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--- |
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language: |
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- en |
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- zh |
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license: cc-by-nc-nd-4.0 |
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size_categories: |
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- 10K<n<100K |
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task_categories: |
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- image-classification |
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pretty_name: Orchid2024 |
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dataset_info: |
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- config_name: Orchid2024 |
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features: |
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- name: Class_Name |
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dtype: string |
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- name: Img |
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dtype: image |
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- name: Label |
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dtype: |
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class_label: |
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names: |
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'0': class0000 |
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'1': class0001 |
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'2': class0002 |
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'3': class0003 |
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'4': class0004 |
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'5': class0005 |
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'6': class0006 |
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'7': class0007 |
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'8': class0008 |
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'9': class0009 |
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'10': class0010 |
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'11': class0011 |
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'12': class0012 |
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'13': class0013 |
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'14': class0014 |
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'15': class0015 |
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'16': class0016 |
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'17': class0017 |
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'18': class0018 |
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'19': class0019 |
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'20': class0020 |
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'21': class0021 |
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'22': class0022 |
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'23': class0023 |
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'24': class0024 |
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'25': class0025 |
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'26': class0026 |
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'27': class0027 |
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'28': class0028 |
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'29': class0029 |
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'30': class0030 |
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'31': class0031 |
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'32': class0032 |
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'33': class0033 |
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'34': class0034 |
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'35': class0035 |
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'36': class0036 |
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'37': class0037 |
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'38': class0038 |
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'39': class0039 |
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'40': class0040 |
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'41': class0041 |
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'42': class0042 |
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'44': class0044 |
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'45': class0045 |
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|
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|
'51': class0051 |
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'52': class0052 |
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|
'55': class0055 |
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|
'56': class0056 |
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|
'57': class0057 |
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|
'60': class0060 |
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'61': class0061 |
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'62': class0062 |
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'89': class0089 |
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'90': class0090 |
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'91': class0091 |
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'92': class0092 |
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'93': class0093 |
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'94': class0094 |
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'95': class0095 |
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'96': class0096 |
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'97': class0097 |
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'98': class0098 |
