

deploy/EXPORT_ONNX_MODEL_en.md

+# PaddleDetection Model Export as ONNX Format Tutorial
+
+PaddleDetection Model support is saved in ONNX format and the list of current test support is as follows
+| Model  | OP Version | NOTE |
+| :---- | :----- | :--- |
+| YOLOv3 |  11   |  Only batch=1 inferring is supported. Model export needs fixed shape |
+| PP-YOLO | 11 | Only batch=1 inferring is supported. A MatrixNMS will be converted to an NMS with slightly different precision; Model export needs fixed shape |
+| PP-YOLOv2 | 11 | Only batch=1 inferring is supported. MatrixNMS will be converted to NMS with slightly different precision; Model export needs fixed shape |
+| PP-YOLO Tiny | 11 | Only batch=1 inferring is supported. Model export needs fixed shape |
+| PP-YOLOE | 11 | Only batch=1 inferring is supported. Model export needs fixed shape |
+| PP-PicoDet | 11 | Only batch=1 inferring is supported. Model export needs fixed shape |
+| FCOS | 11 |Only batch=1 inferring is supported |
+| PAFNet | 11 |- |
+| TTFNet | 11 |-|
+| SSD | 11 |Only batch=1 inferring is supported |
+| PP-TinyPose | 11 | - |
+| Faster RCNN | 16 | Only batch=1 inferring is supported, require paddle2onnx>=0.9.7|
+| Mask RCNN | 16 | Only batch=1 inferring is supported, require paddle2onnx>=0.9.7|
+| Cascade RCNN | 16 | Only batch=1 inferring is supported, require paddle2onnx>=0.9.7|
+| Cascade Mask RCNN | 16 | Only batch=1 inferring is supported, require paddle2onnx>=0.9.7|
+
+
+The function of saving ONNX is provided by [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX). If there is feedback on related problems during conversion, Communicate with engineers in Paddle2ONNX's Github project via [ISSUE](https://github.com/PaddlePaddle/Paddle2ONNX/issues).
+
+## Export Tutorial
+
+### Step 1. Export the Paddle deployment model
+Export procedure reference document[Tutorial on PaddleDetection deployment model export](./EXPORT_MODEL_en.md), for example:
+
+- Models except RCNN series, take YOLOv3 as example
+```
+cd PaddleDetection
+python tools/export_model.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml \
+                             -o weights=https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams \
+                             TestReader.inputs_def.image_shape=[3,608,608] \
+                             --output_dir inference_model
+```
+The derived models were saved in `inference_model/yolov3_darknet53_270e_coco/`, with the structure as follows
+```
+yolov3_darknet
+  ├── infer_cfg.yml          # Model configuration file information
+  ├── model.pdiparams        # Static diagram model parameters
+  ├── model.pdiparams.info   # Parameter Information is not required
+  └── model.pdmodel          # Static diagram model file
+```
+> check`TestReader.inputs_def.image_shape`, For YOLO series models, specify this parameter when exporting; otherwise, the conversion fails
+
+- RCNN series models, take Faster RCNN as example
+
+The conditional block needs to be removed in RCNN series when export ONNX model. Add `export_onnx=True` in command line
+```
+cd PaddleDetection
+python tools/export_model.py -c configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.yml \
+                             -o weights=https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_fpn_1x_coco.pdparams \
+                             export_onnx=True \
+                             --output_dir inference_model
+```
+The derived models were saved in `inference_model/faster_rcnn_r50_fpn_1x_coco/`, with the structure as follows
+```
+faster_rcnn_r50_fpn_1x_coco
+  ├── infer_cfg.yml          # Model configuration file information
+  ├── model.pdiparams        # Static diagram model parameters
+  ├── model.pdiparams.info   # Parameter Information is not required
+  └── model.pdmodel          # Static diagram model file
+```
+
+### Step 2. Convert the deployment model to ONNX format
+Install Paddle2ONNX (version 0.9.7 or higher)
+```
+pip install paddle2onnx
+```
+Use the following command to convert
+```
+# YOLOv3
+paddle2onnx --model_dir inference_model/yolov3_darknet53_270e_coco \
+            --model_filename model.pdmodel \
+            --params_filename model.pdiparams \
+            --opset_version 11 \
+            --save_file yolov3.onnx
+
+# Faster RCNN
+paddle2onnx --model_dir inference_model/faster_rcnn_r50_fpn_1x_coco \
+            --model_filename model.pdmodel \
+            --params_filename model.pdiparams \
+            --opset_version 16 \
+            --save_file faster_rcnn.onnx
+```
+The transformed model is under the current path`yolov3.onnx` and `faster_rcnn.onnx`
+
+### Step 3. Inference with onnxruntime
+Install onnxruntime
+```
+pip install onnxruntime
+```
+Inference code examples are in [deploy/third_engine/onnx](./third_engine/onnx)
+
+Use the following commands for inference：
+```
+# YOLOv3
+python deploy/third_engine/onnx/infer.py
+            --infer_cfg inference_model/yolov3_darknet53_270e_coco/infer_cfg.yml \
+            --onnx_file yolov3.onnx \
+            --image_file demo/000000014439.jpg
+
+# Faster RCNN
+python deploy/third_engine/onnx/infer.py
+            --infer_cfg inference_model/faster_rcnn_r50_fpn_1x_coco/infer_cfg.yml \
+            --onnx_file faster_rcnn.onnx \
+            --image_file demo/000000014439.jpg
+```