├── LICENSE.md ├── README.md ├── classify_GUI.py ├── label.csv └── pic ├── init.png ├── mobilenetv3_l.png └── resnet18.png /LICENSE.md: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. 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-------------------------------------------------------------------------------- 1 | # Classify_GUI 2 | 基于深度学习的图像分类的GUI界面。 3 | 4 | #### 初始化界面 5 | 图片名称 6 | 7 | #### resnet18预测 8 | 图片名称 9 | 10 | #### mobilenetv3预测 11 | 图片名称 12 | -------------------------------------------------------------------------------- /classify_GUI.py: -------------------------------------------------------------------------------- 1 | import tkinter.ttk 2 | from tkinter import * 3 | from tkinter import filedialog 4 | import torch 5 | from torchvision import transforms 6 | import pandas as pd 7 | from torchvision import models 8 | import numpy as np 9 | from PIL import Image, ImageTk 10 | import tkinter.font as tkFont 11 | from tkinter import ttk 12 | 13 | """Written by Cai""" 14 | with torch.no_grad(): 15 | df = pd.read_csv('label.csv') 16 | 17 | net_list = {'resnet18': models.resnet18(pretrained=True), 'resnet50': models.resnet50(pretrained=True), 18 | 'resnet101': models.resnet101(pretrained=True), 19 | 'mobilenetv2': models.mobilenet_v2(pretrained=True), 20 | 'mobilenetv3_l': models.mobilenet_v3_large(pretrained=True), 21 | 'mobilenetv3_s': models.mobilenet_v3_small(pretrained=True), 22 | 'vgg11': models.vgg11_bn(pretrained=True), 'vgg16': models.vgg16_bn(pretrained=True), 23 | 'googlenet': models.googlenet(pretrained=True), 24 | 'efficient_v1': models.efficientnet_b2(pretrained=True), 25 | 'efficient_v2_l': models.efficientnet_v2_l(pretrained=True), 26 | 'efficient_v2_m': models.efficientnet_v2_m(pretrained=True), 27 | 'efficient_v2_s': models.efficientnet_v2_s(pretrained=True)} 28 | 29 | trans = transforms.Compose([transforms.ToTensor(), 30 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), 31 | ]) 32 | 33 | img_file = None 34 | path = None 35 | def open_pred(): 36 | global img_file 37 | global path 38 | ori_path = path 39 | path = filedialog.askopenfilename() 40 | if path == '': 41 | path = ori_path 42 | return 43 | 44 | img = Image.open(path) 45 | img = trans(img) 46 | img_file = Image.open(path) 47 | w, h = img_file.size 48 | if w / h > show_img.winfo_width() / (show_img.winfo_height() * 11 / 12): 49 | img_file = img_file.resize((show_img.winfo_width(), int(show_img.winfo_width() / w * h))) 50 | else: 51 | img_file = img_file.resize( 52 | (int(11 / 12 * show_img.winfo_height() / h * w), int(11 / 12 * show_img.winfo_height()))) 53 | img_file = ImageTk.PhotoImage(img_file) 54 | show_img.create_image(int(show_img.winfo_width() * 0.5), 0, anchor='n', image=img_file) 55 | net = net_list[bbox.get()] 56 | net.eval() 57 | prediction = torch.softmax(net(img.reshape(1, 3, img.shape[1], -1)), dim=1).detach().numpy() 58 | label = np.argmax(prediction, axis=1) 59 | 60 | var.set(df.iloc[label, 1].values[0] + f':{np.max(prediction):.1%}') 61 | 62 | 63 | def pred(): 64 | global img_file 65 | global path 66 | img = Image.open(path) 67 | img = trans(img) 68 | img_file = Image.open(path) 69 | w, h = img_file.size 70 | if w / h > show_img.winfo_width() / (show_img.winfo_height() * 11 / 12): 71 | img_file = img_file.resize((show_img.winfo_width(), int(show_img.winfo_width() / w * h))) 72 | else: 73 | img_file = img_file.resize( 74 | (int(11 / 12 * show_img.winfo_height() / h * w), int(11 / 12 * show_img.winfo_height()))) 75 | img_file = ImageTk.PhotoImage(img_file) 76 | show_img.create_image(int(show_img.winfo_width() * 0.5), 0, anchor='n', image=img_file) 77 | net = net_list[bbox.get()] 78 | net.eval() 79 | prediction = torch.softmax(net(img.reshape(1, 3, img.shape[1], -1)), dim=1).detach().numpy() 80 | label = np.argmax(prediction, axis=1) 81 | 82 | var.set(df.iloc[label, 1].values[0] + f':{np.max(prediction):.1%}') 83 | 84 | 85 | root = Tk() 86 | root.title('Pic Predict GUI') 87 | root.geometry("800x600") 88 | var = StringVar() 89 | 90 | open_Style = tkFont.Font(family="Lucida Grande", size=12) 91 | pre_Style = tkFont.Font(family="Bahnschrift SemiBold", size=16) 92 | # 打开图片的按钮 93 | open_img = Button(root, text='选择图片', command=open_pred, font=open_Style) 94 | open_img.place(relx=0.1, rely=0.8, anchor='nw', relwidth=1 / 8, relheight=1 / 12) 95 | 96 | pred_button = Button(root, text='预测', command=pred, font=open_Style) 97 | pred_button.place(relx=0.48, rely=0.8, anchor='ne', relwidth=1 / 8, relheight=1 / 12) 98 | 99 | show_img = Canvas(root, borderwidth=2, relief='sunken') 100 | show_img.place(relx=0.06, rely=1 / 15, anchor='nw', relwidth=0.45, relheight=0.7) 101 | 102 | # 设置类别标签 103 | cls_label = Label(root, text='模型选择: ', font=pre_Style) 104 | cls_label.place(relx=0.53, rely=0.06, anchor='nw', relwidth=0.15, relheight=0.08) 105 | 106 | # 设置下拉框 107 | bbox = ttk.Combobox(root, 108 | values=['resnet18', 'resnet50', 'resnet101', 'mobilenetv2', 'mobilenetv3_l', 'mobilenetv3_s', 109 | 'vgg11', 'vgg16', 'googlenet', 'efficient_v1', 'efficient_v2_l', 'efficient_v2_m', 110 | 'efficient_v2_s'], font=pre_Style) 111 | bbox.set('resnet18') 112 | bbox.place(relx=0.69, rely=0.06, anchor='nw', relwidth=0.3, relheight=0.08) 113 | 114 | pre_label = Label(root, text='预测结果: ', font=pre_Style) 115 | pre_label.place(relx=0.53, rely=0.2, anchor='nw', relwidth=0.15, relheight=0.08) 116 | 117 | # 显示预测概率 118 | predictive_probability = Label(root, textvariable=var, font=pre_Style, borderwidth=5, relief='groove') 119 | predictive_probability.place(relx=0.69, rely=0.2, anchor='nw', relwidth=0.3, relheight=0.08) 120 | 121 | root.mainloop() 122 | -------------------------------------------------------------------------------- /label.csv: -------------------------------------------------------------------------------- 1 | file_name,label_name 2 | n01440764,丁鲷 3 | n01443537,金鱼 4 | n01484850,大白鲨 5 | n01491361,虎鲨 6 | n01494475,锤头鲨 7 | n01496331,电鳐 8 | n01498041,黄貂鱼 9 | n01514668,公鸡 10 | n01514859,母鸡 11 | n01518878,鸵鸟 12 | n01530575,燕雀 13 | n01531178,金翅雀 14 | n01532829,家朱雀 15 | n01534433,灯芯草雀 16 | n01537544,靛蓝雀 17 | n01558993,蓝鹀 18 | n01560419,夜莺 19 | n01580077,松鸦 20 | n01582220,喜鹊 21 | n01592084,山雀 22 | n01601694,河鸟 23 | n01608432,鸢（猛禽） 24 | n01614925,秃头鹰 25 | n01616318,秃鹫 26 | n01622779,大灰猫头鹰 27 | n01629819,欧洲火蝾螈 28 | n01630670,普通蝾螈 29 | n01631663,水蜥 30 | n01632458,斑点蝾螈 31 | n01632777,蝾螈 32 | n01641577,牛蛙 33 | n01644373,树蛙 34 | n01644900,尾蛙 35 | n01664065,红海龟 36 | n01665541,皮革龟 37 | n01667114,泥龟 38 | n01667778,淡水龟 39 | n01669191,箱龟 