diff --git a/Samples/ResnetPicture/python/README.md b/Samples/ResnetPicture/python/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..85f74b8907e7b9297bec43f9b45c2c1bfdae9317
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+++ b/Samples/ResnetPicture/python/README.md
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+# 图片分类（ResNet50）
+
+#### 样例介绍
+
+基于PyTorch框架的ResNet50模型，对*.jpg图片分类，输出各图片Top5置信度的分类ID、分类名称。
+
+#### 样例下载
+
+可以使用以下两种方式下载，请选择其中一种进行源码准备。
+
+- 命令行方式下载（**下载时间较长，但步骤简单**）。
+
+  ```
+  # 登录开发板，HwHiAiUser用户命令行中执行以下命令下载源码仓。    
+  cd ${HOME}     
+  git clone https://gitee.com/ascend/EdgeAndRobotics.git
+  # 切换到样例目录
+  cd EdgeAndRobotics/Samples/ResnetPicture/pyACLLite/
+  ```
+
+- 压缩包方式下载（**下载时间较短，但步骤稍微复杂**）。
+
+  ```
+  # 1. 仓右上角选择 【克隆/下载】 下拉框并选择 【下载ZIP】。     
+  # 2. 将ZIP包上传到开发板的普通用户家目录中，【例如：${HOME}/EdgeAndRobotics-master.zip】。      
+  # 3. 开发环境中，执行以下命令，解压zip包。      
+  cd ${HOME} 
+  chmod +x EdgeAndRobotics-master.zip
+  unzip EdgeAndRobotics-master.zip
+  # 4. 切换到样例目录
+  cd EdgeAndRobotics-master/Samples/ResnetPicture/pyACLLite/
+  ```
+
+#### 执行准备
+
+1. 以HwHiAiUser用户登录开发板。
+
+2. 安装ACLLite库。
+
+   参考[ACLLite仓](https://gitee.com/ascend/ACLLite)安装ACLLite库python版。
+
+
+#### 运行样例
+
+1. 以HwHiAiUser用户登录开发板，切换到当前样例目录。
+2. 获取PyTorch框架的ResNet50模型（\*.onnx），并转换为昇腾AI处理器能识别的模型（\*.om）。
+   - 当设备内存**小于8G**时，可设置如下两个环境变量减少atc模型转换过程中使用的进程数，减小内存占用。
+     ```
+     export TE_PARALLEL_COMPILER=1
+     export MAX_COMPILE_CORE_NUMBER=1
+     ```
+   - 为了方便下载，在这里直接给出原始模型下载及模型转换命令,可以直接拷贝执行。
+     ```
+     cd model
+     wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/resnet50/resnet50.onnx
+     wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/resnet50/resnet50_DVPP/aipp.cfg
+     atc --model=resnet50.onnx --framework=5 --output=resnet50 --input_shape="actual_input_1:1,3,224,224"  --soc_version=Ascend310B4 --insert_op_conf=aipp.cfg
+     ```
+
+     atc命令中各参数的解释如下，详细约束说明请参见[《ATC模型转换指南》](https://hiascend.com/document/redirect/CannCommunityAtc)。
+
+     -   --model：ResNet-50网络的模型文件的路径。
+     -   --framework：原始框架类型。5表示ONNX。
+     -   --output：resnet50.om模型文件的路径。请注意，记录保存该om模型文件的路径，后续开发应用时需要使用。
+     -   --input\_shape：模型输入数据的shape。
+     -   --soc\_version：昇腾AI处理器的版本。
+
+
+3. 获取测试图片数据。
+
+   请从以下链接获取该样例的测试图片dog1\_1024\_683.jpg，放在data目录下。
+
+   ```
+   cd ../data 
+   wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/models/aclsample/dog1_1024_683.jpg
+   ```
+
+   **注：**若需更换测试图片，则需自行准备测试图片，并将测试图片放到data目录下。
+
+4. 运行样例。
+
+   执行以下脚本运行样例：
+
+   ```
+   bash sample_run.sh
+   ```
+
+   执行成功后，在屏幕上的关键提示信息示例如下，提示信息中的top1-5表示图片置信度的前5种类别、index表示类别标识、value表示该分类的最大置信度，class表示所属类别。这些值可能会根据版本、环境有所不同，请以实际情况为准：
+
+   ```
+    ======== top5 inference results: =============
+    label:162 confidence:0.849168 class:beagle
+    label:161 confidence:0.147563 class:basset,     basset hound
+    label:167 confidence:0.001627 class:English foxhound
+    label:166 confidence:0.001228 class:Walker hound,       Walker foxhound
+    label:163 confidence:0.000184 class:bloodhound, sleuthhound
+
+   ```
+
+#### 相关操作
+
+- 获取更多样例，请单击[Link](https://gitee.com/ascend/samples/tree/master/inference/modelInference)。
+- 获取在线视频课程，请单击[Link](https://www.hiascend.com/edu/courses?activeTab=%E5%BA%94%E7%94%A8%E5%BC%80%E5%8F%91)。
+- 获取学习文档，请单击[AscendCL python](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/80RC1alpha002/devguide/appdevg/aclpythondevg/aclpythondevg_0001.html)，查看最新版本的AscendCL推理应用开发指南。
+- 查模型的输入输出
+
+  可使用第三方工具Netron打开网络模型，查看模型输入或输出的数据类型、Shape，便于在分析应用开发场景时使用。
diff --git a/Samples/ResnetPicture/python/data/.keep b/Samples/ResnetPicture/python/data/.keep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Samples/ResnetPicture/python/model/.keep b/Samples/ResnetPicture/python/model/.keep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Samples/ResnetPicture/python/scripts/sample_run.sh b/Samples/ResnetPicture/python/scripts/sample_run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..16dd55bf25177ca47032d0f7ddf9d652fa9aa76c
--- /dev/null
+++ b/Samples/ResnetPicture/python/scripts/sample_run.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+ScriptPath="$( cd "$(dirname "$BASH_SOURCE")" ; pwd -P )"
+Src=${ScriptPath}/../src
+
+cd ${Src}
+echo "[INFO] The sample starts to run"
+running_command="python3 ResnetDVPP.py"
+${running_command}
+if [ $? -ne 0 ];then
+    echo "[INFO] The program runs failed"
+else
+    echo "[INFO] The program runs successfully"
+fi
diff --git a/Samples/ResnetPicture/python/src/ResnetDVPP.py b/Samples/ResnetPicture/python/src/ResnetDVPP.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d4ea6b6d4f3bc62f46a17b6a6e433d1f8b5a31f
--- /dev/null
+++ b/Samples/ResnetPicture/python/src/ResnetDVPP.py
@@ -0,0 +1,102 @@
+import os
+import numpy as np
+from acllite_imageproc import AclLiteImage
+from acllite_imageproc import AclLiteImageProc
+from acllite_model import AclLiteModel
+from acllite_resource import AclLiteResource
+from label import label
+
+class sample_resnet_dvpp(object):
+    def __init__(self, model_path):
+        self.model_path = model_path    # string
+        self.resource = None
+        self.dvpp = None
+        self.model = None
+        self.resized_image = None
+        self.result = None
+
+    def init_resource(self):
+        # init acl resource
+        self.resource = AclLiteResource()
+        self.resource.init()
+
+        # init dvpp resource
+        self.dvpp = AclLiteImageProc(self.resource)
+
+        # load model from file
+        self.model = AclLiteModel(self.model_path)
+
+    def process_input(self, input_path, model_width, model_height):
+        # read image from file
+        self.image = AclLiteImage(input_path)
+
+        # memory copy from host to dvpp
+        image_input = self.image.copy_to_dvpp()
+
+        # decode image from JPEGD format to YUV
+        yuv_image = self.dvpp.jpegd(image_input)
+
+        # execute resize
+        self.resized_image = self.dvpp.resize(yuv_image, model_width, model_height)
+
+    def inference(self):
+        # inference
+        self.result = self.model.execute([self.resized_image])
+
+    def get_result(self):
+        # do data processing with softmax
+        res = np.array(self.result).flatten()
+        res = np.exp(res)
+
+        # print top 5 classes
+        top_k = res.argsort()[-1:-6:-1]
+        total = np.sum(res)
+        print("======== top5 inference results: =============")
+        for j in top_k:
+            label_class = ""
+            label_string = label.get(str(j))
+            if label_string:
+                list_iterator = iter(range(len(label_string)))
+                for i in list_iterator:
+                    if i == len(label_string) - 1:
+                        label_class += label_string[i]
+                    else:
+                        label_class += label_string[i] + "," + "\t"
+                print("label:%d confidence:%f class:%s" % (j, res[j]/total, label_class))
+            else:
+                raise Exception("the key of label is not exist")
+
+    def release_resource(self):
+        del self.dvpp
+        del self.model
+        del self.resource
+
+if __name__ == '__main__':
+    model_width = 224
+    model_height = 224
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    model_path = os.path.join(current_dir, "../model/resnet50.om")
+    if not os.path.exists(model_path):
+        raise Exception("the model is not exist")
+
+    # read all file path of image
+    images_path = os.path.join(current_dir, "../data")
+    if not os.path.exists(images_path):
+        raise Exception("the directory is not exist")
+    all_path = []
+    for path in os.listdir(images_path):
+        if path != '.keep':
+            total_path = os.path.join(images_path, path)
+            all_path.append(total_path)
+    if len(all_path) == 0:
+        raise Exception("the directory is empty, please download image")
+
+    # inference
+    net = sample_resnet_dvpp(model_path)
+    net.init_resource()
+    for image in all_path:
+        net.process_input(image, model_width, model_height)
+        net.inference()
+        net.get_result()
+    print("*****run finish******")
+    net.release_resource()
diff --git a/Samples/ResnetPicture/python/src/label.py b/Samples/ResnetPicture/python/src/label.py
new file mode 100644
index 0000000000000000000000000000000000000000..76dd0c61476b8e057f03088bc47ee008842b54ba
--- /dev/null
+++ b/Samples/ResnetPicture/python/src/label.py
@@ -0,0 +1,1006 @@
+label = {
+    "0": ["tench", "Tinca tinca"],
+    "1": ["goldfish", "Carassius auratus"],
+    "2": ["great white shark", "white shark", "man-eater", "man-eating shark", "Carcharodon carcharias"],
+    "3": ["tiger shark", "Galeocerdo cuvieri"],
+    "4": ["hammerhead", "hammerhead shark"],
+    "5": ["electric ray", "crampfish", "numbfish", "torpedo"],
+    "6": ["stingray"],
+    "7": ["cock"],
+    "8": ["hen"],
+    "9": ["ostrich", "Struthio camelus"],
+    "10": ["brambling", "Fringilla montifringilla"],
+    "11": ["goldfinch", "Carduelis carduelis"],
+    "12": ["house finch", "linnet", "Carpodacus mexicanus"],
+    "13": ["junco", "snowbird"],
+    "14": ["indigo bunting", "indigo finch", "indigo bird", "Passerina cyanea"],
+    "15": ["robin", "American robin", "Turdus migratorius"],
+    "16": ["bulbul"],
+    "17": ["jay"],
+    "18": ["magpie"],
+    "19": ["chickadee"],
+    "20": ["water ouzel", "dipper"],
+    "21": ["kite"],
+    "22": ["bald eagle", "American eagle", "Haliaeetus leucocephalus"],
+    "23": ["vulture"],
+    "24": ["great grey owl", "great gray owl", "Strix nebulosa"],