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'99': class0099 |
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'100': class0100 |
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'101': class0101 |
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'102': class0102 |
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'103': class0103 |
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'104': class0104 |
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'105': class0105 |
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'106': class0106 |
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'107': class0107 |
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'108': class0108 |
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'109': class0109 |
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'110': class0110 |
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'111': class0111 |
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'112': class0112 |
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'113': class0113 |
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'114': class0114 |
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'115': class0115 |
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'116': class0116 |
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'117': class0117 |
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'118': class0118 |
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'119': class0119 |
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'120': class0120 |
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'121': class0121 |
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'122': class0122 |
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'123': class0123 |
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'124': class0124 |
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'125': class0125 |
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'126': class0126 |
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'127': class0127 |
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'128': class0128 |
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'129': class0129 |
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'130': class0130 |
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'131': class0131 |
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'132': class0132 |
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'133': class0133 |
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'134': class0134 |
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|
'135': class0135 |
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'136': class0136 |
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'172': class0172 |
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'173': class0173 |
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'174': class0174 |
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'175': class0175 |
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'176': class0176 |
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'177': class0177 |
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'178': class0178 |
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'179': class0179 |
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'180': class0180 |
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'181': class0181 |
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'182': class0182 |
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'183': class0183 |
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'184': class0184 |
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'185': class0185 |
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'186': class0186 |
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'187': class0187 |
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'188': class0188 |
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'189': class0189 |
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'190': class0190 |
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'191': class0191 |
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'192': class0192 |
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'193': class0193 |
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'194': class0194 |
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'195': class0195 |
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'196': class0196 |
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'197': class0197 |
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'198': class0198 |
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'199': class0199 |
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'200': class0200 |
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'201': class0201 |
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'202': class0202 |
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'203': class0203 |
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'204': class0204 |
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'205': class0205 |
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'206': class0206 |
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'207': class0207 |
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'208': class0208 |
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'209': class0209 |
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'210': class0210 |
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'211': class0211 |
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'212': class0212 |
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'213': class0213 |
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'214': class0214 |
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'215': class0215 |
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'216': class0216 |