40 | n01675722,带状壁虎 41 | n01677366,普通鬣蜥 42 | n01682714,美国变色龙 43 | n01685808,鞭尾蜥蜴 44 | n01687978,飞龙科蜥蜴 45 | n01688243,褶边蜥蜴 46 | n01689811,鳄鱼蜥蜴 47 | n01692333,毒蜥 48 | n01693334,绿蜥蜴 49 | n01694178,非洲变色龙 50 | n01695060,科莫多蜥蜴 51 | n01697457,非洲鳄 52 | n01698640,美国鳄鱼 53 | n01704323,三角龙 54 | n01728572,雷蛇 55 | n01728920,环蛇 56 | n01729322,希腊蛇 57 | n01729977,绿蛇 58 | n01734418,国王蛇 59 | n01735189,袜带蛇 60 | n01737021,水蛇 61 | n01739381,藤蛇 62 | n01740131,夜蛇 63 | n01742172,大蟒蛇 64 | n01744401,岩石蟒蛇 65 | n01748264,印度眼镜蛇 66 | n01749939,绿曼巴 67 | n01751748,海蛇 68 | n01753488,角腹蛇 69 | n01755581,菱纹响尾蛇 70 | n01756291,角响尾蛇 71 | n01768244,三叶虫 72 | n01770081,盲蜘蛛 73 | n01770393,蝎子 74 | n01773157,黑金花园蜘蛛 75 | n01773549,谷仓蜘蛛 76 | n01773797,花园蜘蛛 77 | n01774384,黑寡妇蜘蛛 78 | n01774750,狼蛛 79 | n01775062,狼蜘蛛 80 | n01776313,壁虱 81 | n01784675,蜈蚣 82 | n01795545,黑松鸡 83 | n01796340,松鸡 84 | n01797886,披肩鸡 85 | n01798484,草原鸡 86 | n01806143,孔雀 87 | n01806567,鹌鹑 88 | n01807496,鹧鸪 89 | n01817953,非洲灰鹦鹉 90 | n01818515,金刚鹦鹉 91 | n01819313,硫冠鹦鹉 92 | n01820546,短尾鹦鹉 93 | n01824575,褐翅鸦鹃 94 | n01828970,蜜蜂 95 | n01829413,犀鸟 96 | n01833805,蜂鸟 97 | n01843065,鹟䴕 98 | n01843383,犀鸟 99 | n01847000,野鸭 100 | n01855032,红胸秋沙鸭 101 | n01855672,鹅 102 | n01860187,黑天鹅 103 | n01871265,大象 104 | n01872401,针鼹鼠 105 | n01873310,鸭嘴兽 106 | n01877812,沙袋鼠 107 | n01882714,考拉 108 | n01883070,袋熊 109 | n01910747,水母 110 | n01914609,海葵 111 | n01917289,脑珊瑚 112 | n01924916,扁形虫扁虫 113 | n01930112,线虫 114 | n01943899,海螺 115 | n01944390,蜗牛 116 | n01945685,鼻涕虫 117 | n01950731,海参 118 | n01955084,石鳖 119 | n01968897,鹦鹉螺 120 | n01978287,珍宝蟹 121 | n01978455,石蟹 122 | n01980166,招潮蟹 123 | n01981276,帝王蟹 124 | n01983481,美国龙虾 125 | n01984695,大螯虾 126 | n01985128,小龙虾 127 | n01986214,寄居蟹 128 | n01990800,等足目动物(明虾和螃蟹近亲) 129 | n02002556,白鹳 130 | n02002724,黑鹳 131 | n02006656,鹭 132 | n02007558,火烈鸟 133 | n02009229,小蓝鹭 134 | n02009912,美国鹭 135 | n02011460,麻鸦 136 | n02012849,鹤 137 | n02013706,秧鹤 138 | n02017213,欧洲水鸡 139 | n02018207,沼泽泥母鸡 140 | n02018795,鸨 141 | n02025239,红翻石鹬 142 | n02027492,红背鹬 143 | n02028035,红脚鹬 144 | n02033041,半蹼鹬 145 | n02037110,蛎鹬 146 | n02051845,鹈鹕 147 | n02056570,国王企鹅 148 | n02058221,信天翁 149 | n02066245,灰鲸 150 | n02071294,杀人鲸 151 | n02074367,海牛 152 | n02077923,海狮 153 | n02085620,奇瓦瓦 154 | n02085782,日本猎犬 155 | n02085936,马尔济斯犬 156 | n02086079,狮子狗 157 | n02086240,西施犬 158 | n02086646,布莱尼姆猎犬 159 | n02086910,巴比狗 160 | n02087046,玩具犬 161 | n02087394,罗得西亚长背猎狗 162 | n02088094,阿富汗猎犬 163 | n02088238,猎犬 164 | n02088364,比格犬 165 | n02088466,侦探犬 166 | n02088632,蓝色快狗 167 | n02089078,黑褐猎浣熊犬 168 | n02089867,沃克猎犬 169 | n02089973,英国猎狐犬 170 | n02090379,美洲赤狗 171 | n02090622,俄罗斯猎狼犬 172 | n02090721,爱尔兰猎狼犬 173 | n02091032,意大利灰狗 174 | n02091134,惠比特犬 175 | n02091244,依比沙猎犬 176 | n02091467,挪威猎犬 177 | n02091635,奥达猎犬 178 | n02091831,沙克犬 179 | n02092002,苏格兰猎鹿犬 180 | n02092339,威玛猎犬 181 | n02093256,斯塔福德郡牛头梗 182 | n02093428,美国斯塔福德郡梗 183 | n02093647,贝德灵顿梗 184 | n02093754,边境梗 185 | n02093859,凯丽蓝梗 186 | n02093991,爱尔兰梗 187 | n02094114,诺福克梗 188 | n02094258,诺维奇梗 189 | n02094433,约克郡梗 190 | n02095314,刚毛猎狐梗 191 | n02095570,莱克兰梗 192 | n02095889,锡利哈姆梗 193 | n02096051,艾尔谷犬 194 | n02096177,凯恩梗 195 | n02096294,澳大利亚梗 196 | n02096437,丹迪丁蒙梗 197 | n02096585,波士顿梗 198 | n02097047,迷你雪纳瑞犬 199 | n02097130,巨型雪纳瑞犬 200 | n02097209,标准雪纳瑞犬 201 | n02097298,苏格兰梗 202 | n02097474,西藏梗 203 | n02097658,丝毛梗 204 | n02098105,软毛麦色梗 205 | n02098286,西高地白梗 206 | n02098413,拉萨阿普索犬 207 | n02099267,平毛寻回犬 208 | n02099429,卷毛寻回犬 209 | n02099601,金毛猎犬 210 | n02099712,拉布拉多猎犬 211 | n02099849,乞沙比克猎犬 212 | n02100236,德国短毛猎犬 213 | n02100583,维兹拉犬 214 | n02100735,英国谍犬 215 | n02100877,爱尔兰雪达犬 216 | n02101006,戈登雪达犬 217 | n02101388,布列塔尼犬猎犬 218 | n02101556,黄毛 219 | n02102040,英国史宾格犬 220 | n02102177,威尔士史宾格犬 221 | n02102318,可卡犬 222 | n02102480,萨塞克斯猎犬 223 | n02102973,爱尔兰水猎犬 224 | n02104029,哥威斯犬 225 | n02104365,舒柏奇犬 226 | n02105056,比利时牧羊犬 227 | n02105162,马里努阿犬 228 | n02105251,伯瑞犬 229 | n02105412,凯尔皮犬 230 | n02105505,匈牙利牧羊犬 231 | n02105641,老英国牧羊犬 232 | n02105855,喜乐蒂牧羊犬 233 | n02106030,牧羊犬 234 | n02106166,边境牧羊犬 235 | n02106382,法兰德斯牧牛狗 236 | n02106550,罗特韦尔犬 237 | n02106662,德国牧羊犬 238 | n02107142,多伯曼犬 239 | n02107312,迷你杜宾犬 240 | n02107574,大瑞士山地犬 241 | n02107683,伯恩山犬 242 | n02107908,Appenzeller狗 243 | n02108000,EntleBucher狗 244 | n02108089,拳师狗 245 | n02108422,斗牛獒 246 | n02108551,藏獒 247 | n02108915,法国斗牛犬 248 | n02109047,大丹犬 249 | n02109525,圣伯纳德狗 250 | n02109961,爱斯基摩犬 251 | n02110063,雪橇犬 252 | n02110185,哈士奇 253 | n02110341,达尔马提亚 254 | n02110627,狮毛狗 255 | n02110806,巴辛吉狗 256 | n02110958,哈巴狗 257 | n02111129,莱昂贝格狗 258 | n02111277,纽芬兰岛狗 259 | n02111500,大白熊犬 260 | n02111889,萨摩耶犬 261 | n02112018,博美犬 262 | n02112137,松狮 263 | n02112350,荷兰卷尾狮毛狗 264 | n02112706,布鲁塞尔格林芬犬 265 | n02113023,彭布洛克威尔士科基犬 266 | n02113186,威尔士柯基犬 267 | n02113624,玩具贵宾犬 268 | n02113712,迷你贵宾犬 269 | n02113799,标准贵宾犬 270 | n02113978,墨西哥无毛犬 271 | n02114367,灰狼 272 | n02114548,白狼 273 | n02114712,红太狼 274 | n02114855,狼 275 | n02115641,澳洲野狗 276 | n02115913,豺 277 | n02116738,非洲猎犬 278 | n02117135,鬣狗 279 | n02119022,红狐狸 280 | n02119789,沙狐 281 | n02120079,北极狐狸 282 | n02120505,灰狐狸 283 | n02123045,虎斑猫 284 | n02123159,山猫 285 | n02123394,波斯猫 286 | n02123597,暹罗暹罗猫 287 | n02124075,埃及猫 288 | n02125311,美洲狮 289 | n02127052,猞猁 290 | n02128385,豹子 291 | n02128757,雪豹 292 | n02128925,美洲虎 293 | n02129165,狮子 294 | n02129604,老虎 295 | n02130308,猎豹 296 | n02132136,棕熊 297 | n02133161,美洲黑熊 298 | n02134084,冰熊 299 | n02134418,懒熊 300 | n02137549,猫鼬 301 | n02138441,猫鼬 302 | n02165105,虎甲虫 303 | n02165456,瓢虫 304 | n02167151,土鳖虫 305 | n02168699,天牛 306 | n02169497,龟甲虫 307 | n02172182,粪甲虫 308 | n02174001,犀牛甲虫 309 | n02177972,象甲 310 | n02190166,苍蝇 311 | n02206856,蜜蜂 312 | n02219486,蚂蚁 313 | n02226429,蚱蜢 314 | n02229544,蟋蟀 315 | n02231487,竹节虫 316 | n02233338,蟑螂 317 | n02236044,螳螂 318 | n02256656,蝉 319 | n02259212,叶蝉 320 | n02264363,草蜻蛉 321 | n02268443,蜻蜓 322 | n02268853,豆娘 323 | n02276258,优红蛱蝶 324 | n02277742,小环蝴蝶 325 | n02279972,君主蝴蝶 326 | n02280649,菜粉蝶 327 | n02281406,白蝴蝶 328 | n02281787,灰蝶 329 | n02317335,海星 330 | n02319095,海胆 331 | n02321529,海参 332 | n02325366,野兔 333 | n02326432,兔 334 | n02328150,安哥拉兔 335 | n02342885,仓鼠 336 | n02346627,刺猬 337 | n02356798,黑松鼠 338 | n02361337,土拨鼠 339 | n02363005,海狸 340 | n02364673,豚鼠 341 | n02389026,栗色马 342 | n02391049,斑马 343 | n02395406,猪 344 | n02396427,野猪 345 | n02397096,疣猪 346 | n02398521,河马 347 | n02403003,牛 348 | n02408429,水牛 349 | n02410509,野牛 350 | n02412080,公羊 351 | n02415577,大角羊 352 | n02417914,山羊 353 | n02422106,狷羚 354 | n02422699,黑斑羚 355 | n02423022,瞪羚 356 | n02437312,阿拉伯单峰骆驼 357 | n02437616,骆驼 358 | n02441942,黄鼠狼 359 | n02442845,水貂 360 | n02443114,臭猫 