+    "25": ["European fire salamander", "Salamandra salamandra"],
+    "26": ["common newt", "Triturus vulgaris"],
+    "27": ["eft"],
+    "28": ["spotted salamander", "Ambystoma maculatum"],
+    "29": ["axolotl", "mud puppy", "Ambystoma mexicanum"],
+    "30": ["bullfrog", "Rana catesbeiana"],
+    "31": ["tree frog", "tree-frog"],
+    "32": ["tailed frog", "bell toad", "ribbed toad", "tailed toad", "Ascaphus trui"],
+    "33": ["loggerhead", "loggerhead turtle", "Caretta caretta"],
+    "34": ["leatherback turtle", "leatherback", "leathery turtle", "Dermochelys coriacea"],
+    "35": ["mud turtle"],
+    "36": ["terrapin"],
+    "37": ["box turtle", "box tortoise"],
+    "38": ["banded gecko"],
+    "39": ["common iguana", "iguana", "Iguana iguana"],
+    "40": ["American chameleon", "anole", "Anolis carolinensis"],
+    "41": ["whiptail", "whiptail lizard"],
+    "42": ["agama"],
+    "43": ["frilled lizard", "Chlamydosaurus kingi"],
+    "44": ["alligator lizard"],
+    "45": ["Gila monster", "Heloderma suspectum"],
+    "46": ["green lizard", "Lacerta viridis"],
+    "47": ["African chameleon", "Chamaeleo chamaeleon"],
+    "48": ["Komodo dragon", "Komodo lizard", "dragon lizard", "giant lizard", "Varanus komodoensis"],
+    "49": ["African crocodile", "Nile crocodile", "Crocodylus niloticus"],
+    "50": ["American alligator", "Alligator mississipiensis"],
+    "51": ["triceratops"],
+    "52": ["thunder snake", "worm snake", "Carphophis amoenus"],
+    "53": ["ringneck snake", "ring-necked snake", "ring snake"],
+    "54": ["hognose snake", "puff adder", "sand viper"],
+    "55": ["green snake", "grass snake"],
+    "56": ["king snake", "kingsnake"],
+    "57": ["garter snake", "grass snake"],
+    "58": ["water snake"],
+    "59": ["vine snake"],
+    "60": ["night snake", "Hypsiglena torquata"],
+    "61": ["boa constrictor", "Constrictor constrictor"],
+    "62": ["rock python", "rock snake", "Python sebae"],
+    "63": ["Indian cobra", "Naja naja"],
+    "64": ["green mamba"],
+    "65": ["sea snake"],
+    "66": ["horned viper", "cerastes", "sand viper", "horned asp", "Cerastes cornutus"],
+    "67": ["diamondback", "diamondback rattlesnake", "Crotalus adamanteus"],
+    "68": ["sidewinder", "horned rattlesnake", "Crotalus cerastes"],
+    "69": ["trilobite"],
+    "70": ["harvestman", "daddy longlegs", "Phalangium opilio"],
+    "71": ["scorpion"],
+    "72": ["black and gold garden spider", "Argiope aurantia"],
+    "73": ["barn spider", "Araneus cavaticus"],
+    "74": ["garden spider", "Aranea diademata"],
+    "75": ["black widow", "Latrodectus mactans"],
+    "76": ["tarantula"],
+    "77": ["wolf spider", "hunting spider"],
+    "78": ["tick"],
+    "79": ["centipede"],
+    "80": ["black grouse"],
+    "81": ["ptarmigan"],
+    "82": ["ruffed grouse", "partridge", "Bonasa umbellus"],
+    "83": ["prairie chicken", "prairie grouse", "prairie fowl"],
+    "84": ["peacock"],
+    "85": ["quail"],
+    "86": ["partridge"],
+    "87": ["African grey", "African gray", "Psittacus erithacus"],
+    "88": ["macaw"],
+    "89": ["sulphur-crested cockatoo", "Kakatoe galerita", "Cacatua galerita"],
+    "90": ["lorikeet"],
+    "91": ["coucal"],
+    "92": ["bee eater"],
+    "93": ["hornbill"],
+    "94": ["hummingbird"],
+    "95": ["jacamar"],
+    "96": ["toucan"],
+    "97": ["drake"],
+    "98": ["red-breasted merganser", "Mergus serrator"],
+    "99": ["goose"],
+    "100": ["black swan", "Cygnus atratus"],
+    "101": ["tusker"],
+    "102": ["echidna", "spiny anteater", "anteater"],
+    "103": ["platypus", "duckbill", "duckbilled platypus", "duck-billed platypus", "Ornithorhynchus anatinus"],
+    "104": ["wallaby", "brush kangaroo"],
+    "105": ["koala", "koala bear", "kangaroo bear", "native bear", "Phascolarctos cinereus"],
+    "106": ["wombat"],
+    "107": ["jellyfish"],
+    "108": ["sea anemone", "anemone"],
+    "109": ["brain coral"],
+    "110": ["flatworm", "platyhelminth"],
+    "111": ["nematode", "nematode worm", "roundworm"],
+    "112": ["conch"],
+    "113": ["snail"],
+    "114": ["slug"],
+    "115": ["sea slug", "nudibranch"],
+    "116": ["chiton", "coat-of-mail shell", "sea cradle", "polyplacophore"],
+    "117": ["chambered nautilus", "pearly nautilus", "nautilus"],
+    "118": ["Dungeness crab", "Cancer magister"],
+    "119": ["rock crab", "Cancer irroratus"],
+    "120": ["fiddler crab"],
+    "121": ["king crab", "Alaska crab", "Alaskan king crab", "Alaska king crab", "Paralithodes camtschatica"],
+    "122": ["American lobster", "Northern lobster", "Maine lobster", "Homarus americanus"],
+    "123": ["spiny lobster", "langouste", "rock lobster", "crawfish", "crayfish", "sea crawfish"],
+    "124": ["crayfish", "crawfish", "crawdad", "crawdaddy"],
+    "125": ["hermit crab"],
+    "126": ["isopod"],
+    "127": ["white stork", "Ciconia ciconia"],
+    "128": ["black stork", "Ciconia nigra"],
+    "129": ["spoonbill"],
+    "130": ["flamingo"],
+    "131": ["little blue heron", "Egretta caerulea"],
+    "132": ["American egret", "great white heron", "Egretta albus"],
+    "133": ["bittern"],
+    "134": ["crane"],
+    "135": ["limpkin", "Aramus pictus"],
+    "136": ["European gallinule", "Porphyrio porphyrio"],
+    "137": ["American coot", "marsh hen", "mud hen", "water hen", "Fulica americana"],
+    "138": ["bustard"],
+    "139": ["ruddy turnstone", "Arenaria interpres"],
+    "140": ["red-backed sandpiper", "dunlin", "Erolia alpina"],
+    "141": ["redshank", "Tringa totanus"],
+    "142": ["dowitcher"],
+    "143": ["oystercatcher", "oyster catcher"],
+    "144": ["pelican"],
+    "145": ["king penguin", "Aptenodytes patagonica"],
+    "146": ["albatross", "mollymawk"],
+    "147": ["grey whale", "gray whale", "devilfish", "Eschrichtius gibbosus", "Eschrichtius robustus"],
+    "148": ["killer whale", "killer", "orca", "grampus", "sea wolf", "Orcinus orca"],
+    "149": ["dugong", "Dugong dugon"],
+    "150": ["sea lion"],
+    "151": ["Chihuahua"],
+    "152": ["Japanese spaniel"],
+    "153": ["Maltese dog", "Maltese terrier", "Maltese"],
+    "154": ["Pekinese", "Pekingese", "Peke"],
+    "155": ["Shih-Tzu"],
+    "156": ["Blenheim spaniel"],
+    "157": ["papillon"],
+    "158": ["toy terrier"],
+    "159": ["Rhodesian ridgeback"],
+    "160": ["Afghan hound", "Afghan"],
+    "161": ["basset", "basset hound"],
+    "162": ["beagle"],
+    "163": ["bloodhound", "sleuthhound"],
+    "164": ["bluetick"],
+    "165": ["black-and-tan coonhound"],
+    "166": ["Walker hound", "Walker foxhound"],
+    "167": ["English foxhound"],
+    "168": ["redbone"],
+    "169": ["borzoi", "Russian wolfhound"],
+    "170": ["Irish wolfhound"],
+    "171": ["Italian greyhound"],
+    "172": ["whippet"],
+    "173": ["Ibizan hound", "Ibizan Podenco"],
+    "174": ["Norwegian elkhound", "elkhound"],
+    "175": ["otterhound", "otter hound"],
+    "176": ["Saluki", "gazelle hound"],
+    "177": ["Scottish deerhound", "deerhound"],
+    "178": ["Weimaraner"],
+    "179": ["Staffordshire bullterrier", "Staffordshire bull terrier"],
+    "180": ["American Staffordshire terrier", "Staffordshire terrier", "American pit bull terrier", "pit bull terrier"],
+    "181": ["Bedlington terrier"],
+    "182": ["Border terrier"],
+    "183": ["Kerry blue terrier"],
+    "184": ["Irish terrier"],
+    "185": ["Norfolk terrier"],
+    "186": ["Norwich terrier"],
+    "187": ["Yorkshire terrier"],
+    "188": ["wire-haired fox terrier"],
+    "189": ["Lakeland terrier"],
+    "190": ["Sealyham terrier", "Sealyham"],
+    "191": ["Airedale", "Airedale terrier"],
+    "192": ["cairn", "cairn terrier"],
+    "193": ["Australian terrier"],
+    "194": ["Dandie Dinmont", "Dandie Dinmont terrier"],
+    "195": ["Boston bull", "Boston terrier"],
+    "196": ["miniature schnauzer"],
+    "197": ["giant schnauzer"],
+    "198": ["standard schnauzer"],
+    "199": ["Scotch terrier", "Scottish terrier", "Scottie"],
+    "200": ["Tibetan terrier", "chrysanthemum dog"],
+    "201": ["silky terrier", "Sydney silky"],
+    "202": ["soft-coated wheaten terrier"],
+    "203": ["West Highland white terrier"],
+    "204": ["Lhasa", "Lhasa apso"],
+    "205": ["flat-coated retriever"],
+    "206": ["curly-coated retriever"],
+    "207": ["golden retriever"],
+    "208": ["Labrador retriever"],
+    "209": ["Chesapeake Bay retriever"],
+    "210": ["German short-haired pointer"],
+    "211": ["vizsla", "Hungarian pointer"],
+    "212": ["English setter"],
+    "213": ["Irish setter", "red setter"],
+    "214": ["Gordon setter"],
+    "215": ["Brittany spaniel"],
+    "216": ["clumber", "clumber spaniel"],
+    "217": ["English springer", "English springer spaniel"],
+    "218": ["Welsh springer spaniel"],
+    "219": ["cocker spaniel", "English cocker spaniel", "cocker"],
+    "220": ["Sussex spaniel"],
+    "221": ["Irish water spaniel"],
+    "222": ["kuvasz"],
+    "223": ["schipperke"],
+    "224": ["groenendael"],
+    "225": ["malinois"],
+    "226": ["briard"],
+    "227": ["kelpie"],
+    "228": ["komondor"],
+    "229": ["Old English sheepdog", "bobtail"],
+    "230": ["Shetland sheepdog", "Shetland sheep dog", "Shetland"],
+    "231": ["collie"],
+    "232": ["Border collie"],
+    "233": ["Bouvier des Flandres", "Bouviers des Flandres"],
+    "234": ["Rottweiler"],
+    "235": ["German shepherd", "German shepherd dog", "German police dog", "alsatian"],
+    "236": ["Doberman", "Doberman pinscher"],
+    "237": ["miniature pinscher"],
+    "238": ["Greater Swiss Mountain dog"],
+    "239": ["Bernese mountain dog"],
+    "240": ["Appenzeller"],
+    "241": ["EntleBucher"],