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'217': class0217 |
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'218': class0218 |
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'219': class0219 |
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'220': class0220 |
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'221': class0221 |
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'222': class0222 |
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'223': class0223 |
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'225': class0225 |
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'226': class0226 |
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'227': class0227 |
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'229': class0229 |
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'230': class0230 |
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'231': class0231 |
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'232': class0232 |
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'233': class0233 |
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'234': class0234 |
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'235': class0235 |
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'236': class0236 |
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'239': class0239 |
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'240': class0240 |
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'242': class0242 |
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'243': class0243 |
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'244': class0244 |
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'245': class0245 |
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'247': class0247 |
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'249': class0249 |
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'250': class0250 |
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'261': class0261 |
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'263': class0263 |
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'265': class0265 |
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'266': class0266 |
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'271': class0271 |
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'272': class0272 |
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'273': class0273 |
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'274': class0274 |
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'275': class0275 |
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'276': class0276 |
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'277': class0277 |
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'278': class0278 |
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'279': class0279 |
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'280': class0280 |
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'281': class0281 |
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'282': class0282 |
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'283': class0283 |
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'284': class0284 |
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'285': class0285 |
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'286': class0286 |
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'287': class0287 |
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'288': class0288 |
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'289': class0289 |
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'290': class0290 |
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'291': class0291 |
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'292': class0292 |
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'293': class0293 |
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'294': class0294 |
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'295': class0295 |
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'296': class0296 |
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'297': class0297 |
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'300': class0300 |
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'301': class0301 |
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'302': class0302 |
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'307': class0307 |
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'318': class0318 |
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'319': class0319 |
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'320': class0320 |
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|
'321': class0321 |
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'322': class0322 |
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|
'323': class0323 |
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'324': class0324 |
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|
'325': class0325 |
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'326': class0326 |
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'327': class0327 |
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'328': class0328 |
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'329': class0329 |
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'331': class0331 |
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'332': class0332 |
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'333': class0333 |
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'334': class0334 |
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'336': class0336 |
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'337': class0337 |
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'340': class0340 |
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'341': class0341 |
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'342': class0342 |