361 | n02443484,黑足鼬 362 | n02444819,水獭 363 | n02445715,臭鼬 364 | n02447366,獾 365 | n02454379,犰狳 366 | n02457408,树懒 367 | n02480495,猩猩 368 | n02480855,大猩猩 369 | n02481823,黑猩猩 370 | n02483362,长臂猿 371 | n02483708,合趾猿长臂猿 372 | n02484975,长尾猴 373 | n02486261,赤猴 374 | n02486410,狒狒 375 | n02487347,恒河猴 376 | n02488291,白头叶猴 377 | n02488702,疣猴 378 | n02489166,长鼻猴 379 | n02490219,狨（美洲产小型长尾猴） 380 | n02492035,卷尾猴 381 | n02492660,吼猴 382 | n02493509,伶猴 383 | n02493793,蜘蛛猴 384 | n02494079,松鼠猴 385 | n02497673,马达加斯加环尾狐猴 386 | n02500267,大狐猴 387 | n02504013,印度大象 388 | n02504458,非洲象 389 | n02509815,小熊猫 390 | n02510455,大熊猫 391 | n02514041,杖鱼 392 | n02526121,鳗鱼 393 | n02536864,银鲑 394 | n02606052,三色刺蝶鱼 395 | n02607072,海葵鱼 396 | n02640242,鲟鱼 397 | n02641379,雀鳝 398 | n02643566,狮子鱼 399 | n02655020,河豚 400 | n02666196,算盘 401 | n02667093,长袍 402 | n02669723,学位袍 403 | n02672831,手风琴 404 | n02676566,原声吉他 405 | n02687172,航空母舰 406 | n02690373,客机 407 | n02692877,飞艇 408 | n02699494,祭坛 409 | n02701002,救护车 410 | n02704792,水陆两用车 411 | n02708093,模拟时钟 412 | n02727426,蜂房 413 | n02730930,围裙 414 | n02747177,垃圾桶 415 | n02749479,攻击步枪 416 | n02769748,背包 417 | n02776631,面包店 418 | n02777292,平衡木 419 | n02782093,热气球 420 | n02783161,圆珠笔 421 | n02786058,创可贴 422 | n02787622,班卓琴 423 | n02788148,栏杆 424 | n02790996,杠铃 425 | n02791124,理发师的椅子 426 | n02791270,理发店 427 | n02793495,牲口棚 428 | n02794156,晴雨表 429 | n02795169,圆筒 430 | n02797295,园地小车 431 | n02799071,棒球 432 | n02802426,篮球 433 | n02804414,婴儿床 434 | n02804610,巴松管 435 | n02807133,游泳帽 436 | n02808304,沐浴毛巾 437 | n02808440,浴缸 438 | n02814533,沙滩车 439 | n02814860,灯塔 440 | n02815834,高脚杯 441 | n02817516,熊皮高帽 442 | n02823428,啤酒瓶 443 | n02823750,啤酒杯 444 | n02825657,钟塔 445 | n02834397,（小儿用的）围嘴 446 | n02835271,串联自行车 447 | n02837789,比基尼 448 | n02840245,装订册 449 | n02841315,双筒望远镜 450 | n02843684,鸟舍 451 | n02859443,船库 452 | n02860847,雪橇 453 | n02865351,饰扣式领带 454 | n02869837,阔边女帽 455 | n02870880,书橱 456 | n02871525,书店 457 | n02877765,瓶盖 458 | n02879718,弓箭 459 | n02883205,蝴蝶结领结 460 | n02892201,铜制牌位 461 | n02892767,奶罩 462 | n02894605,防波堤 463 | n02895154,铠甲 464 | n02906734,扫帚 465 | n02909870,桶 466 | n02910353,扣环 467 | n02916936,防弹背心 468 | n02917067,动车 469 | n02927161,肉铺 470 | n02930766,出租车 471 | n02939185,大锅 472 | n02948072,蜡烛 473 | n02950826,大炮 474 | n02951358,独木舟 475 | n02951585,开瓶器 476 | n02963159,开衫 477 | n02965783,车镜 478 | n02966193,旋转木马 479 | n02966687,木匠的工具包 480 | n02971356,纸箱 481 | n02974003,车轮 482 | n02977058,取款机 483 | n02978881,盒式录音带 484 | n02979186,卡带播放器 485 | n02980441,城堡 486 | n02981792,双体船 487 | n02988304,CD播放器 488 | n02992211,大提琴 489 | n02992529,移动电话 490 | n02999410,铁链 491 | n03000134,围栏 492 | n03000247,链甲 493 | n03000684,电锯 494 | n03014705,箱子 495 | n03016953,衣柜 496 | n03017168,编钟 497 | n03018349,中国橱柜 498 | n03026506,圣诞袜 499 | n03028079,教堂 500 | n03032252,电影院 501 | n03041632,切肉刀 502 | n03042490,悬崖屋 503 | n03045698,斗篷 504 | n03047690,木屐 505 | n03062245,鸡尾酒调酒器 506 | n03063599,咖啡杯 507 | n03063689,咖啡壶 508 | n03065424,螺旋结构（楼梯） 509 | n03075370,组合锁 510 | n03085013,电脑键盘 511 | n03089624,糖果 512 | n03095699,集装箱船 513 | n03100240,敞篷车 514 | n03109150,开瓶器 515 | n03110669,短号 516 | n03124043,牛仔靴 517 | n03124170,牛仔帽 518 | n03125729,摇篮 519 | n03126707,起重机 520 | n03127747,头盔 521 | n03127925,板条箱 522 | n03131574,小儿床 523 | n03133878,砂锅 524 | n03134739,槌球 525 | n03141823,拐杖 526 | n03146219,胸甲 527 | n03160309,大坝 528 | n03179701,书桌 529 | n03180011,台式电脑 530 | n03187595,有线电话 531 | n03188531,尿布湿 532 | n03196217,数字时钟 533 | n03197337,数字手表 534 | n03201208,餐桌板 535 | n03207743,抹布 536 | n03207941,洗碗机 537 | n03208938,盘式制动器 538 | n03216828,码头 539 | n03218198,狗拉雪橇 540 | n03220513,圆顶 541 | n03223299,门垫 542 | n03240683,钻井平台 543 | n03249569,鼓 544 | n03250847,鼓槌 545 | n03255030,哑铃 546 | n03259280,荷兰烤箱 547 | n03271574,电风扇 548 | n03272010,电吉他 549 | n03272562,电力机车 550 | n03290653,电视 551 | n03291819,信封 552 | n03297495,浓缩咖啡机 553 | n03314780,扑面粉 554 | n03325584,女用长围巾 555 | n03337140,文件 556 | n03344393,消防船 557 | n03345487,消防车 558 | n03347037,火炉栏 559 | n03355925,旗杆 560 | n03372029,长笛 561 | n03376595,折叠椅 562 | n03379051,橄榄球头盔 563 | n03384352,叉车 564 | n03388043,喷泉 565 | n03388183,钢笔 566 | n03388549,有四根帷柱的床 567 | n03393912,运货车厢 568 | n03394916,圆号 569 | n03400231,煎锅 570 | n03404251,裘皮大衣 571 | n03417042,垃圾车 572 | n03424325,防毒面具 573 | n03425413,汽油泵 574 | n03443371,高脚杯 575 | n03444034,卡丁车 576 | n03445777,高尔夫球 577 | n03445924,高尔夫球车 578 | n03447447,狭长小船 579 | n03447721,锣 580 | n03450230,礼服 581 | n03452741,钢琴 582 | n03457902,温室 583 | n03459775,散热器格栅 584 | n03461385,杂货店 585 | n03467068,断头台 586 | n03476684,小发夹 587 | n03476991,头发喷雾 588 | n03478589,半履带装甲车 589 | n03481172,锤子 590 | n03482405,大篮子 591 | n03483316,手摇鼓风机 592 | n03485407,手提电脑 593 | n03485794,手帕 594 | n03492542,硬盘 595 | n03494278,口琴 596 | n03495258,竖琴 597 | n03496892,收割机 598 | n03498962,斧头 599 | n03527444,手枪皮套 600 | n03529860,家庭影院 601 | n03530642,蜂窝 602 | n03532672,钩爪 603 | n03534580,衬裙 604 | n03535780,单杠 605 | n03538406,马车 606 | n03544143,沙漏 607 | n03584254,iPod 608 | n03584829,熨斗 609 | n03590841,南瓜灯笼 610 | n03594734,牛仔裤 611 | n03594945,吉普车 612 | n03595614,运动衫 613 | n03598930,拼图 614 | n03599486,人力车 615 | n03602883,操纵杆 616 | n03617480,和服 617 | n03623198,护膝 618 | n03627232,蝴蝶结 619 | n03630383,大褂 620 | n03633091,长柄勺 621 | n03637318,灯罩 622 | n03642806,笔记本电脑 623 | n03649909,割草机 624 | n03657121,镜头盖 625 | n03658185,开信刀 626 | n03661043,图书馆 627 | n03662601,救生艇 628 | n03666591,点火器 629 | n03670208,豪华轿车 630 | n03673027,远洋班轮 631 | n03676483,唇膏 632 | n03680355,平底便鞋 633 | n03690938,洗剂 634 | n03691459,扬声器 635 | n03692522,放大镜 636 | n03697007,锯木厂 637 | n03706229,磁罗盘 638 | n03709823,邮袋 639 | n03710193,信箱 640 | n03710637,女游泳衣 641 | n03710721,有肩带浴衣 642 | n03717622,窨井盖 643 | n03720891,沙球（一种打击乐器） 644 | n03721384,马林巴木琴 645 | n03724870,面膜 646 | n03729826,火柴 647 | n03733131,花柱 648 | n03733281,迷宫 649 | n03733805,量杯 650 | n03742115,药箱 651 | n03743016,巨石 652 | n03759954,麦克风 653 | n03761084,微波炉 654 | n03763968,军装 655 | n03764736,奶桶 656 | n03769881,迷你巴士 657 | n03770439,迷你裙 658 | n03770679,面包车 659 | n03773504,导弹 660 | n03775071,连指手套 661 | n03775546,搅拌钵 662 | n03776460,活动房屋（由汽车拖拉的） 663 | n03777568,T型发动机小汽车 664 | n03777754,调制解调器 665 | n03781244,修道院 666 | n03782006,显示器 667 | n03785016,电瓶车 668 | n03786901,砂浆 669 | n03787032,学士 670 | n03788195,清真寺 671 | n03788365,蚊帐 672 | n03791053,摩托车 673 | n03792782,山地自行车 674 | n03792972,登山帐 675 | n03793489,鼠标 676 | n03794056,捕鼠器 677 | n03796401,搬家车 678 | n03803284,口套 679 | n03804744,钉子 680 | n03814639,颈托 681 | n03814906,项链 682 | n03825788,乳头（瓶） 683 | n03832673,笔记本 684 | n03837869,方尖碑 685 | n03838899,双簧管 686 | n03840681,陶笛 