+    "242": ["boxer"],
+    "243": ["bull mastiff"],
+    "244": ["Tibetan mastiff"],
+    "245": ["French bulldog"],
+    "246": ["Great Dane"],
+    "247": ["Saint Bernard", "St Bernard"],
+    "248": ["Eskimo dog", "husky"],
+    "249": ["malamute", "malemute", "Alaskan malamute"],
+    "250": ["Siberian husky"],
+    "251": ["dalmatian", "coach dog", "carriage dog"],
+    "252": ["affenpinscher", "monkey pinscher", "monkey dog"],
+    "253": ["basenji"],
+    "254": ["pug", "pug-dog"],
+    "255": ["Leonberg"],
+    "256": ["Newfoundland", "Newfoundland dog"],
+    "257": ["Great Pyrenees"],
+    "258": ["Samoyed", "Samoyede"],
+    "259": ["Pomeranian"],
+    "260": ["chow", "chow chow"],
+    "261": ["keeshond"],
+    "262": ["Brabancon griffon"],
+    "263": ["Pembroke", "Pembroke Welsh corgi"],
+    "264": ["Cardigan", "Cardigan Welsh corgi"],
+    "265": ["toy poodle"],
+    "266": ["miniature poodle"],
+    "267": ["standard poodle"],
+    "268": ["Mexican hairless"],
+    "269": ["timber wolf", "grey wolf", "gray wolf", "Canis lupus"],
+    "270": ["white wolf", "Arctic wolf", "Canis lupus tundrarum"],
+    "271": ["red wolf", "maned wolf", "Canis rufus", "Canis niger"],
+    "272": ["coyote", "prairie wolf", "brush wolf", "Canis latrans"],
+    "273": ["dingo", "warrigal", "warragal", "Canis dingo"],
+    "274": ["dhole", "Cuon alpinus"],
+    "275": ["African hunting dog", "hyena dog", "Cape hunting dog", "Lycaon pictus"],
+    "276": ["hyena", "hyaena"],
+    "277": ["red fox", "Vulpes vulpes"],
+    "278": ["kit fox", "Vulpes macrotis"],
+    "279": ["Arctic fox", "white fox", "Alopex lagopus"],
+    "280": ["grey fox", "gray fox", "Urocyon cinereoargenteus"],
+    "281": ["tabby", "tabby cat"],
+    "282": ["tiger cat"],
+    "283": ["Persian cat"],
+    "284": ["Siamese cat", "Siamese"],
+    "285": ["Egyptian cat"],
+    "286": ["cougar", "puma", "catamount", "mountain lion", "painter", "panther", "Felis concolor"],
+    "287": ["lynx", "catamount"],
+    "288": ["leopard", "Panthera pardus"],
+    "289": ["snow leopard", "ounce", "Panthera uncia"],
+    "290": ["jaguar", "panther", "Panthera onca", "Felis onca"],
+    "291": ["lion", "king of beasts", "Panthera leo"],
+    "292": ["tiger", "Panthera tigris"],
+    "293": ["cheetah", "chetah", "Acinonyx jubatus"],
+    "294": ["brown bear", "bruin", "Ursus arctos"],
+    "295": ["American black bear", "black bear", "Ursus americanus", "Euarctos americanus"],
+    "296": ["ice bear", "polar bear", "Ursus Maritimus", "Thalarctos maritimus"],
+    "297": ["sloth bear", "Melursus ursinus", "Ursus ursinus"],
+    "298": ["mongoose"],
+    "299": ["meerkat", "mierkat"],
+    "300": ["tiger beetle"],
+    "301": ["ladybug", "ladybeetle", "lady beetle", "ladybird", "ladybird beetle"],
+    "302": ["ground beetle", "carabid beetle"],
+    "303": ["long-horned beetle", "longicorn", "longicorn beetle"],
+    "304": ["leaf beetle", "chrysomelid"],
+    "305": ["dung beetle"],
+    "306": ["rhinoceros beetle"],
+    "307": ["weevil"],
+    "308": ["fly"],
+    "309": ["bee"],
+    "310": ["ant", "emmet", "pismire"],
+    "311": ["grasshopper", "hopper"],
+    "312": ["cricket"],
+    "313": ["walking stick", "walkingstick", "stick insect"],
+    "314": ["cockroach", "roach"],
+    "315": ["mantis", "mantid"],
+    "316": ["cicada", "cicala"],
+    "317": ["leafhopper"],
+    "318": ["lacewing", "lacewing fly"],
+    "319": ["dragonfly", "darning needle", "devil's darning needle", "sewing needle", \
+            "snake feeder", "snake doctor", "mosquito hawk", "skeeter hawk"],
+    "320": ["damselfly"],
+    "321": ["admiral"],
+    "322": ["ringlet", "ringlet butterfly"],
+    "323": ["monarch", "monarch butterfly", "milkweed butterfly", "Danaus plexippus"],
+    "324": ["cabbage butterfly"],
+    "325": ["sulphur butterfly", "sulfur butterfly"],
+    "326": ["lycaenid", "lycaenid butterfly"],
+    "327": ["starfish", "sea star"],
+    "328": ["sea urchin"],
+    "329": ["sea cucumber", "holothurian"],
+    "330": ["wood rabbit", "cottontail", "cottontail rabbit"],
+    "331": ["hare"],
+    "332": ["Angora", "Angora rabbit"],
+    "333": ["hamster"],
+    "334": ["porcupine", "hedgehog"],
+    "335": ["fox squirrel", "eastern fox squirrel", "Sciurus niger"],
+    "336": ["marmot"],
+    "337": ["beaver"],
+    "338": ["guinea pig", "Cavia cobaya"],
+    "339": ["sorrel"],
+    "340": ["zebra"],
+    "341": ["hog", "pig", "grunter", "squealer", "Sus scrofa"],
+    "342": ["wild boar", "boar", "Sus scrofa"],
+    "343": ["warthog"],
+    "344": ["hippopotamus", "hippo", "river horse", "Hippopotamus amphibius"],
+    "345": ["ox"],
+    "346": ["water buffalo", "water ox", "Asiatic buffalo", "Bubalus bubalis"],
+    "347": ["bison"],
+    "348": ["ram", "tup"],
+    "349": ["bighorn", "bighorn sheep", "cimarron", "Rocky Mountain bighorn", "Rocky Mountain sheep", \
+            "Ovis canadensis"],
+    "350": ["ibex", "Capra ibex"],
+    "351": ["hartebeest"],
+    "352": ["impala", "Aepyceros melampus"],
+    "353": ["gazelle"],
+    "354": ["Arabian camel", "dromedary", "Camelus dromedarius"],
+    "355": ["llama"],
+    "356": ["weasel"],
+    "357": ["mink"],
+    "358": ["polecat", "fitch", "foulmart", "foumart", "Mustela putorius"],
+    "359": ["black-footed ferret", "ferret", "Mustela nigripes"],
+    "360": ["otter"],
+    "361": ["skunk", "polecat", "wood pussy"],
+    "362": ["badger"],
+    "363": ["armadillo"],
+    "364": ["three-toed sloth", "ai", "Bradypus tridactylus"],
+    "365": ["orangutan", "orang", "orangutang", "Pongo pygmaeus"],
+    "366": ["gorilla", "Gorilla gorilla"],
+    "367": ["chimpanzee", "chimp", "Pan troglodytes"],
+    "368": ["gibbon", "Hylobates lar"],
+    "369": ["siamang", "Hylobates syndactylus", "Symphalangus syndactylus"],
+    "370": ["guenon", "guenon monkey"],
+    "371": ["patas", "hussar monkey", "Erythrocebus patas"],
+    "372": ["baboon"],
+    "373": ["macaque"],
+    "374": ["langur"],
+    "375": ["colobus", "colobus monkey"],
+    "376": ["proboscis monkey", "Nasalis larvatus"],
+    "377": ["marmoset"],
+    "378": ["capuchin", "ringtail", "Cebus capucinus"],
+    "379": ["howler monkey", "howler"],
+    "380": ["titi", "titi monkey"],
+    "381": ["spider monkey", "Ateles geoffroyi"],
+    "382": ["squirrel monkey", "Saimiri sciureus"],
+    "383": ["Madagascar cat", "ring-tailed lemur", "Lemur catta"],
+    "384": ["indri", "indris", "Indri indri", "Indri brevicaudatus"],
+    "385": ["Indian elephant", "Elephas maximus"],
+    "386": ["African elephant", "Loxodonta africana"],
+    "387": ["lesser panda", "red panda", "panda", "bear cat", "cat bear", "Ailurus fulgens"],
+    "388": ["giant panda", "panda", "panda bear", "coon bear", "Ailuropoda melanoleuca"],
+    "389": ["barracouta", "snoek"],
+    "390": ["eel"],
+    "391": ["coho", "cohoe", "coho salmon", "blue jack", "silver salmon", "Oncorhynchus kisutch"],
+    "392": ["rock beauty", "Holocanthus tricolor"],
+    "393": ["anemone fish"],
+    "394": ["sturgeon"],
+    "395": ["gar", "garfish", "garpike", "billfish", "Lepisosteus osseus"],
+    "396": ["lionfish"],
+    "397": ["puffer", "pufferfish", "blowfish", "globefish"],
+    "398": ["abacus"],
+    "399": ["abaya"],
+    "400": ["academic gown", "academic robe", "judge's robe"],
+    "401": ["accordion", "piano accordion", "squeeze box"],
+    "402": ["acoustic guitar"],
+    "403": ["aircraft carrier", "carrier", "flattop", "attack aircraft carrier"],
+    "404": ["airliner"],
+    "405": ["airship", "dirigible"],
+    "406": ["altar"],
+    "407": ["ambulance"],
+    "408": ["amphibian", "amphibious vehicle"],
+    "409": ["analog clock"],
+    "410": ["apiary", "bee house"],
+    "411": ["apron"],
+    "412": ["ashcan", "trash can", "garbage can", "wastebin", "ash bin", "ash-bin", "ashbin",\
+            "dustbin", "trash barrel", "trash bin"],
+    "413": ["assault rifle", "assault gun"],
+    "414": ["backpack", "back pack", "knapsack", "packsack", "rucksack", "haversack"],
+    "415": ["bakery", "bakeshop", "bakehouse"],
+    "416": ["balance beam", "beam"],
+    "417": ["balloon"],
+    "418": ["ballpoint", "ballpoint pen", "ballpen", "Biro"],
+    "419": ["Band Aid"],
+    "420": ["banjo"],
+    "421": ["bannister", "banister", "balustrade", "balusters", "handrail"],
+    "422": ["barbell"],
+    "423": ["barber chair"],
+    "424": ["barbershop"],
+    "425": ["barn"],
+    "426": ["barometer"],
+    "427": ["barrel", "cask"],
+    "428": ["barrow", "garden cart", "lawn cart", "wheelbarrow"],
+    "429": ["baseball"],
+    "430": ["basketball"],
+    "431": ["bassinet"],
+    "432": ["bassoon"],
+    "433": ["bathing cap", "swimming cap"],
+    "434": ["bath towel"],
+    "435": ["bathtub", "bathing tub", "bath", "tub"],
+    "436": ["beach wagon", "station wagon", "wagon", "estate car", "beach waggon", "station waggon", "waggon"],
+    "437": ["beacon", "lighthouse", "beacon light", "pharos"],
+    "438": ["beaker"],
+    "439": ["bearskin", "busby", "shako"],
+    "440": ["beer bottle"],
+    "441": ["beer glass"],
+    "442": ["bell cote", "bell cot"],
+    "443": ["bib"],
+    "444": ["bicycle-built-for-two", "tandem bicycle", "tandem"],
+    "445": ["bikini", "two-piece"],
+    "446": ["binder", "ring-binder"],
+    "447": ["binoculars", "field glasses", "opera glasses"],
+    "448": ["birdhouse"],
+    "449": ["boathouse"],
+    "450": ["bobsled", "bobsleigh", "bob"],
+    "451": ["bolo tie", "bolo", "bola tie", "bola"],
+    "452": ["bonnet", "poke bonnet"],
+    "453": ["bookcase"],
+    "454": ["bookshop", "bookstore", "bookstall"],
