def train_traffic_net(): download_traffic_net() trainer = ModelTraining() trainer.setModelTypeAsResNet() trainer.setDataDirectory("trafficnet_dataset_v1") trainer.trainModel(num_objects=4, num_experiments=200, batch_size=32, save_full_model=True, enhance_data=True)
def ImageREcognition(): model_trainer = ModelTraining() model_trainer.setModelTypeAsResNet() model_trainer.setDataDirectory("idenprof") model_trainer.trainModel(num_objects=10, num_experiments=100, enhance_data=True, batch_size=32, show_network_summary=True)
def main(): model_trainer = ModelTraining() model_trainer.setModelTypeAsResNet() model_trainer.setDataDirectory("data/images") model_trainer.trainModel(num_objects=2, num_experiments=20, enhance_data=True, batch_size=16, show_network_summary=True)
def main(path_data): model_trainer = ModelTraining() model_trainer.setModelTypeAsResNet() model_trainer.setDataDirectory(path_data) model_trainer.trainModel(num_objects=10, num_experiments=20, enhance_data=True, batch_size=32, show_network_summary=True)
def training(image_directory): model_trainer = ModelTraining() model_trainer.setModelTypeAsResNet() model_trainer.setDataDirectory(training_image_directory) model_trainer.trainModel(num_objects=1, num_experiments=200, enhance_data=True, batch_size=5, show_network_summary=True)
def train_func(n): model_trainer = ModelTraining() model_trainer.setModelTypeAsResNet() model_trainer.setDataDirectory('Dataset') model_trainer.trainModel(num_objects=n, num_experiments=100, enhance_data=True, batch_size=32, show_network_summary=True)
def modelIrain(dataDir='data', classNum=2, epochs=100, batch_size=32): ''' 模型训练部分 ''' #创建了ModelTraining类的新实例 model_trainer = ModelTraining() #将模型类型设置为ResNet model_trainer.setModelTypeAsResNet() #设置我们想要训练的数据集的路径 model_trainer.setDataDirectory(dataDir) #模型训练 ''' num_objects:该参数用于指定图像数据集中对象的数量 num_experiments:该参数用于指定将对图像训练的次数,也称为epochs enhance_data(可选):该参数用于指定是否生成训练图像的副本以获得更好的性能 batch_size:该参数用于指定批次数量。由于内存限制,需要分批训练,直到所有批次训练集都完成为止。 show_network_summary:该参数用于指定是否在控制台中显示训练的过程 ''' model_trainer.trainModel(num_objects=classNum, num_experiments=epochs, enhance_data=True, batch_size=batch_size, show_network_summary=True) print('Model Train Finished!!!') def modelPredict(model_path='data/models/model_ex-001_acc-0.500000.h5', class_path='data/json/model_class.json', pic_path='a.jpg', classNum=2, resNum=5): ''' 模型预测部分 prediction_speed[模型加载的速度]:fast faster fastest ''' prediction = CustomImagePrediction() prediction.setModelTypeAsResNet() prediction.setModelPath(model_path) prediction.setJsonPath(class_path) prediction.loadModel(num_objects=classNum, prediction_speed='fastest') prediction, probabilities = prediction.predictImage( pic_path, result_count=resNum) for eachPrediction, eachProbability in zip(predictions, probabilities): print(eachPrediction + " : " + str(eachProbability)) if __name__ == '__main__': #模型训练 modelTrain(dataDir='data', classNum=2, epochs=10, batch_size=8) #模型识别预测 modelPredict(model_path='data/models/model_ex-001_acc-0.500000.h5', class_path='data/json/model_class.json', pic_path='test.jpg', classNum=2, resNum=5)
def train_model(foldername,Model_Type,num_objects=2, num_experiments=1, enhance_data=False, batch_size=1, show_network_summary=True): model_trainer = ModelTraining() if Model_Type in "ResNet": model_trainer.setModelTypeAsResNet() elif Model_Type in "SqueezeNet": model_trainer.setModelTypeAsSqueezeNet() elif Model_Type in "InceptionV3": model_trainer.setModelTypeAsInceptionV3() elif Model_Type in "DenseNet": model_trainer.setModelTypeAsDenseNet() model_trainer.setDataDirectory(foldername) model_trainer.trainModel(num_objects=num_objects, num_experiments=num_experiments, enhance_data=enhance_data, batch_size=batch_size, show_network_summary=show_network_summary)
def test_resnet_training(): trainer = ModelTraining() trainer.setModelTypeAsResNet() trainer.setDataDirectory(data_directory=sample_dataset) trainer.trainModel(num_objects=10, num_experiments=1, enhance_data=True, batch_size=16, show_network_summary=True) assert os.path.isdir(sample_dataset_json_folder) assert os.path.isdir(sample_dataset_models_folder) assert os.path.isfile( os.path.join(sample_dataset_json_folder, "model_class.json")) assert (len(os.listdir(sample_dataset_models_folder)) > 0) shutil.rmtree(os.path.join(sample_dataset_json_folder)) shutil.rmtree(os.path.join(sample_dataset_models_folder))