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'343': class0343 |
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'345': class0345 |
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'346': class0346 |
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'347': class0347 |
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'348': class0348 |
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'350': class0350 |
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'351': class0351 |
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'352': class0352 |
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'353': class0353 |
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'354': class0354 |
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'355': class0355 |
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'356': class0356 |
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|
'357': class0357 |
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|
'358': class0358 |
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|
'359': class0359 |
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|
'360': class0360 |
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|
'361': class0361 |
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|
'362': class0362 |
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|
'363': class0363 |
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|
'364': class0364 |
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|
'365': class0365 |
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|
'366': class0366 |
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|
'367': class0367 |
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|
'368': class0368 |
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|
'369': class0369 |
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|
'370': class0370 |
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|
'371': class0371 |
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|
'372': class0372 |
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|
'373': class0373 |
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|
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|
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|
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|
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|
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|
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|
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|
'381': class0381 |
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|
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|
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|
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|
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|
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|
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|
'392': class0392 |
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'393': class0393 |
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|
'394': class0394 |
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'395': class0395 |
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|
'396': class0396 |
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|
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|
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|
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|
|
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|
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'402': class0402 |
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'403': class0403 |
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'405': class0405 |
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|
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|
'407': class0407 |
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|
'408': class0408 |
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|
'409': class0409 |
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'410': class0410 |
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|
'411': class0411 |
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'412': class0412 |
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|
'413': class0413 |
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|
'414': class0414 |
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|
'415': class0415 |
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|
'416': class0416 |
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|
'417': class0417 |
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|
'418': class0418 |
|
|
'419': class0419 |
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|
'420': class0420 |
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|
'421': class0421 |
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|
'422': class0422 |
|
|
'423': class0423 |
|
|
'424': class0424 |
|
|
'425': class0425 |
|
|
'426': class0426 |
|
|
'427': class0427 |
|
|
'428': class0428 |
|
|
'429': class0429 |
|
|
'430': class0430 |
|
|
'431': class0431 |
|
|
'432': class0432 |
|
|
'433': class0433 |
|
|
'434': class0434 |
|
|
'435': class0435 |
|
|
'436': class0436 |
|
|
'437': class0437 |
|
|
'438': class0438 |
|
|
'439': class0439 |
|
|
'440': class0440 |
|
|
'441': class0441 |
|
|
'442': class0442 |
|
|
'443': class0443 |
|
|
'444': class0444 |
|
|
'445': class0445 |
|
|
'446': class0446 |
|
|
'447': class0447 |
|
|
'448': class0448 |
|
|
'449': class0449 |
|
|
'450': class0450 |
|
|
'451': class0451 |
|
|
'452': class0452 |
|
|
'453': class0453 |
|
|
'454': class0454 |
|
|
'455': class0455 |
|
|
'456': class0456 |
|
|
'457': class0457 |
|
|
'458': class0458 |
|
|
'459': class0459 |
|
|
'460': class0460 |
|
|
'461': class0461 |
|
|
'462': class0462 |
|
|
'463': class0463 |
|
|
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|
|
'465': class0465 |
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