+    "455": ["bottlecap"],
+    "456": ["bow"],
+    "457": ["bow tie", "bow-tie", "bowtie"],
+    "458": ["brass", "memorial tablet", "plaque"],
+    "459": ["brassiere", "bra", "bandeau"],
+    "460": ["breakwater", "groin", "groyne", "mole", "bulwark", "seawall", "jetty"],
+    "461": ["breastplate", "aegis", "egis"],
+    "462": ["broom"],
+    "463": ["bucket", "pail"],
+    "464": ["buckle"],
+    "465": ["bulletproof vest"],
+    "466": ["bullet train", "bullet"],
+    "467": ["butcher shop", "meat market"],
+    "468": ["cab", "hack", "taxi", "taxicab"],
+    "469": ["caldron", "cauldron"],
+    "470": ["candle", "taper", "wax light"],
+    "471": ["cannon"],
+    "472": ["canoe"],
+    "473": ["can opener", "tin opener"],
+    "474": ["cardigan"],
+    "475": ["car mirror"],
+    "476": ["carousel", "carrousel", "merry-go-round", "roundabout", "whirligig"],
+    "477": ["carpenter's kit", "tool kit"],
+    "478": ["carton"],
+    "479": ["car wheel"],
+    "480": ["cash machine", "cash dispenser", "automated teller machine", "automatic teller machine",\
+            "automated teller", "automatic teller", "ATM"],
+    "481": ["cassette"],
+    "482": ["cassette player"],
+    "483": ["castle"],
+    "484": ["catamaran"],
+    "485": ["CD player"],
+    "486": ["cello", "violoncello"],
+    "487": ["cellular telephone", "cellular phone", "cellphone", "cell", "mobile phone"],
+    "488": ["chain"],
+    "489": ["chainlink fence"],
+    "490": ["chain mail", "ring mail", "mail", "chain armor", "chain armour", "ring armor", "ring armour"],
+    "491": ["chain saw", "chainsaw"],
+    "492": ["chest"],
+    "493": ["chiffonier", "commode"],
+    "494": ["chime", "bell", "gong"],
+    "495": ["china cabinet", "china closet"],
+    "496": ["Christmas stocking"],
+    "497": ["church", "church building"],
+    "498": ["cinema", "movie theater", "movie theatre", "movie house", "picture palace"],
+    "499": ["cleaver", "meat cleaver", "chopper"],
+    "500": ["cliff dwelling"],
+    "501": ["cloak"],
+    "502": ["clog", "geta", "patten", "sabot"],
+    "503": ["cocktail shaker"],
+    "504": ["coffee mug"],
+    "505": ["coffeepot"],
+    "506": ["coil", "spiral", "volute", "whorl", "helix"],
+    "507": ["combination lock"],
+    "508": ["computer keyboard", "keypad"],
+    "509": ["confectionery", "confectionary", "candy store"],
+    "510": ["container ship", "containership", "container vessel"],
+    "511": ["convertible"],
+    "512": ["corkscrew", "bottle screw"],
+    "513": ["cornet", "horn", "trumpet", "trump"],
+    "514": ["cowboy boot"],
+    "515": ["cowboy hat", "ten-gallon hat"],
+    "516": ["cradle"],
+    "517": ["crane"],
+    "518": ["crash helmet"],
+    "519": ["crate"],
+    "520": ["crib", "cot"],
+    "521": ["Crock Pot"],
+    "522": ["croquet ball"],
+    "523": ["crutch"],
+    "524": ["cuirass"],
+    "525": ["dam", "dike", "dyke"],
+    "526": ["desk"],
+    "527": ["desktop computer"],
+    "528": ["dial telephone", "dial phone"],
+    "529": ["diaper", "nappy", "napkin"],
+    "530": ["digital clock"],
+    "531": ["digital watch"],
+    "532": ["dining table", "board"],
+    "533": ["dishrag", "dishcloth"],
+    "534": ["dishwasher", "dish washer", "dishwashing machine"],
+    "535": ["disk brake", "disc brake"],
+    "536": ["dock", "dockage", "docking facility"],
+    "537": ["dogsled", "dog sled", "dog sleigh"],
+    "538": ["dome"],
+    "539": ["doormat", "welcome mat"],
+    "540": ["drilling platform", "offshore rig"],
+    "541": ["drum", "membranophone", "tympan"],
+    "542": ["drumstick"],
+    "543": ["dumbbell"],
+    "544": ["Dutch oven"],
+    "545": ["electric fan", "blower"],
+    "546": ["electric guitar"],
+    "547": ["electric locomotive"],
+    "548": ["entertainment center"],
+    "549": ["envelope"],
+    "550": ["espresso maker"],
+    "551": ["face powder"],
+    "552": ["feather boa", "boa"],
+    "553": ["file", "file cabinet", "filing cabinet"],
+    "554": ["fireboat"],
+    "555": ["fire engine", "fire truck"],
+    "556": ["fire screen", "fireguard"],
+    "557": ["flagpole", "flagstaff"],
+    "558": ["flute", "transverse flute"],
+    "559": ["folding chair"],
+    "560": ["football helmet"],
+    "561": ["forklift"],
+    "562": ["fountain"],
+    "563": ["fountain pen"],
+    "564": ["four-poster"],
+    "565": ["freight car"],
+    "566": ["French horn", "horn"],
+    "567": ["frying pan", "frypan", "skillet"],
+    "568": ["fur coat"],
+    "569": ["garbage truck", "dustcart"],
+    "570": ["gasmask", "respirator", "gas helmet"],
+    "571": ["gas pump", "gasoline pump", "petrol pump", "island dispenser"],
+    "572": ["goblet"],
+    "573": ["go-kart"],
+    "574": ["golf ball"],
+    "575": ["golfcart", "golf cart"],
+    "576": ["gondola"],
+    "577": ["gong", "tam-tam"],
+    "578": ["gown"],
+    "579": ["grand piano", "grand"],
+    "580": ["greenhouse", "nursery", "glasshouse"],
+    "581": ["grille", "radiator grille"],
+    "582": ["grocery store", "grocery", "food market", "market"],
+    "583": ["guillotine"],
+    "584": ["hair slide"],
+    "585": ["hair spray"],
+    "586": ["half track"],
+    "587": ["hammer"],
+    "588": ["hamper"],
+    "589": ["hand blower", "blow dryer", "blow drier", "hair dryer", "hair drier"],
+    "590": ["hand-held computer", "hand-held microcomputer"],
+    "591": ["handkerchief", "hankie", "hanky", "hankey"],
+    "592": ["hard disc", "hard disk", "fixed disk"],
+    "593": ["harmonica", "mouth organ", "harp", "mouth harp"],
+    "594": ["harp"],
+    "595": ["harvester", "reaper"],
+    "596": ["hatchet"],
+    "597": ["holster"],
+    "598": ["home theater", "home theatre"],
+    "599": ["honeycomb"],
+    "600": ["hook", "claw"],
+    "601": ["hoopskirt", "crinoline"],
+    "602": ["horizontal bar", "high bar"],
+    "603": ["horse cart", "horse-cart"],
+    "604": ["hourglass"],
+    "605": ["iPod"],
+    "606": ["iron", "smoothing iron"],
+    "607": ["jack-o'-lantern"],
+    "608": ["jean", "blue jean", "denim"],
+    "609": ["jeep", "landrover"],
+    "610": ["jersey", "T-shirt", "tee shirt"],
+    "611": ["jigsaw puzzle"],
+    "612": ["jinrikisha", "ricksha", "rickshaw"],
+    "613": ["joystick"],
+    "614": ["kimono"],
+    "615": ["knee pad"],
+    "616": ["knot"],
+    "617": ["lab coat", "laboratory coat"],
+    "618": ["ladle"],
+    "619": ["lampshade", "lamp shade"],
+    "620": ["laptop", "laptop computer"],
+    "621": ["lawn mower", "mower"],
+    "622": ["lens cap", "lens cover"],
+    "623": ["letter opener", "paper knife", "paperknife"],
+    "624": ["library"],
+    "625": ["lifeboat"],
+    "626": ["lighter", "light", "igniter", "ignitor"],
+    "627": ["limousine", "limo"],
+    "628": ["liner", "ocean liner"],
+    "629": ["lipstick", "lip rouge"],
+    "630": ["Loafer"],
+    "631": ["lotion"],
+    "632": ["loudspeaker", "speaker", "speaker unit", "loudspeaker system", "speaker system"],
+    "633": ["loupe", "jeweler's loupe"],
+    "634": ["lumbermill", "sawmill"],
+    "635": ["magnetic compass"],
+    "636": ["mailbag", "postbag"],
+    "637": ["mailbox", "letter box"],
+    "638": ["maillot"],
+    "639": ["maillot", "tank suit"],
+    "640": ["manhole cover"],
+    "641": ["maraca"],
+    "642": ["marimba", "xylophone"],
+    "643": ["mask"],
+    "644": ["matchstick"],
+    "645": ["maypole"],
+    "646": ["maze", "labyrinth"],
+    "647": ["measuring cup"],
+    "648": ["medicine chest", "medicine cabinet"],
+    "649": ["megalith", "megalithic structure"],
+    "650": ["microphone", "mike"],
+    "651": ["microwave", "microwave oven"],
+    "652": ["military uniform"],
+    "653": ["milk can"],
+    "654": ["minibus"],
+    "655": ["miniskirt", "mini"],
+    "656": ["minivan"],
+    "657": ["missile"],
+    "658": ["mitten"],
+    "659": ["mixing bowl"],
+    "660": ["mobile home", "manufactured home"],
+    "661": ["Model T"],
+    "662": ["modem"],
+    "663": ["monastery"],
+    "664": ["monitor"],
+    "665": ["moped"],
+    "666": ["mortar"],
+    "667": ["mortarboard"],
+    "668": ["mosque"],
+    "669": ["mosquito net"],
+    "670": ["motor scooter", "scooter"],
+    "671": ["mountain bike", "all-terrain bike", "off-roader"],
+    "672": ["mountain tent"],
+    "673": ["mouse", "computer mouse"],
+    "674": ["mousetrap"],
+    "675": ["moving van"],
+    "676": ["muzzle"],
+    "677": ["nail"],
+    "678": ["neck brace"],
+    "679": ["necklace"],
+    "680": ["nipple"],
+    "681": ["notebook", "notebook computer"],
+    "682": ["obelisk"],
+    "683": ["oboe", "hautboy", "hautbois"],
+    "684": ["ocarina", "sweet potato"],
+    "685": ["odometer", "hodometer", "mileometer", "milometer"],
+    "686": ["oil filter"],
+    "687": ["organ", "pipe organ"],
+    "688": ["oscilloscope", "scope", "cathode-ray oscilloscope", "CRO"],
+    "689": ["overskirt"],
+    "690": ["oxcart"],
+    "691": ["oxygen mask"],
+    "692": ["packet"],
+    "693": ["paddle", "boat paddle"],
+    "694": ["paddlewheel", "paddle wheel"],
+    "695": ["padlock"],
+    "696": ["paintbrush"],
+    "697": ["pajama", "pyjama", "pj's", "jammies"],
+    "698": ["palace"],
+    "699": ["panpipe", "pandean pipe", "syrinx"],
+    "700": ["paper towel"],
+    "701": ["parachute", "chute"],
+    "702": ["parallel bars", "bars"],
+    "703": ["park bench"],
+    "704": ["parking meter"],
+    "705": ["passenger car", "coach", "carriage"],
+    "706": ["patio", "terrace"],
+    "707": ["pay-phone", "pay-station"],
+    "708": ["pedestal", "plinth", "footstall"],
+    "709": ["pencil box", "pencil case"],