def TrainModel(files, Classes, Epochs, BatchSize): model_trainer = ModelTraining() #create an instance for de model training model_trainer.setModelTypeAsResNet( ) #set model to ResNet NN (SqueezeNet, ResNet, InceptionV3 and DenseNet) model_trainer.setDataDirectory( files) #folder that contains train and test sets ''' train model function number_objects : number of clases num_experiments : number of iterations (epochs) Enhance_data (Optional) : if true, creates modified copies of the images to maximize accuracy (but more process cost) batch_size: number of images that the model trainer will study at once Show_network_summary (Optional) : if true, shows the structure of the model type ''' model_trainer.trainModel(num_objects=Classes, num_experiments=Epochs, enhance_data=True, batch_size=BatchSize, show_network_summary=True)
from imageai.Prediction.Custom import ModelTraining model_trainer = ModelTraining() model_trainer.setModelTypeAsResNet() model_trainer.setDataDirectory(r"D:/python/imageAI_model_train/pets") model_trainer.trainModel(num_objects=2, num_experiments=100, enhance_data=True, batch_size=16, show_network_summary=True)
from imageai.Prediction.Custom import ModelTraining model_trainer = ModelTraining( ) # type of training algorithm in this case basic NN model_trainer.setModelTypeAsResNet( ) # defines the type of model that will be stored, in this case it will be a simple .h5 file model_trainer.setDataDirectory( "idenprof") # where the AI will look for data to train it model_trainer.trainModel(num_objects=2, num_experiments=200, batch_size=32, show_network_summary=True) # num_objects is the total number of different types of objects, eg: chef, car, cat # num_experiments is the total number of epochs or the total number of times an "experiment" is run # batch_size is the total number of images tested in an epoch
from imageai.Prediction.Custom import ModelTraining # Train using the images found in the data subdirectory trainer = ModelTraining() trainer.setModelTypeAsResNet() trainer.setDataDirectory("data") trainer.trainModel( num_objects=15, num_experiments=50, enhance_data=True, save_full_model=True, batch_size=25, show_network_summary=True, transfer_from_model="resnet50_weights_tf_dim_ordering_tf_kernels.h5", initial_num_objects=1000, transfer_with_full_training=True)
from imageai.Prediction.Custom import ModelTraining model_trainer = ModelTraining() model_trainer.setModelTypeAsResNet() # others include SqueezeNet, ResNet, InceptionV3 and DenseNet model_trainer.setDataDirectory("idenprof") # dataset model_trainer.trainModel(num_objects=10, # prediction classes num_experiments=200, enhance_data=True, # allow data augmentation batch_size=32, # images per cycle show_network_summary=True)
extract1.extractall(DATASET_TRAIN_DIR) extract1.close() print("Extracting idenprof-train2.zip") extract2 = ZipFile(TRAIN_ZIP_TWO) extract2.extractall(DATASET_TRAIN_DIR) extract2.close() if(len(os.listdir(DATASET_TEST_DIR)) < 10): if (os.path.exists(TEST_ZIP) == False): print("Downloading idenprof-test.zip") data = requests.get("https://github.com/OlafenwaMoses/IdenProf/releases/download/v1.0/idenprof-test.zip", stream=True) with open(TEST_ZIP, "wb") as file: shutil.copyfileobj(data.raw, file) del data print("Extracting idenprof-test.zip") extract = ZipFile(TEST_ZIP) extract.extractall(DATASET_TEST_DIR) extract.close() model_trainer = ModelTraining() model_trainer.setModelTypeAsResNet() model_trainer.setDataDirectory(DATASET_DIR) model_trainer.trainModel(num_objects=10, num_experiments=100, enhance_data=True, batch_size=32, show_network_summary=True)
from imageai.Prediction.Custom import ModelTraining model_trainer = ModelTraining( ) ##create an instance of the ModelTraining class model_trainer.setModelTypeAsResNet( ) ##set your instance property and start the traning process. ##this function sets the model type of the training instance you created to the ResNet ##model, which means the ResNet algorithm will be trained on your dataset model_trainer.setDataDirectory( r"C:\Users\Andreas Thoma\Desktop\Mathimata\EPL445\Project\Tortillas") ## accepts a string which must be the path to the folder that contains the test and train subfolder of your image dataset model_trainer.trainModel(num_objects=2, num_experiments=5, enhance_data=True, batch_size=32, show_network_summary=True) ##this is the function that starts the training process. Once it starts, it will create a JSON file in ##the dataset/json folder (e.g Tortillas/json) which contains the mapping of the classes of the dataset. ##The JSON file will be used during custom prediction to produce reults