'482': class0482 |
|
|
'483': class0483 |
|
|
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|
|
'485': class0485 |
|
|
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|
|
'487': class0487 |
|
|
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|
|
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|
|
'490': class0490 |
|
|
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|
|
'492': class0492 |
|
|
'493': class0493 |
|
|
'494': class0494 |
|
|
'495': class0495 |
|
|
'496': class0496 |
|
|
'497': class0497 |
|
|
'498': class0498 |
|
|
'499': class0499 |
|
|
'500': class0500 |
|
|
'501': class0501 |
|
|
'502': class0502 |
|
|
'503': class0503 |
|
|
'504': class0504 |
|
|
'505': class0505 |
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|
'506': class0506 |
|
|
'507': class0507 |
|
|
'508': class0508 |
|
|
'509': class0509 |
|
|
'510': class0510 |
|
|
'511': class0511 |
|
|
'512': class0512 |
|
|
'513': class0513 |
|
|
'514': class0514 |
|
|
'515': class0515 |
|
|
'516': class0516 |
|
|
'517': class0517 |
|
|
'518': class0518 |
|
|
'519': class0519 |
|
|
'520': class0520 |
|
|
'521': class0521 |
|
|
'522': class0522 |
|
|
'523': class0523 |
|
|
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|
|
'525': class0525 |
|
|
'526': class0526 |
|
|
'527': class0527 |
|
|
'528': class0528 |
|
|
'529': class0529 |
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|
|
'1065': class1065 |
|
|
'1066': class1066 |
|
|
'1067': class1067 |
|
|
'1068': class1068 |
|
|
'1069': class1069 |
|
|
'1070': class1070 |
|
|
'1071': class1071 |
|
|
'1072': class1072 |
|
|
'1073': class1073 |
|
|
'1074': class1074 |
|
|
'1075': class1075 |
|
|
'1076': class1076 |
|
|
'1077': class1077 |
|
|
'1078': class1078 |
|
|
'1079': class1079 |
|
|
'1080': class1080 |
|
|
'1081': class1081 |
|
|
'1082': class1082 |
|
|
'1083': class1083 |
|
|
'1084': class1084 |
|
|
'1085': class1085 |
|
|
'1086': class1086 |
|
|
'1087': class1087 |
|
|
'1088': class1088 |
|
|
'1089': class1089 |
|
|
'1090': class1090 |
|
|
'1091': class1091 |
|
|
'1092': class1092 |
|
|
'1093': class1093 |
|
|
'1094': class1094 |
|
|
'1095': class1095 |
|
|
'1096': class1096 |
|
|
'1097': class1097 |
|
|
'1098': class1098 |
|
|
'1099': class1099 |
|
|
'1100': class1100 |
|
|
'1101': class1101 |
|
|
'1102': class1102 |
|
|
'1103': class1103 |
|
|
'1104': class1104 |
|
|
'1105': class1105 |
|
|
'1106': class1106 |
|
|
'1107': class1107 |
|
|
'1108': class1108 |
|
|
'1109': class1109 |
|
|
'1110': class1110 |
|
|
'1111': class1111 |
|
|
'1112': class1112 |
|
|
'1113': class1113 |
|
|
'1114': class1114 |
|
|
'1115': class1115 |
|
|
'1116': class1116 |
|
|
'1117': class1117 |
|
|
'1118': class1118 |
|
|
'1119': class1119 |
|
|
'1120': class1120 |
|
|
'1121': class1121 |
|
|
'1122': class1122 |
|
|
'1123': class1123 |
|
|
'1124': class1124 |
|
|
'1125': class1125 |
|
|
'1126': class1126 |
|
|
'1127': class1127 |
|
|
'1128': class1128 |
|
|
'1129': class1129 |
|
|
'1130': class1130 |
|
|
'1131': class1131 |
|
|
'1132': class1132 |
|
|
'1133': class1133 |
|
|
'1134': class1134 |
|
|
'1135': class1135 |
|
|
'1136': class1136 |
|
|
'1137': class1137 |
|
|
'1138': class1138 |
|
|
'1139': class1139 |
|
|
'1140': class1140 |
|
|
'1141': class1141 |
|
|
'1142': class1142 |
|
|
'1143': class1143 |
|
|
'1144': class1144 |
|
|
'1145': class1145 |
|
|
'1146': class1146 |
|
|
'1147': class1147 |
|
|
'1148': class1148 |
|
|
'1149': class1149 |
|
|
'1150': class1150 |
|
|
'1151': class1151 |
|
|
'1152': class1152 |
|
|
'1153': class1153 |
|
|
'1154': class1154 |
|
|
'1155': class1155 |
|
|
'1156': class1156 |
|
|
'1157': class1157 |
|
|
'1158': class1158 |
|
|
'1159': class1159 |
|
|
'1160': class1160 |
|
|
'1161': class1161 |
|
|
'1162': class1162 |
|
|
'1163': class1163 |
|
|
'1164': class1164 |
|
|
'1165': class1165 |
|
|
'1166': class1166 |
|
|
'1167': class1167 |
|
|
'1168': class1168 |
|
|
'1169': class1169 |
|
|
'1170': class1170 |
|
|
'1171': class1171 |
|
|
'1172': class1172 |
|
|
'1173': class1173 |
|
|
'1174': class1174 |
|
|
'1175': class1175 |
|
|
'1176': class1176 |
|
|
'1177': class1177 |
|
|
'1178': class1178 |
|
|
'1179': class1179 |
|
|
'1180': class1180 |
|
|
'1181': class1181 |
|
|
'1182': class1182 |
|
|
'1183': class1183 |
|
|
'1184': class1184 |
|
|
'1185': class1185 |
|
|
'1186': class1186 |
|
|
'1187': class1187 |
|
|
'1188': class1188 |
|
|
'1189': class1189 |
|
|
'1190': class1190 |
|
|
'1191': class1191 |
|
|
'1192': class1192 |
|
|
'1193': class1193 |
|
|
'1194': class1194 |
|
|
'1195': class1195 |
|
|
'1196': class1196 |
|
|
'1197': class1197 |
|
|
'1198': class1198 |
|
|
'1199': class1199 |
|
|
'1200': class1200 |
|
|
'1201': class1201 |
|
|
'1202': class1202 |
|
|
'1203': class1203 |
|
|
'1204': class1204 |
|
|
'1205': class1205 |
|
|
'1206': class1206 |
|
|
'1207': class1207 |
|
|
'1208': class1208 |
|
|
'1209': class1209 |
|
|
'1210': class1210 |
|
|
'1211': class1211 |
|
|
'1212': class1212 |
|
|
'1213': class1213 |
|
|
'1214': class1214 |
|
|
'1215': class1215 |
|
|
'1216': class1216 |
|
|
'1217': class1217 |
|
|
'1218': class1218 |
|
|
'1219': class1219 |
|
|
'1220': class1220 |
|
|
'1221': class1221 |
|
|
'1222': class1222 |
|
|
'1223': class1223 |
|
|
'1224': class1224 |
|
|
'1225': class1225 |
|
|
'1226': class1226 |
|
|
'1227': class1227 |
|
|
'1228': class1228 |
|
|
'1229': class1229 |
|
|
'1230': class1230 |
|
|
'1231': class1231 |
|
|
'1232': class1232 |
|
|
'1233': class1233 |
|
|
'1234': class1234 |
|
|
'1235': class1235 |
|
|
'1236': class1236 |
|
|
'1237': class1237 |
|
|
'1238': class1238 |
|
|
'1239': class1239 |
|
|
'1240': class1240 |
|
|
'1241': class1241 |
|
|
'1242': class1242 |
|
|
'1243': class1243 |
|
|
'1244': class1244 |
|
|
'1245': class1245 |
|
|
'1246': class1246 |
|
|
'1247': class1247 |
|
|
'1248': class1248 |
|
|
'1249': class1249 |
|
|
'1250': class1250 |
|
|
'1251': class1251 |
|
|
'1252': class1252 |
|
|
'1253': class1253 |
|
|
'1254': class1254 |
|
|