+    "710": ["pencil sharpener"],
+    "711": ["perfume", "essence"],
+    "712": ["Petri dish"],
+    "713": ["photocopier"],
+    "714": ["pick", "plectrum", "plectron"],
+    "715": ["pickelhaube"],
+    "716": ["picket fence", "paling"],
+    "717": ["pickup", "pickup truck"],
+    "718": ["pier"],
+    "719": ["piggy bank", "penny bank"],
+    "720": ["pill bottle"],
+    "721": ["pillow"],
+    "722": ["ping-pong ball"],
+    "723": ["pinwheel"],
+    "724": ["pirate", "pirate ship"],
+    "725": ["pitcher", "ewer"],
+    "726": ["plane", "carpenter's plane", "woodworking plane"],
+    "727": ["planetarium"],
+    "728": ["plastic bag"],
+    "729": ["plate rack"],
+    "730": ["plow", "plough"],
+    "731": ["plunger", "plumber's helper"],
+    "732": ["Polaroid camera", "Polaroid Land camera"],
+    "733": ["pole"],
+    "734": ["police van", "police wagon", "paddy wagon", "patrol wagon", "wagon", "black Maria"],
+    "735": ["poncho"],
+    "736": ["pool table", "billiard table", "snooker table"],
+    "737": ["pop bottle", "soda bottle"],
+    "738": ["pot", "flowerpot"],
+    "739": ["potter's wheel"],
+    "740": ["power drill"],
+    "741": ["prayer rug", "prayer mat"],
+    "742": ["printer"],
+    "743": ["prison", "prison house"],
+    "744": ["projectile", "missile"],
+    "745": ["projector"],
+    "746": ["puck", "hockey puck"],
+    "747": ["punching bag", "punch bag", "punching ball", "punchball"],
+    "748": ["purse"],
+    "749": ["quill", "quill pen"],
+    "750": ["quilt", "comforter", "comfort", "puff"],
+    "751": ["racer", "race car", "racing car"],
+    "752": ["racket", "racquet"],
+    "753": ["radiator"],
+    "754": ["radio", "wireless"],
+    "755": ["radio telescope", "radio reflector"],
+    "756": ["rain barrel"],
+    "757": ["recreational vehicle", "RV", "R.V."],
+    "758": ["reel"],
+    "759": ["reflex camera"],
+    "760": ["refrigerator", "icebox"],
+    "761": ["remote control", "remote"],
+    "762": ["restaurant", "eating house", "eating place", "eatery"],
+    "763": ["revolver", "six-gun", "six-shooter"],
+    "764": ["rifle"],
+    "765": ["rocking chair", "rocker"],
+    "766": ["rotisserie"],
+    "767": ["rubber eraser", "rubber", "pencil eraser"],
+    "768": ["rugby ball"],
+    "769": ["rule", "ruler"],
+    "770": ["running shoe"],
+    "771": ["safe"],
+    "772": ["safety pin"],
+    "773": ["saltshaker", "salt shaker"],
+    "774": ["sandal"],
+    "775": ["sarong"],
+    "776": ["sax", "saxophone"],
+    "777": ["scabbard"],
+    "778": ["scale", "weighing machine"],
+    "779": ["school bus"],
+    "780": ["schooner"],
+    "781": ["scoreboard"],
+    "782": ["screen", "CRT screen"],
+    "783": ["screw"],
+    "784": ["screwdriver"],
+    "785": ["seat belt", "seatbelt"],
+    "786": ["sewing machine"],
+    "787": ["shield", "buckler"],
+    "788": ["shoe shop", "shoe-shop", "shoe store"],
+    "789": ["shoji"],
+    "790": ["shopping basket"],
+    "791": ["shopping cart"],
+    "792": ["shovel"],
+    "793": ["shower cap"],
+    "794": ["shower curtain"],
+    "795": ["ski"],
+    "796": ["ski mask"],
+    "797": ["sleeping bag"],
+    "798": ["slide rule", "slipstick"],
+    "799": ["sliding door"],
+    "800": ["slot", "one-armed bandit"],
+    "801": ["snorkel"],
+    "802": ["snowmobile"],
+    "803": ["snowplow", "snowplough"],
+    "804": ["soap dispenser"],
+    "805": ["soccer ball"],
+    "806": ["sock"],
+    "807": ["solar dish", "solar collector", "solar furnace"],
+    "808": ["sombrero"],
+    "809": ["soup bowl"],
+    "810": ["space bar"],
+    "811": ["space heater"],
+    "812": ["space shuttle"],
+    "813": ["spatula"],
+    "814": ["speedboat"],
+    "815": ["spider web", "spider's web"],
+    "816": ["spindle"],
+    "817": ["sports car", "sport car"],
+    "818": ["spotlight", "spot"],
+    "819": ["stage"],
+    "820": ["steam locomotive"],
+    "821": ["steel arch bridge"],
+    "822": ["steel drum"],
+    "823": ["stethoscope"],
+    "824": ["stole"],
+    "825": ["stone wall"],
+    "826": ["stopwatch", "stop watch"],
+    "827": ["stove"],
+    "828": ["strainer"],
+    "829": ["streetcar", "tram", "tramcar", "trolley", "trolley car"],
+    "830": ["stretcher"],
+    "831": ["studio couch", "day bed"],
+    "832": ["stupa", "tope"],
+    "833": ["submarine", "pigboat", "sub", "U-boat"],
+    "834": ["suit", "suit of clothes"],
+    "835": ["sundial"],
+    "836": ["sunglass"],
+    "837": ["sunglasses", "dark glasses", "shades"],
+    "838": ["sunscreen", "sunblock", "sun blocker"],
+    "839": ["suspension bridge"],
+    "840": ["swab", "swob", "mop"],
+    "841": ["sweatshirt"],
+    "842": ["swimming trunks", "bathing trunks"],
+    "843": ["swing"],
+    "844": ["switch", "electric switch", "electrical switch"],
+    "845": ["syringe"],
+    "846": ["table lamp"],
+    "847": ["tank", "army tank", "armored combat vehicle", "armoured combat vehicle"],
+    "848": ["tape player"],
+    "849": ["teapot"],
+    "850": ["teddy", "teddy bear"],
+    "851": ["television", "television system"],
+    "852": ["tennis ball"],
+    "853": ["thatch", "thatched roof"],
+    "854": ["theater curtain", "theatre curtain"],
+    "855": ["thimble"],
+    "856": ["thresher", "thrasher", "threshing machine"],
+    "857": ["throne"],
+    "858": ["tile roof"],
+    "859": ["toaster"],
+    "860": ["tobacco shop", "tobacconist shop", "tobacconist"],
+    "861": ["toilet seat"],
+    "862": ["torch"],
+    "863": ["totem pole"],
+    "864": ["tow truck", "tow car", "wrecker"],
+    "865": ["toyshop"],
+    "866": ["tractor"],
+    "867": ["trailer truck", "tractor trailer", "trucking rig", "rig", "articulated lorry", "semi"],
+    "868": ["tray"],
+    "869": ["trench coat"],
+    "870": ["tricycle", "trike", "velocipede"],
+    "871": ["trimaran"],
+    "872": ["tripod"],
+    "873": ["triumphal arch"],
+    "874": ["trolleybus", "trolley coach", "trackless trolley"],
+    "875": ["trombone"],
+    "876": ["tub", "vat"],
+    "877": ["turnstile"],
+    "878": ["typewriter keyboard"],
+    "879": ["umbrella"],
+    "880": ["unicycle", "monocycle"],
+    "881": ["upright", "upright piano"],
+    "882": ["vacuum", "vacuum cleaner"],
+    "883": ["vase"],
+    "884": ["vault"],
+    "885": ["velvet"],
+    "886": ["vending machine"],
+    "887": ["vestment"],
+    "888": ["viaduct"],
+    "889": ["violin", "fiddle"],
+    "890": ["volleyball"],
+    "891": ["waffle iron"],
+    "892": ["wall clock"],
+    "893": ["wallet", "billfold", "notecase", "pocketbook"],
+    "894": ["wardrobe", "closet", "press"],
+    "895": ["warplane", "military plane"],
+    "896": ["washbasin", "handbasin", "washbowl", "lavabo", "wash-hand basin"],
+    "897": ["washer", "automatic washer", "washing machine"],
+    "898": ["water bottle"],
+    "899": ["water jug"],
+    "900": ["water tower"],
+    "901": ["whiskey jug"],
+    "902": ["whistle"],
+    "903": ["wig"],
+    "904": ["window screen"],
+    "905": ["window shade"],
+    "906": ["Windsor tie"],
+    "907": ["wine bottle"],
+    "908": ["wing"],
+    "909": ["wok"],
+    "910": ["wooden spoon"],
+    "911": ["wool", "woolen", "woollen"],
+    "912": ["worm fence", "snake fence", "snake-rail fence", "Virginia fence"],
+    "913": ["wreck"],
+    "914": ["yawl"],
+    "915": ["yurt"],
+    "916": ["web site", "website", "internet site", "site"],
+    "917": ["comic book"],
+    "918": ["crossword puzzle", "crossword"],
+    "919": ["street sign"],
+    "920": ["traffic light", "traffic signal", "stoplight"],
+    "921": ["book jacket", "dust cover", "dust jacket", "dust wrapper"],
+    "922": ["menu"],
+    "923": ["plate"],
+    "924": ["guacamole"],
+    "925": ["consomme"],
+    "926": ["hot pot", "hotpot"],
+    "927": ["trifle"],
+    "928": ["ice cream", "icecream"],
+    "929": ["ice lolly", "lolly", "lollipop", "popsicle"],
+    "930": ["French loaf"],
+    "931": ["bagel", "beigel"],
+    "932": ["pretzel"],
+    "933": ["cheeseburger"],
+    "934": ["hotdog", "hot dog", "red hot"],
+    "935": ["mashed potato"],
+    "936": ["head cabbage"],
+    "937": ["broccoli"],
+    "938": ["cauliflower"],
+    "939": ["zucchini", "courgette"],
+    "940": ["spaghetti squash"],
+    "941": ["acorn squash"],
+    "942": ["butternut squash"],
+    "943": ["cucumber", "cuke"],
+    "944": ["artichoke", "globe artichoke"],
+    "945": ["bell pepper"],
+    "946": ["cardoon"],
+    "947": ["mushroom"],
+    "948": ["Granny Smith"],
+    "949": ["strawberry"],
+    "950": ["orange"],
+    "951": ["lemon"],
+    "952": ["fig"],
+    "953": ["pineapple", "ananas"],
+    "954": ["banana"],
+    "955": ["jackfruit", "jak", "jack"],
+    "956": ["custard apple"],
+    "957": ["pomegranate"],
+    "958": ["hay"],
+    "959": ["carbonara"],
+    "960": ["chocolate sauce", "chocolate syrup"],
+    "961": ["dough"],
+    "962": ["meat loaf", "meatloaf"],
+    "963": ["pizza", "pizza pie"],
+    "964": ["potpie"],
+    "965": ["burrito"],
+    "966": ["red wine"],
+    "967": ["espresso"],
+    "968": ["cup"],
+    "969": ["eggnog"],
+    "970": ["alp"],
+    "971": ["bubble"],
+    "972": ["cliff", "drop", "drop-off"],
+    "973": ["coral reef"],
+    "974": ["geyser"],
+    "975": ["lakeside", "lakeshore"],
+    "976": ["promontory", "headland", "head", "foreland"],
+    "977": ["sandbar", "sand bar"],
+    "978": ["seashore", "coast", "seacoast", "sea-coast"],
+    "979": ["valley", "vale"],
+    "980": ["volcano"],
+    "981": ["ballplayer", "baseball player"],
+    "982": ["groom", "bridegroom"],
+    "983": ["scuba diver"],
+    "984": ["rapeseed"],
+    "985": ["daisy"],