687 | n03841143,里程表 688 | n03843555,滤油器 689 | n03854065,风琴 690 | n03857828,示波器 691 | n03866082,罩裙 692 | n03868242,牛车 693 | n03868863,氧气面罩 694 | n03871628,包装 695 | n03873416,船桨 696 | n03874293,明轮 697 | n03874599,挂锁 698 | n03876231,画笔 699 | n03877472,睡衣 700 | n03877845,宫殿 701 | n03884397,排箫 702 | n03887697,纸巾 703 | n03888257,降落伞 704 | n03888605,双杠 705 | n03891251,公园长椅 706 | n03891332,停车收费表 707 | n03895866,客车 708 | n03899768,露台 709 | n03902125,付费电话 710 | n03903868,基座 711 | n03908618,铅笔盒 712 | n03908714,卷笔刀 713 | n03916031,香水（瓶） 714 | n03920288,培养皿 715 | n03924679,复印机 716 | n03929660,拨弦片 717 | n03929855,尖顶头盔 718 | n03930313,栅栏 719 | n03930630,皮卡 720 | n03933933,桥墩 721 | n03935335,存钱罐 722 | n03937543,药瓶 723 | n03938244,枕头 724 | n03942813,乒乓球 725 | n03944341,风车 726 | n03947888,海盗船 727 | n03950228,水罐 728 | n03954731,木工刨 729 | n03956157,天文馆 730 | n03958227,塑料袋 731 | n03961711,板架 732 | n03967562,犁型铲雪机 733 | n03970156,手压皮碗泵 734 | n03976467,宝丽来相机 735 | n03976657,电线杆 736 | n03977966,警车 737 | n03980874,雨披 738 | n03982430,台球桌 739 | n03983396,充气饮料瓶 740 | n03991062,花盆 741 | n03992509,陶工旋盘 742 | n03995372,电钻 743 | n03998194,祈祷垫 744 | n04004767,打印机 745 | n04005630,监狱 746 | n04008634,炮弹 747 | n04009552,投影仪 748 | n04019541,冰球 749 | n04023962,沙包 750 | n04026417,钱包 751 | n04033901,羽管笔 752 | n04033995,被子 753 | n04037443,赛车 754 | n04039381,球拍 755 | n04040759,散热器 756 | n04041544,收音机 757 | n04044716,射电望远镜 758 | n04049303,雨桶 759 | n04065272,休闲车 760 | n04067472,卷轴 761 | n04069434,反射式照相机 762 | n04070727,冰箱 763 | n04074963,遥控器 764 | n04081281,餐厅 765 | n04086273,左轮手枪 766 | n04090263,步枪 767 | n04099969,摇椅 768 | n04111531,电转烤肉架 769 | n04116512,橡皮 770 | n04118538,橄榄球 771 | n04118776,直尺 772 | n04120489,跑步鞋 773 | n04125021,保险柜 774 | n04127249,安全别针 775 | n04131690,盐瓶（调味用） 776 | n04133789,凉鞋 777 | n04136333,纱笼 778 | n04141076,萨克斯管 779 | n04141327,剑鞘 780 | n04141975,秤 781 | n04146614,校车 782 | n04147183,帆船 783 | n04149813,记分牌 784 | n04152593,屏幕 785 | n04153751,螺丝 786 | n04154565,螺丝刀 787 | n04162706,安全带 788 | n04179913,缝纫机 789 | n04192698,盾牌 790 | n04200800,皮鞋店 791 | n04201297,障子 792 | n04204238,购物篮 793 | n04204347,购物车 794 | n04208210,铁锹 795 | n04209133,浴帽 796 | n04209239,浴帘 797 | n04228054,滑雪板 798 | n04229816,滑雪面罩 799 | n04235860,睡袋 800 | n04238763,滑尺 801 | n04239074,滑动门 802 | n04243546,角子老虎机 803 | n04251144,潜水通气管 804 | n04252077,雪橇 805 | n04252225,扫雪机 806 | n04254120,皂液器 807 | n04254680,足球 808 | n04254777,袜子 809 | n04258138,碟式太阳能 810 | n04259630,宽边帽 811 | n04263257,汤碗 812 | n04264628,空格键 813 | n04265275,空间加热器 814 | n04266014,航天飞机 815 | n04270147,铲（搅拌或涂敷用的） 816 | n04273569,快艇 817 | n04275548,蜘蛛网 818 | n04277352,纺锤 819 | n04285008,跑车 820 | n04286575,聚光灯 821 | n04296562,舞台 822 | n04310018,蒸汽机车 823 | n04311004,钢拱桥 824 | n04311174,钢滚筒 825 | n04317175,听诊器 826 | n04325704,女用披肩 827 | n04326547,石头墙 828 | n04328186,秒表 829 | n04330267,火炉 830 | n04332243,过滤器 831 | n04335435,有轨电车 832 | n04336792,担架 833 | n04344873,沙发床 834 | n04346328,佛塔 835 | n04347754,潜艇 836 | n04350905,套装 837 | n04355338,日晷 838 | n04355933,太阳镜 839 | n04356056,太阳镜 840 | n04357314,防晒霜 841 | n04366367,悬索桥 842 | n04367480,拖把 843 | n04370456,运动衫 844 | n04371430,游泳裤 845 | n04371774,秋千 846 | n04372370,开关 847 | n04376876,注射器 848 | n04380533,台灯 849 | n04389033,坦克 850 | n04392985,磁带播放器 851 | n04398044,茶壶 852 | n04399382,泰迪 853 | n04404412,电视 854 | n04409515,网球 855 | n04417672,茅草 856 | n04418357,幕布 857 | n04423845,顶针 858 | n04428191,脱粒机 859 | n04429376,宝座 860 | n04435653,瓦屋顶 861 | n04442312,烤面包机 862 | n04443257,烟草店 863 | n04447861,马桶 864 | n04456115,火炬 865 | n04458633,图腾柱 866 | n04461696,拖车 867 | n04462240,玩具店 868 | n04465501,拖拉机 869 | n04467665,拖车 870 | n04476259,托盘 871 | n04479046,风衣 872 | n04482393,三轮车 873 | n04483307,三体船 874 | n04485082,三脚架 875 | n04486054,凯旋门 876 | n04487081,无轨电车 877 | n04487394,长号 878 | n04493381,浴盆 879 | n04501370,旋转式栅门 880 | n04505470,打字机键盘 881 | n04507155,伞 882 | n04509417,独轮车 883 | n04515003,直立式钢琴 884 | n04517823,真空吸尘器 885 | n04522168,花瓶 886 | n04523525,拱顶 887 | n04525038,天鹅绒 888 | n04525305,自动售货机 889 | n04532106,祭服 890 | n04532670,高架桥 891 | n04536866,小提琴 892 | n04540053,排球 893 | n04542943,松饼机 894 | n04548280,挂钟 895 | n04548362,钱包 896 | n04550184,衣柜 897 | n04552348,军用飞机 898 | n04553703,洗脸盆 899 | n04554684,洗衣机 900 | n04557648,水瓶 901 | n04560804,水壶 902 | n04562935,水塔 903 | n04579145,威士忌壶 904 | n04579432,哨子 905 | n04584207,假发 906 | n04589890,纱窗 907 | n04590129,百叶窗 908 | n04591157,温莎领带 909 | n04591713,葡萄酒瓶 910 | n04592741,飞机翅膀 911 | n04596742,炒菜锅 912 | n04597913,木制的勺子 913 | n04599235,毛织品 914 | n04604644,栅栏 915 | n04606251,沉船 916 | n04612504,双桅船 917 | n04613696,蒙古包 918 | n06359193,网站 919 | n06596364,漫画 920 | n06785654,纵横字谜 921 | n06794110,路标 922 | n06874185,交通信号灯 923 | n07248320,防尘罩 924 | n07565083,菜单 925 | n07579787,盘子 926 | n07583066,鳄梨酱 927 | n07584110,清汤 928 | n07590611,罐焖土豆烧肉 929 | n07613480,蛋糕 930 | n07614500,冰淇淋 931 | n07615774,雪糕 932 | n07684084,法式面包 933 | n07693725,百吉饼 934 | n07695742,椒盐脆饼 935 | n07697313,芝士汉堡 936 | n07697537,热狗 937 | n07711569,土豆泥 938 | n07714571,结球甘蓝 939 | n07714990,西兰花 940 | n07715103,菜花 941 | n07716358,绿皮密生西葫芦 942 | n07716906,西葫芦 943 | n07717410,小青南瓜 944 | n07717556,南瓜 945 | n07718472,黄瓜 946 | n07718747,朝鲜蓟 947 | n07720875,甜椒 948 | n07730033,刺棘蓟 949 | n07734744,蘑菇 950 | n07742313,绿苹果 951 | n07745940,草莓 952 | n07747607,橘子 953 | n07749582,柠檬 954 | n07753113,无花果 955 | n07753275,菠萝 956 | n07753592,香蕉 957 | n07754684,菠萝蜜 958 | n07760859,蛋奶冻苹果 959 | n07768694,石榴 960 | n07802026,干草 961 | n07831146,烤面条加干酪沙司 962 | n07836838,巧克力酱 963 | n07860988,面团 964 | n07871810,瑞士肉包 965 | n07873807,披萨 966 | n07875152,馅饼 967 | n07880968,卷饼 968 | n07892512,红葡萄酒 969 | n07920052,意大利浓咖啡 970 | n07930864,杯子 971 | n07932039,蛋酒 972 | n09193705,高山 973 | n09229709,泡泡 974 | n09246464,悬崖 975 | n09256479,珊瑚礁 976 | n09288635,间歇泉 977 | n09332890,湖边 978 | n09399592,海角 979 | n09421951,沙洲 980 | n09428293,海滨 981 | n09468604,峡谷 982 | n09472597,火山 983 | n09835506,棒球 984 | n10148035,新郎 985 | n10565667,潜水员 986 | n11879895,油菜 987 | n11939491,雏菊 988 | n12057211,杓兰 989 | n12144580,玉米 990 | n12267677,橡子 991 | n12620546,玫瑰果 992 | n12768682,七叶树果实 993 | n12985857,珊瑚菌 994 | n12998815,木耳 995 | n13037406,鹿花菌 996 | n13040303,鬼笔菌 997 | n13044778,地星 998 | n13052670,多叶奇果菌 999 | n13054560,牛肝菌 1000 | n13133613,玉米穗 1001 | n15075141,卫生纸 1002 | -------------------------------------------------------------------------------- /pic/init.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Saito912/Classify_GUI/603354a4eabf8a796d372062553ceeb3a77b7447/pic/init.png 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