'1255': class1255 |
|
|
'1256': class1256 |
|
|
'1257': class1257 |
|
|
'1258': class1258 |
|
|
'1259': class1259 |
|
|
'1260': class1260 |
|
|
'1261': class1261 |
|
|
'1262': class1262 |
|
|
'1263': class1263 |
|
|
'1264': class1264 |
|
|
'1265': class1265 |
|
|
'1266': class1266 |
|
|
'1267': class1267 |
|
|
'1268': class1268 |
|
|
'1269': class1269 |
|
|
'1270': class1270 |
|
|
'1271': class1271 |
|
|
'1272': class1272 |
|
|
'1273': class1273 |
|
|
'1274': class1274 |
|
|
- name: Cultivar_Name |
|
|
dtype: string |
|
|
- name: Species_Name |
|
|
dtype: string |
|
|
- name: Chinese_Cultivar_Name |
|
|
dtype: string |
|
|
- name: Chinese_Species_Name |
|
|
dtype: string |
|
|
configs: |
|
|
- config_name: Orchid2024 |
|
|
data_files: |
|
|
- split: train |
|
|
path: dataset/train.parquet |
|
|
- split: validation |
|
|
path: dataset/validation.parquet |
|
|
- split: test |
|
|
path: dataset/test.parquet |
|
|
tags: |
|
|
- classification |
|
|
- plant |
|
|
- fine-grained image classification |
|
|
--- |
|
|
|
|
|
# Dataset Card for Orchid2024 |
|
|
|
|
|
 |
|
|
|
|
|
The Orchid2024 dataset is a fine-grained classification dataset specifically designed for cultivars of Chinese Cymbidium orchids (Chinese orchids). The dataset's samples come from 20 cities across 12 provincial-level administrative regions in China, covering 1,269 cultivars from 8 Cymbidium species, and 6 additional categories, totaling 156,630 images. The dataset nearly includes all common Chinese orchid cultivars currently found in China. Its fine-grained nature and focus on real-world scenarios make it a unique and practical resource for scientific research and real-world applications. |
|
|
|
|
|
The dataset contained in the current repository is a compressed version of Orchid2024 data (the maximum side length of 512 pixels). **The full version can be found in the Orchid2024 dataset's GitHub repository: [Orchid2024 Repository](https://github.com/pengyingshu/Orchid2024)**. The dataset authors achieved the highest Top-1 accuracy of 86.14% and a Top-5 accuracy of 95.44% on this dataset. |
|
|
|
|
|
|
|
|
## Dataset Description |
|
|
|
|
|
- **Homepage:** [Orchid2024 Homepage](https://pengyingshu.github.io/orchid/Orchid2024.html) |
|
|
- **Repository:** [Orchid2024 Repository](https://github.com/pengyingshu/Orchid2024) |
|
|
- **Paper:** [Orchid2024 Paper](https://plantmethods.biomedcentral.com/articles/10.1186/s13007-024-01252-w) |
|
|
|
|
|
## Dataset Features |
|
|
|
|
|
+ Most existing flower or fine-grained datasets are only aimed at species-level identification (such as distinguishing different types of orchids), while Orchid2024 extends fine-grained classification to the cultivar level, i.e., detecting different cultivars within orchid species. |
|
|
+ Due to the high ornamental value of Chinese orchids and the large differences in value between cultivars, the dataset exhibits a significant long-tail distribution. The number of images for each cultivar in this dataset ranges from 10 to 1387, which is basically consistent with their market popularity: common cultivars have abundant images, while rare cultivars have relatively fewer. |
|
|
+ The Orchid2024 dataset features a two-level fine-grained classification system of species and cultivars, which can accurately identify subtle visual differences between different cultivars. This dataset covers 8 orchid species and their subordinate cultivars, some of which have significant differences, while others have similar forms; and within the same species, the difficulty of distinguishing cultivars is even higher. |
|
|
|
|
|
## Dataset Format and Structure |
|
|
|
|
|
### Data Format |
|
|
|
|
|
The Orchid2024 dataset contains three parts: training images, training information files, and data description files. The training images are divided into training, validation, and test sets in a ratio of 6:2:2, specifically including 94,036 training samples, 31,297 validation samples, and 31,297 test samples. Each set is organized by category in the form of subfolders, covering a total of 1,275 categories, with each subfolder corresponding to a category, which stores JPG format images of the same category. |
|
|
|
|
|
### Dataset Loading Method |
|
|
|
|
|
You can load the specified data yourself according to actual needs. The loading method example is as follows: |
|
|
|
|
|
```python |
|
|
from datasets import load_dataset |
|
|
|
|
|
dataset = load_dataset("HelloPlant/Orchid2024") |
|
|
# check dataset splits |
|
|
print(dataset.keys()) |
|
|
# View the first sample in the train set |
|
|
print(dataset["train"][0]) |
|
|
print(f"Dataset size: {len(dataset['train'])}") |
|
|
|
|
|
# Iterate through each image in the test dataset |
|
|
for data in dataset['test']: |
|
|
# Get training data |
|
|
img = data['Img'] # Get image data |
|
|
label = data['Label'] # Category label ID corresponding to the image |
|
|
# Get attribute data attached to the image |
|
|
class_name = data['Class_Name'] # Get class name |
|
|
cultivar_name = data['Cultivar_Name'] # Get cultivar name (English) |
|
|
species_name = data['Species_Name'] # Get species name (English) |
|
|
Chinese_cultivar_name = data['Chinese_Cultivar_Name'] # Get cultivar name (Chinese) |
|
|
Chinese_species_name = data['Chinese_Species_Name'] # Get species name (Chinese) |
|
|
# Perform subsequent processing... |
|
|
break # Only display the first piece of data |
|
|
``` |
|
|
|
|
|
## Citation Information |
|
|
|
|
|
If this dataset is helpful to you, please cite the following related paper: |
|
|
|
|
|
```BibTeX |
|
|
@article{Peng2024Orchid, |
|
|
title={Orchid2024: A cultivar-level dataset and methodology for fine-grained classification of Chinese Cymbidium Orchids}, |
|
|
author={Peng, Yingshu and Zhou, Yuxia and Zhang, Li and Fu, Hongyan and Tang, Guimei and Huang, Guolin and Li, Weidong}, |
|
|
journal={Plant Methods}, |
|
|
volume={20}, |
|
|
number={1}, |
|
|
pages={124}, |
|
|
year={2024}, |
|
|
} |
|
|
``` |