+    "986": ["yellow lady's slipper", "yellow lady-slipper", "Cypripedium calceolus", "Cypripedium parviflorum"],
+    "987": ["corn"],
+    "988": ["acorn"],
+    "989": ["hip", "rose hip", "rosehip"],
+    "990": ["buckeye", "horse chestnut", "conker"],
+    "991": ["coral fungus"],
+    "992": ["agaric"],
+    "993": ["gyromitra"],
+    "994": ["stinkhorn", "carrion fungus"],
+    "995": ["earthstar"],
+    "996": ["hen-of-the-woods", "hen of the woods", "Polyporus frondosus", "Grifola frondosa"],
+    "997": ["bolete"],
+    "998": ["ear", "spike", "capitulum"],
+    "999": ["toilet tissue", "toilet paper", "bathroom tissue"]
+}
diff --git a/Samples/YOLOV5MultiInput/python/README.md b/Samples/YOLOV5MultiInput/python/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..3f4a1b69c6662c6200f360b28d71cd730def5f1d
--- /dev/null
+++ b/Samples/YOLOV5MultiInput/python/README.md
@@ -0,0 +1,96 @@
+# 目标检测（YoloV5s）
+
+#### 样例介绍
+
+使用多路离线视频流（*.mp4）作为应用程序的输入，基于YoloV5s模型对输入视频中的物体做实时检测，将推理结果信息使用imshow方式显示。
+样例代码逻辑如下所示：![输入图片说明](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/samples-pic/EdgeAndRobotics/%E5%A4%9A%E7%BA%BF%E7%A8%8B%E7%A4%BA%E4%BE%8B%E5%9B%BE%E7%89%87.png)
+
+#### 样例下载
+
+可以使用以下两种方式下载，请选择其中一种进行源码准备。
+
+- 命令行方式下载（**下载时间较长，但步骤简单**）。
+
+  ```
+  # 登录开发板，HwHiAiUser用户命令行中执行以下命令下载源码仓。    
+  cd ${HOME}     
+  git clone https://gitee.com/ascend/EdgeAndRobotics.git
+  # 切换到样例目录
+  cd EdgeAndRobotics/Samples/YOLOV5MultiInput
+  ```
+
+- 压缩包方式下载（**下载时间较短，但步骤稍微复杂**）。
+
+  ```
+  # 1. 仓右上角选择 【克隆/下载】 下拉框并选择 【下载ZIP】。     
+  # 2. 将ZIP包上传到开发板的普通用户家目录中，【例如：${HOME}/EdgeAndRobotics-master.zip】。      
+  # 3. 开发环境中，执行以下命令，解压zip包。      
+  cd ${HOME} 
+  chmod +x EdgeAndRobotics-master.zip
+  unzip EdgeAndRobotics-master.zip
+  # 4. 切换到样例目录
+  cd EdgeAndRobotics-master/Samples/YOLOV5MultiInput/python
+  ```
+
+#### 准备环境
+
+1. 以HwHiAiUser用户登录开发板。
+
+2. 安装ACLLite库。
+
+   参考[ACLLite仓](https://gitee.com/ascend/ACLLite)安装PyACLLite库。
+
+
+#### 运行样例
+
+1. 以HwHiAiUser用户登录开发板，切换到当前样例目录。
+
+2. 获取PyTorch框架的YoloV5s模型（\*.onnx），并转换为昇腾AI处理器能识别的模型（\*.om）。
+   - 当设备内存**小于8G**时，可设置如下两个环境变量减少atc模型转换过程中使用的进程数，减小内存占用。
+     ```
+     export TE_PARALLEL_COMPILER=1
+     export MAX_COMPILE_CORE_NUMBER=1
+     ```
+   - 为了方便下载，在这里直接给出原始模型下载及模型转换命令,可以直接拷贝执行。
+     ```
+     cd model
+     wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/yolov5s/yolov5s_nms.onnx --no-check-certificate
+     wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/yolov5s/aipp_rgb.cfg --no-check-certificate
+     atc --model=yolov5s_nms.onnx --framework=5 --output=yolov5s_rgb --input_shape="images:1,3,640,640;img_info:1,4"  --soc_version=Ascend310B4 --insert_op_conf=aipp_rgb.cfg
+     ```
+
+* Home page: https://www.hiascend.com/hardware/developer-kit-a2
+* Documentation: https://www.hiascend.com/hardware/developer-kit-a2/resource
+     atc命令中各参数的解释如下，详细约束说明请参见[《ATC模型转换指南》](https://hiascend.com/document/redirect/CannCommunityAtc)。
+
+     - --model：YoloV5s网络的模型文件的路径。
+     - --framework：原始框架类型。5表示ONNX。
+     - --output：om模型文件的路径。请注意，记录保存该om模型文件的路径，后续开发应用时需要使用。
+     - --input\_shape：模型输入数据的shape。
+     - --soc\_version：昇腾AI处理器的版本。
+
+3. 准备测试视频。
+
+   请从以下链接获取该样例的测试视频，放在data目录下。
+
+   ```
+   cd ../data 
+   wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/yolov5s/test.mp4 --no-check-certificate
+   ```
+
+   **注：**若需更换测试视频，则需自行准备测试视频，并将测试视频放到data目录下。
+
+4. 运行样例。
+
+     ```
+     bash sample_run.sh
+     ```
+
+#### 相关操作
+
+- 获取更多样例，请单击[Link](https://gitee.com/ascend/samples/tree/master/inference/modelInference)。
+- 获取在线视频课程，请单击[Link](https://www.hiascend.com/edu/courses?activeTab=%E5%BA%94%E7%94%A8%E5%BC%80%E5%8F%91)。
+- 获取学习文档，请单击[AscendCL python](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/80RC1alpha002/devguide/appdevg/aclpythondevg/aclpythondevg_0001.html)，查看最新版本的AscendCL推理应用开发指南。
+- 查模型的输入输出
+
+  可使用第三方工具Netron打开网络模型，查看模型输入或输出的数据类型、Shape，便于在分析应用开发场景时使用。
diff --git a/Samples/YOLOV5MultiInput/python/data/.keep b/Samples/YOLOV5MultiInput/python/data/.keep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Samples/YOLOV5MultiInput/python/model/.keep b/Samples/YOLOV5MultiInput/python/model/.keep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Samples/YOLOV5MultiInput/python/scripts/sample_run.sh b/Samples/YOLOV5MultiInput/python/scripts/sample_run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..edae8439b50737ef9b18f1a881ce2116f4b7b6f4
--- /dev/null
+++ b/Samples/YOLOV5MultiInput/python/scripts/sample_run.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+ScriptPath="$( cd "$(dirname "$BASH_SOURCE")" ; pwd -P )"
+
+echo "[INFO] The sample starts to run"
+running_command="python3 YOLOV5MultiInput.py"
+cd ${ScriptPath}/../src
+${running_command}
+if [ $? -ne 0 ];then
+    echo "[INFO] The program runs failed"
+else
+    echo "[INFO] The program runs successfully"
+fi
diff --git a/Samples/YOLOV5MultiInput/python/src/YOLOV5MultiInput.py b/Samples/YOLOV5MultiInput/python/src/YOLOV5MultiInput.py
new file mode 100644
index 0000000000000000000000000000000000000000..596456db447bc3ed8eed6c043cf0abfd7604f19d
--- /dev/null
+++ b/Samples/YOLOV5MultiInput/python/src/YOLOV5MultiInput.py
@@ -0,0 +1,158 @@
+import numpy as np
+import videocapture as video
+import acl
+import acllite_utils as utils
+import time
+import cv2
+import constants as const
+
+from acllite_resource import AclLiteResource
+from acllite_model import AclLiteModel
+from acllite_imageproc import AclLiteImageProc
+from acllite_image import AclLiteImage
+from acllite_logger import log_error, log_info
+from multiprocessing import Process, Queue, Pool, Value
+
+Q_PRE_SIZE = 32
+Q_OUT_SIZE = 32
+VIDEO_WIDTH = 1920
+VIDEO_HEIGHT = 1080
+WAIT_TIME = 0.000003
+labels = ["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", "suitcase", "frisbee",
+          "skis", "snowboard", "sports ball", "kite", "baseball bat",
+          "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle",
+          "wine glass", "cup", "fork", "knife", "spoon",
+          "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog",
+          "pizza", "donut", "cake", "chair", "sofa", "potted plant", "bed", "dining table",
+          "toilet", "TV monitor", "laptop", "mouse", "remote", "keyboard", "cell phone",
+          "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
+          "scissors", "teddy bear", "hair drier", "toothbrush"]
+
+def preprocess(path, q_pre, model_width, model_height, channel):
+    print(f'sub process preprocess{i} start')
+    VIDEO_WIDTH = 1920
+    VIDEO_HEIGHT = 1080
+    scale_x = VIDEO_WIDTH / model_width
+    scale_y = VIDEO_HEIGHT / model_height
+    # get scale factor
+    if scale_x > scale_y:
+        max_scale = scale_x
+        resize_shape = (model_width, int(VIDEO_HEIGHT/max_scale))
+    else:
+        max_scale = scale_y
+        resize_shape = (int(VIDEO_WIDTH/max_scale), model_height)
+    count = 0
+    cap = cv2.VideoCapture(path)
+    if not cap.isOpened() :
+        print('video connect failed')
+        exit(0)
+    while True:
+        ret, frame = cap.read()
+        if not ret: #  
+            print('cap read end! close subprocess cap read')
+            q_pre.put('EOF')
+            break
+        else:
+            img = np.zeros([model_height, model_width, 3], dtype=np.uint8)
+            # frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            resize_image = cv2.resize(frame, resize_shape)
+            img[0:resize_shape[1],0:resize_shape[0]] = resize_image
+            q_pre.put(img)
+            count += 1
+    print(f'pre process end! {channel}')
+
+def infer(model_path, q_pre, q_out, pnums):
+    resource = AclLiteResource()
+    resource.init()
+    model = AclLiteModel(model_path)
+    count = 0
+    nums = pnums
+    image_info = np.array([640, 640,
+                            640, 640],
+                            dtype=np.float32)
+    start = time.time()
+    while True:
+        if pnums == 0:
+            break
+        for i,q in enumerate(q_pre):
+            img = q.get()
+            if isinstance(img, str):
+                pnums -= 1
+                q_out[i].put('EOF')
+                continue
+            output = model.execute([img, image_info])
+            count += 1
+            q_out[i].put(output)
+    end = time.time()
+    print(f'fps: {count/(end-start):.3f}')
+    del resource
+    del model
+    print('infer end! close infer ')
+
+def postprocess(q_out, model_width, model_height):
+    VIDEO_WIDTH = 1920
+    VIDEO_HEIGHT = 1080
+    while True:
+        output = q_out.get()
+        if isinstance(output, str):
+            print('postprocess end! close subprocess postprocess')
+            break
+        box_num = output[1][0, 0]
+        box_info = output[0].flatten()
+        scale_x = VIDEO_WIDTH / model_width
+        scale_y = VIDEO_HEIGHT / model_height
+
+        # get scale factor
+        if scale_x > scale_y:
+            max_scale = scale_x
+        else:
+            max_scale = scale_y
+        colors = [0, 0, 255]
+
+        # draw the boxes in original image
+        result_msg = ""
+        for n in range(int(box_num)):
+            ids = int(box_info[5 * int(box_num) + n])
+            score = box_info[4 * int(box_num) + n]
+            label = labels[ids] + ":" + str("%.2f" % score)
+            top_left_x = box_info[0 * int(box_num) + n] * max_scale
+            top_left_y = box_info[1 * int(box_num) + n] * max_scale
+            bottom_right_x = box_info[2 * int(box_num) + n] * max_scale
+            bottom_right_y = box_info[3 * int(box_num) + n] * max_scale
+            result_msg += f'label:{label}  '
