takane revised this on 9 Jul 2023

turi2.py

import turicreate as tc import re # データセットの読み込み train_data = tc.image_analysis.load_images('./Train9' , with_path=True) # データセットの読み込み test_data = tc.image_analysis.load_images('./Test9' , with_path=True) # ラベル作成の関数 def makelabel(path): if re.search(r'/beaker', path): return 'beaker' elif re.search(r'/crucible', path): return 'crucible' elif re.search(r'/flask', path): return 'flask' elif re.search(r'/funnel', path): return 'funnel' elif re.search(r'/test tube', path): return 'test tube' elif re.search(r'/burette', path): return 'burette' elif re.search(r'/Eggplantflask', path): return 'Eggplantflask' elif re.search(r'/glassrod', path): return 'glassrod' elif re.search(r'/Komagomepipette', path): return 'Komagomepipette' elif re.search(r'/Messcylinder', path): return 'Messcylinder' elif re.search(r'/Messflask', path): return 'Messflask' elif re.search(r'/Messpipette', path): return 'Messpipette' elif re.search(r'/Petridish', path): return 'Petridish' elif re.search(r'/thermometer', path): return 'thermometer' elif re.search(r'/vollpipette', path): return 'vollpipette' else: return 'Watchglass' #パス名からラベルを生成 train_data['label'] = train_data['path'].apply(lambda path: makelabel(path)) #パス名からラベルを生成 test_data['label'] = test_data['path'].apply(lambda path: makelabel(path)) train_data.explore() #学習 model = tc.image_classifier.create(train_data, target= 'label', model='squeezenet_v1.1', max_iterations=100) # 評価 metrics = model.evaluate(test_data) # 評価データの正解率 print(metrics['accuracy']) # TuriCreateモデルの保存 model.save('./ImageClassification.model') # Core ML形式のモデルの保存 model.export_coreml('./ImageClassification.mlmodel')