+            # cv2.rectangle(src_image, (int(top_left_x), int(top_left_y)),
+            #             (int(bottom_right_x), int(bottom_right_y)), colors)
+            # p3 = (max(int(top_left_x), 15), max(int(top_left_y), 15))
+            # cv2.putText(src_image, label, p3, cv2.FONT_ITALIC, 0.6, colors, 1)
+            # cv2.imshow('frame', src_image)
+        # cv2.imwrite(f'../out/out_{count}.jpg', src_image)
+        print(f'results: {result_msg}')
+
+if __name__ == '__main__':  
+    stream_path = "../data/test.mp4"
+    model_path = "../model/yolov5s_rgb.om"
+    model_width = 640
+    model_height = 640
+    pnums = 2
+
+    q_pre = [Queue(maxsize=Q_PRE_SIZE) for i in range(pnums)]
+    q_out = [Queue(maxsize=Q_OUT_SIZE) for i in range(pnums)]
+    
+    loopTime, initTime = time.time(), time.time()
+
+    processes = []
+    for i in range(pnums):
+        processes.append(Process(target=preprocess, args=(stream_path, q_pre[i], model_width, model_height, i)))
+    processes.append(Process(target=infer, args=(model_path, q_pre, q_out, pnums)))
+    for i in range(pnums):
+        processes.append(Process(target=postprocess, args=(q_out[i],model_width, model_width)))
+
+    [process.start() for process in processes]
+    [process.join() for process in processes]
+    print('子进程运行结束')
+
diff --git a/Samples/YOLOV5USBCamera/README.md b/Samples/YOLOV5USBCamera/README.md
index 98141b581a9d9f748a3b641479607e471cefb23f..eb8337f4beeae7b2b1fa61281b081ada563ccb4b 100644
--- a/Samples/YOLOV5USBCamera/README.md
+++ b/Samples/YOLOV5USBCamera/README.md
@@ -72,7 +72,7 @@
 
      atc命令中各参数的解释如下，详细约束说明请参见[《ATC模型转换指南》](https://hiascend.com/document/redirect/CannCommunityAtc)。
 
-     - --model：ResNet-50网络的模型文件的路径。
+     - --model：Yolov5网络的模型文件的路径。
      - --framework：原始框架类型。5表示ONNX。
      - --output：yolov5s.om模型文件的路径。请注意，记录保存该om模型文件的路径，后续开发应用时需要使用。
      - --input\_shape：模型输入数据的shape。
diff --git a/Samples/YOLOV5USBCamera/python/README.md b/Samples/YOLOV5USBCamera/python/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..f7f14b91669a9fe49aa8e19bd703019debce8a1b
--- /dev/null
+++ b/Samples/YOLOV5USBCamera/python/README.md
@@ -0,0 +1,85 @@
+# 目标检测（YoloV5s）
+
+#### 样例介绍
+
+通过USB接口连接Camera与开发板，从Camera获取视频，基于yolov5s模型对输入视频中的物体做实时检测，将推理结果信息使用imshow方式显示。    
+
+#### 样例下载
+
+可以使用以下两种方式下载，请选择其中一种进行源码准备。
+
+- 命令行方式下载（**下载时间较长，但步骤简单**）。
+
+  ```
+  # 登录开发板，HwHiAiUser用户命令行中执行以下命令下载源码仓。    
+  cd ${HOME}     
+  git clone https://gitee.com/ascend/EdgeAndRobotics.git
+  # 切换到样例目录
+  cd EdgeAndRobotics/Samples/YOLOV5USBCamera/python
+  ```
+
+- 压缩包方式下载（**下载时间较短，但步骤稍微复杂**）。
+
+  ```
+  # 1. 仓右上角选择 【克隆/下载】 下拉框并选择 【下载ZIP】。     
+  # 2. 将ZIP包上传到开发板的普通用户家目录中，【例如：${HOME}/EdgeAndRobotics-master.zip】。      
+  # 3. 开发环境中，执行以下命令，解压zip包。      
+  cd ${HOME} 
+  chmod +x EdgeAndRobotics-master.zip
+  unzip EdgeAndRobotics-master.zip
+  # 4. 切换到样例目录
+  cd EdgeAndRobotics-master/Samples/YOLOV5USBCamera/python
+  ```
+
+#### 执行准备
+
+1. 确认已安装带桌面的镜像且HDMI连接的屏幕正常显示
+
+2. 以HwHiAiUser用户登录开发板。
+
+3. 安装ACLLite库。
+
+   参考[ACLLite仓](https://gitee.com/ascend/ACLLite)安装PyACLLite库。
+
+#### 运行样例
+
+1. 以HwHiAiUser用户登录开发板，切换到当前样例目录。
+
+2. 获取PyTorch框架的Yolov5模型（\*.onnx），并转换为昇腾AI处理器能识别的模型（\*.om）。
+   - 当设备内存**小于8G**时，可设置如下两个环境变量减少atc模型转换过程中使用的进程数，减小内存占用。
+     ```
+     export TE_PARALLEL_COMPILER=1
+     export MAX_COMPILE_CORE_NUMBER=1
+     ```
+   - 为了方便下载，在这里直接给出原始模型下载及模型转换命令,可以直接拷贝执行。
+     ```
+     cd model
+     wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/yolov5s/yolov5s_nms.onnx --no-check-certificate
+     wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/yolov5s/aipp_rgb.cfg --no-check-certificate
+     atc --model=yolov5s_nms.onnx --framework=5 --output=yolov5s_rgb --input_shape="images:1,3,640,640;img_info:1,4"  --soc_version=Ascend310B4  --insert_op_conf=aipp_rgb.cfg
+     ```
+
+     atc命令中各参数的解释如下，详细约束说明请参见[《ATC模型转换指南》](https://hiascend.com/document/redirect/CannCommunityAtc)。
+
+     - --model：yolov5网络的模型文件的路径。
+     - --framework：原始框架类型。5表示ONNX。
+     - --output：yolov5s_rgb.om模型文件的路径。请注意，记录保存该om模型文件的路径，后续开发应用时需要使用。
+     - --input\_shape：模型输入数据的shape。
+     - --soc\_version：昇腾AI处理器的版本。
+
+3. 运行样例。
+
+   执行以下脚本运行样例：
+
+   ```
+   bash sample_run.sh
+   ```
+
+#### 相关操作
+
+- 获取更多样例，请单击[Link](https://gitee.com/ascend/samples/tree/master/inference/modelInference)。
+- 获取在线视频课程，请单击[Link](https://www.hiascend.com/edu/courses?activeTab=%E5%BA%94%E7%94%A8%E5%BC%80%E5%8F%91)。
+- 获取学习文档，请单击[AscendCL Python](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/80RC1alpha003/devguide/appdevg/aclpythondevg/aclpythondevg_0001.html)，查看最新版本的AscendCL推理应用开发指南。
+- 查模型的输入输出
+
+  可使用第三方工具Netron打开网络模型，查看模型输入或输出的数据类型、Shape，便于在分析应用开发场景时使用。
diff --git a/Samples/YOLOV5USBCamera/python/data/.keep b/Samples/YOLOV5USBCamera/python/data/.keep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Samples/YOLOV5USBCamera/python/model/.keep b/Samples/YOLOV5USBCamera/python/model/.keep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Samples/YOLOV5USBCamera/python/scripts/sample_run.sh b/Samples/YOLOV5USBCamera/python/scripts/sample_run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..14e9d7e5ef4589097e296a5bb17d67bbc3e546c1
--- /dev/null
+++ b/Samples/YOLOV5USBCamera/python/scripts/sample_run.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+ScriptPath="$( cd "$(dirname "$BASH_SOURCE")" ; pwd -P )"
+
+echo "[INFO] The sample starts to run"
+
+cd ${ScriptPath}/../src
+python3 YOLOV5USBCamera.py
+if [ $? -ne 0 ];then
+    echo "[INFO] The program runs failed"
+else
+    echo "[INFO] The program runs successfully"
+fi
diff --git a/Samples/YOLOV5USBCamera/python/src/YOLOV5USBCamera.py b/Samples/YOLOV5USBCamera/python/src/YOLOV5USBCamera.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7a88894f9d2bbd19f641ad00d0606c678507104
--- /dev/null
+++ b/Samples/YOLOV5USBCamera/python/src/YOLOV5USBCamera.py
@@ -0,0 +1,112 @@
+import videocapture as video
+import numpy as np
+import cv2
+
+import time 
+
+from acllite_resource import AclLiteResource
+from acllite_model import AclLiteModel
+from acllite_imageproc import AclLiteImageProc
+from acllite_image import AclLiteImage
+from label import labels
+from acllite_logger import log_error, log_info
+
+
+class sampleYOLOV7(object):
+    '''load the model, and do preprocess, infer, postprocess'''
+    def __init__(self, model_path, model_width, model_height):
+        self.model_path = model_path
+        self.model_width = model_width
+        self.model_height = model_height
+
+    def init_resource(self):
+        # initial acl resource, create image processor, create model
+        self._resource = AclLiteResource()
+        self._resource.init()
+    
+        self._dvpp = AclLiteImageProc(self._resource) 
+        self._model = AclLiteModel(self.model_path)
+
+    def preprocess(self, frame):
+        # resize frame, keep original image
+        self.src_image = frame
+        self.resized_image = cv2.resize(frame, (self.model_width, self.model_height))
+
+    def infer(self):
+        # infer frame
+        image_info = np.array([640, 640,
+                            640, 640],
+                            dtype=np.float32)
+        self.result = self._model.execute([self.resized_image, image_info])
+    
+    def postprocess(self):
+        box_num = self.result[1][0, 0]
+        box_info = self.result[0].flatten()
+
+        height, width, _ = self.src_image.shape 
+        scale_x = width / self.model_width
+        scale_y = height / self.model_height
+
+        colors = [0, 0, 255]
+        text = ""
+        # draw the boxes in original image
+        for n in range(int(box_num)):
+            ids = int(box_info[5 * int(box_num) + n])
+            score = box_info[4 * int(box_num) + n]
+            label = labels[ids] + ":" + str("%.2f" % score)
+            top_left_x = box_info[0 * int(box_num) + n] * scale_x
+            top_left_y = box_info[1 * int(box_num) + n] * scale_y
+            bottom_right_x = box_info[2 * int(box_num) + n] * scale_x
+            bottom_right_y = box_info[3 * int(box_num) + n] * scale_y
+            cv2.rectangle(self.src_image, (int(top_left_x), int(top_left_y)),
+                        (int(bottom_right_x), int(bottom_right_y)), colors)
+            p3 = (max(int(top_left_x), 15), max(int(top_left_y), 15))
+            position = [int(top_left_x), int(top_left_y), int(bottom_right_x), int(bottom_right_y)]
+            cv2.putText(self.src_image, label, p3, cv2.FONT_ITALIC, 0.6, colors, 1)
+            text += f'label:{label} {position}  '
+        log_info(text)
+        cv2.imshow('out', self.src_image)
+
+    def release_resource(self):
+        # release resource includes acl resource, data set and unload model
+        del self._resource
+        del self._dvpp
+        del self._model
+        del self.resized_image
+
+def find_camera_index():
+    max_index_to_check = 10  # Maximum index to check for camera
+    for index in range(max_index_to_check):
+        cap = cv2.VideoCapture(index)
+        if cap.read()[0]:
+            cap.release()
+            return index
+    # If no camera is found
+    raise ValueError("No camera found.")
+
+
+if __name__ == '__main__':
+    model_path = '../model/yolov5s_rgb.om'
+    model_width = 640
+    model_height = 640
+    model = sampleYOLOV7(model_path, model_width, model_height)
+    model.init_resource()
+
+    camera_index = find_camera_index()
+    cap = cv2.VideoCapture(camera_index)
+    cv2.namedWindow('out', cv2.WINDOW_NORMAL)
+    while True:
+        ret, frame = cap.read()
+        if not ret:  
+            print("Can't receive frame (stream end?). Exiting ...")  
+            break  
+        model.preprocess(frame)
+        model.infer()
+        model.postprocess()
+        # cv2.imshow('Frame', frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'):  
+            break  
+    cap.release()  
+    cv2.destroyAllWindows()
+    
+    model.release_resource()
diff --git a/Samples/YOLOV5USBCamera/python/src/label.py b/Samples/YOLOV5USBCamera/python/src/label.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc23459ea93a175bf113f22f8776aa6ff664625b
--- /dev/null
+++ b/Samples/YOLOV5USBCamera/python/src/label.py
@@ -0,0 +1,20 @@
+"""
+Copyright (R) @huawei.com, all rights reserved
+-*- coding:utf-8 -*-
+CREATED:  2023-05-25 09:12:13
+MODIFIED: 2023-05-25 10:10:55
+"""
+labels = ["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", "suitcase", "frisbee",
+          "skis", "snowboard", "sports ball", "kite", "baseball bat",
+          "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle",
+          "wine glass", "cup", "fork", "knife", "spoon",
+          "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog",
+          "pizza", "donut", "cake", "chair", "sofa", "potted plant", "bed", "dining table",
+          "toilet", "TV monitor", "laptop", "mouse", "remote", "keyboard", "cell phone",
+          "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
+          "scissors", "teddy bear", "hair drier", "toothbrush"]
\ No newline at end of file
diff --git a/Samples/YOLOV5Video/README.md b/Samples/YOLOV5Video/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ac623ec8f243ea5dba27be152f6c2bedae2630e1
--- /dev/null
+++ b/Samples/YOLOV5Video/README.md
@@ -0,0 +1,98 @@
+# 目标检测（YoloV5s）
+
+#### 样例介绍
+
+通过USB接口连接Camera与开发板，从Camera获取视频，基于yolov5s模型对输入视频中的物体做实时检测，将推理结果信息使用imshow方式显示。    
+
+#### 样例下载
+
+可以使用以下两种方式下载，请选择其中一种进行源码准备。
+
+- 命令行方式下载（**下载时间较长，但步骤简单**）。
+
+  ```
+  # 登录开发板，HwHiAiUser用户命令行中执行以下命令下载源码仓。    
+  cd ${HOME}     
+  git clone https://gitee.com/ascend/EdgeAndRobotics.git
+  # 切换到样例目录
+  cd EdgeAndRobotics/Samples/YOLOV5Video
+  ```
+
+- 压缩包方式下载（**下载时间较短，但步骤稍微复杂**）。
+
+  ```
+  # 1. 仓右上角选择 【克隆/下载】 下拉框并选择 【下载ZIP】。     
+  # 2. 将ZIP包上传到开发板的普通用户家目录中，【例如：${HOME}/EdgeAndRobotics-master.zip】。      
+  # 3. 开发环境中，执行以下命令，解压zip包。      
+  cd ${HOME} 
+  chmod +x EdgeAndRobotics-master.zip
+  unzip EdgeAndRobotics-master.zip
+  # 4. 切换到样例目录
+  cd EdgeAndRobotics-master/Samples/YOLOV5Video
+  ```
+
+#### 执行准备
+
+1. 确认已安装带桌面的镜像且HDMI连接的屏幕正常显示
+
+2. 以HwHiAiUser用户登录开发板。
+
+3. 安装ACLLite库。
+
+   参考[ACLLite仓](https://gitee.com/ascend/ACLLite)安装PyACLLite库。
+
+#### 运行样例
+
+1. 以HwHiAiUser用户登录开发板，切换到当前样例目录。
+
+2. 获取PyTorch框架的Yolov5模型（\*.onnx），并转换为昇腾AI处理器能识别的模型（\*.om）。
+   - 当设备内存**小于8G**时，可设置如下两个环境变量减少atc模型转换过程中使用的进程数，减小内存占用。
+     ```
+     export TE_PARALLEL_COMPILER=1
+     export MAX_COMPILE_CORE_NUMBER=1
+     ```
+   - 为了方便下载，在这里直接给出原始模型下载及模型转换命令,可以直接拷贝执行。
+     ```
+     cd model
+     wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/yolov5s/yolov5s_nms.onnx --no-check-certificate
+     wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/yolov5s/aipp.cfg --no-check-certificate
+     atc --model=yolov5s_nms.onnx --framework=5 --output=yolov5s_nms --input_shape="images:1,3,640,640;img_info:1,4"  --soc_version=Ascend310B4  --insert_op_conf=aipp.cfg
+     ```
+
+     atc命令中各参数的解释如下，详细约束说明请参见[《ATC模型转换指南》](https://hiascend.com/document/redirect/CannCommunityAtc)。
+
+     - --model：yolov5网络的模型文件的路径。
+     - --framework：原始框架类型。5表示ONNX。
+     - --output：yolov5s_nms.om模型文件的路径。请注意，记录保存该om模型文件的路径，后续开发应用时需要使用。
+     - --input\_shape：模型输入数据的shape。
+     - --soc\_version：昇腾AI处理器的版本。
+
+3. 准备测试视频和图片。
+
+   请从以下链接获取该样例的测试视频，放在data目录下。
+
+   ```
+   cd ../data 
+   wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/yolov5s/test.和64 --no-check-certificate
+   wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/models/aclsample/dog1_1024_683.jpg --no-check-certificate
+   ```
+
+   **注：**若需更换测试视频，则需自行准备测试视频，并将测试视频放到data目录下。
+
+4. 运行样例。
+
+   执行以下脚本运行样例：
+
+   ```
+   bash sample_run.sh
+   ```
+   可以通过修改YOLOV5Video.py中的变量mode选择处理图片或视频，结果会以imshow的方式呈现。
+
+#### 相关操作
+
+- 获取更多样例，请单击[Link](https://gitee.com/ascend/samples/tree/master/inference/modelInference)。
+- 获取在线视频课程，请单击[Link](https://www.hiascend.com/edu/courses?activeTab=%E5%BA%94%E7%94%A8%E5%BC%80%E5%8F%91)。
+- 获取学习文档，请单击[AscendCL Python](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/80RC1alpha003/devguide/appdevg/aclpythondevg/aclpythondevg_0001.html)，查看最新版本的AscendCL推理应用开发指南。
+- 查模型的输入输出
+
+  可使用第三方工具Netron打开网络模型，查看模型输入或输出的数据类型、Shape，便于在分析应用开发场景时使用。
diff --git a/Samples/YOLOV5Video/data/.keep b/Samples/YOLOV5Video/data/.keep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Samples/YOLOV5Video/model/.keep b/Samples/YOLOV5Video/model/.keep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Samples/YOLOV5Video/scripts/sample_run.sh b/Samples/YOLOV5Video/scripts/sample_run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..abc91851ff53212657ddc7e8586a9c83d4dc8404
--- /dev/null
+++ b/Samples/YOLOV5Video/scripts/sample_run.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+ScriptPath="$( cd "$(dirname "$BASH_SOURCE")" ; pwd -P )"
+
+echo "[INFO] The sample starts to run"
+
+cd ${ScriptPath}/../src
+python3 YOLOV5Video.py
+if [ $? -ne 0 ];then
+    echo "[INFO] The program runs failed"
+else
+    echo "[INFO] The program runs successfully"
+fi
diff --git a/Samples/YOLOV5Video/src/YOLOV5Video.py b/Samples/YOLOV5Video/src/YOLOV5Video.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b827ec508e3d20f1dfc5f3410c6cb51cd51f0ba
--- /dev/null
+++ b/Samples/YOLOV5Video/src/YOLOV5Video.py
@@ -0,0 +1,137 @@
+import videocapture as video
+import numpy as np
+import cv2
+
+import time 
+
+from acllite_resource import AclLiteResource
+from acllite_model import AclLiteModel
+from acllite_imageproc import AclLiteImageProc
+from acllite_image import AclLiteImage
+from label import labels
+from acllite_logger import log_error, log_info
+
+
+class sampleYOLOV7(object):
+    '''load the model, and do preprocess, infer, postprocess'''
+    def __init__(self, model_path, model_width, model_height):
+        self.model_path = model_path
+        self.model_width = model_width
+        self.model_height = model_height
+        self.conf_thres = 0.25
+        self.nms_thres = 0.45
+        self.src_image = None
+
+    def init_resource(self):
+        # initial acl resource, create image processor, create model
+        self._resource = AclLiteResource()
+        self._resource.init()
+    
+        self._dvpp = AclLiteImageProc(self._resource) 
+        self._model = AclLiteModel(self.model_path)
+
+    def preprocess_image(self, frame):
+        # resize frame by dvpp
+        yuv_image = self._dvpp.jpegd(frame)
+        self.resized_image = self._dvpp.resize(yuv_image, self.model_width, self.model_height)
+
+    def preprocess_vis(self, frame):
+        # resize frame, keep original image
+        src_image = frame.byte_data_to_np_array().astype(np.uint8)
+        self.src_image = cv2.cvtColor(src_image.reshape((frame.height*3//2, frame.width)), cv2.COLOR_YUV2RGB_NV21)
+
+        self.resized_image = self._dvpp.resize(frame, self.model_width, self.model_height)
+
+    def infer(self):
+        # infer frame
+        image_info = np.array([640, 640,
+                            640, 640],
+                            dtype=np.float32)
+        self.result = self._model.execute([self.resized_image, image_info])
+    
+    def postprocess(self,path):
+        box_num = self.result[1][0, 0]
+        box_info = self.result[0].flatten()
+
+        if self.src_image is None:
+            src_image = cv2.imread(path)
+        else:
+            src_image = self.src_image
+        height, width, _ = src_image.shape 
+        scale_x = width / self.model_width
+        scale_y = height / self.model_height
+
+        colors = [0, 0, 255]
+        text = ""
+        # draw the boxes in original image
+        for n in range(int(box_num)):
+            ids = int(box_info[5 * int(box_num) + n])
+            score = box_info[4 * int(box_num) + n]
+            label = labels[ids] + ":" + str("%.2f" % score)
+            top_left_x = box_info[0 * int(box_num) + n] * scale_x
+            top_left_y = box_info[1 * int(box_num) + n] * scale_y
+            bottom_right_x = box_info[2 * int(box_num) + n] * scale_x
+            bottom_right_y = box_info[3 * int(box_num) + n] * scale_y
+            cv2.rectangle(src_image, (int(top_left_x), int(top_left_y)),
+                        (int(bottom_right_x), int(bottom_right_y)), colors)
+            p3 = (max(int(top_left_x), 15), max(int(top_left_y), 15))
+            position = [int(top_left_x), int(top_left_y), int(bottom_right_x), int(bottom_right_y)]
+            cv2.putText(src_image, label, p3, cv2.FONT_ITALIC, 0.6, colors, 1)
+            text += f'label:{label} {position}  '
+        log_info(text)
+        cv2.imshow('out', src_image)
+
+    def release_resource(self):
+        # release resource includes acl resource, data set and unload model
+        del self.resized_image
+        del self._model
+        del self._dvpp
+        del self._resource
+
+def video_infer(video_path, model):
+    cap = video.VideoCapture(video_path)
+    while True:
+        ret, frame = cap.read()
+        if ret:
+            print('cap read end! close subprocess cap read')
+            break
+        if frame is not None:
+            print('start preprocess')
+            model.preprocess_vis(frame)
+            model.infer()
+            model.postprocess(video_path)
+            cv2.waitKey(1)
+        else:
+            log_info("read frame finish")
+            break
+    del cap
+
+def image_infer(image_path, model):
+    frame = AclLiteImage(image_path)
+    model.preprocess_image(frame)
+    model.infer()
+    model.postprocess(image_path)
+    cv2.waitKey(0)
+
+
+if __name__ == '__main__':
+    model_path = '../model/yolov5s_nms.om'
+    model_width = 640
+    model_height = 640
+
+    model = sampleYOLOV7(model_path, model_width, model_height)
+    model.init_resource()
+    cv2.namedWindow('out', cv2.WINDOW_NORMAL)
+
+    mode = "video"
+    if mode == "image":
+        path = "../data/dog1_1024_683.jpg"
+        image_infer(path, model)
+    elif mode == "video":
+        path = "../data/test.h264"
+        video_infer(path, model)
+    else:
+        print('input mode is incorrect.')
+
+    cv2.destroyAllWindows()
+    model.release_resource()
diff --git a/Samples/YOLOV5Video/src/label.py b/Samples/YOLOV5Video/src/label.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc23459ea93a175bf113f22f8776aa6ff664625b
--- /dev/null
+++ b/Samples/YOLOV5Video/src/label.py
@@ -0,0 +1,20 @@
+"""
+Copyright (R) @huawei.com, all rights reserved
+-*- coding:utf-8 -*-
+CREATED:  2023-05-25 09:12:13
+MODIFIED: 2023-05-25 10:10:55
+"""
+labels = ["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", "suitcase", "frisbee",
+          "skis", "snowboard", "sports ball", "kite", "baseball bat",
+          "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle",
+          "wine glass", "cup", "fork", "knife", "spoon",
+          "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog",
+          "pizza", "donut", "cake", "chair", "sofa", "potted plant", "bed", "dining table",
+          "toilet", "TV monitor", "laptop", "mouse", "remote", "keyboard", "cell phone",
+          "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
+          "scissors", "teddy bear", "hair drier", "toothbrush"]
\ No newline at end of file