updated README for Custom Classification and Training

OlafenwaMoses · Jan 5, 2021 · 94a2d22 · 94a2d22
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diff --git a/examples/custom_full_model_prediction.py b/examples/custom_full_model_prediction.py
diff --git a/examples/custom_model_continuos_training.py b/examples/custom_model_continuos_training.py
@@ -1,8 +1,8 @@
-from imageai.Prediction.Custom import ModelTraining
+from imageai.Classification.Custom import ClassificationModelTrainer
 import os
 
-trainer = ModelTraining()
-trainer.setModelTypeAsDenseNet()
+trainer = ClassificationModelTrainer()
+trainer.setModelTypeAsDenseNet121()
 trainer.setDataDirectory("idenprof")
 trainer.trainModel(num_objects=10, num_experiments=50, enhance_data=True, batch_size=8, show_network_summary=True, continue_from_model="idenprof_densenet-0.763500.h5") # Download the model via this link https://github.com/OlafenwaMoses/ImageAI/releases/tag/models-v3
 
diff --git a/examples/custom_model_convert_deepstack.py b/examples/custom_model_convert_deepstack.py
diff --git a/examples/custom_model_convert_to_tensorflow.py b/examples/custom_model_convert_to_tensorflow.py
diff --git a/examples/custom_model_prediction.py b/examples/custom_model_prediction.py
@@ -1,15 +1,15 @@
-from imageai.Prediction.Custom import CustomImagePrediction
+from imageai.Classification.Custom import CustomImageClassification
 import os
 
 execution_path = os.getcwd()
 
-prediction = CustomImagePrediction()
-prediction.setModelTypeAsResNet()
-prediction.setModelPath(os.path.join(execution_path, "idenprof_resnet.h5")) # Download the model via this link https://github.com/OlafenwaMoses/ImageAI/releases/tag/models-v3
+prediction = CustomImageClassification()
+prediction.setModelTypeAsResNet50()
+prediction.setModelPath(os.path.join(execution_path, "idenprof_resnet_ex-056_acc-0.993062.h5")) # Download the model via this link https://github.com/OlafenwaMoses/ImageAI/releases/tag/models-v3
 prediction.setJsonPath(os.path.join(execution_path, "idenprof.json"))
 prediction.loadModel(num_objects=10)
 
-predictions, probabilities = prediction.predictImage(os.path.join(execution_path, "9.jpg"), result_count=5)
+predictions, probabilities = prediction.classifyImage(os.path.join(execution_path, "9.jpg"), result_count=5)
 
 for eachPrediction, eachProbability in zip(predictions, probabilities):
     print(eachPrediction , " : " , eachProbability)
diff --git a/examples/custom_model_training.py b/examples/custom_model_training.py
@@ -1,7 +1,7 @@
-from imageai.Prediction.Custom import ModelTraining
+from imageai.Classification.Custom import ClassificationModelTrainer
 
 
-model_trainer = ModelTraining()
-model_trainer.setModelTypeAsResNet()
+model_trainer = ClassificationModelTrainer()
+model_trainer.setModelTypeAsResNet50()
 model_trainer.setDataDirectory("idenprof")
 model_trainer.trainModel(num_objects=10, num_experiments=200, enhance_data=True, batch_size=32, show_network_summary=True)
diff --git a/examples/custom_model_transfer_learning_training.py b/examples/custom_model_transfer_learning_training.py
@@ -1,7 +1,7 @@
-from imageai.Prediction.Custom import ModelTraining
+from imageai.Classification.Custom import ClassificationModelTrainer
 import os
 
-trainer = ModelTraining()
-trainer.setModelTypeAsResNet()
+trainer = ClassificationModelTrainer()
+trainer.setModelTypeAsResNet50()
 trainer.setDataDirectory("idenprof")
 trainer.trainModel(num_objects=10, num_experiments=50, enhance_data=True, batch_size=16, show_network_summary=True,transfer_from_model="resnet50_weights_tf_dim_ordering_tf_kernels.h5", initial_num_objects=1000) # Download the model via this link https://github.com/OlafenwaMoses/ImageAI/releases/tag/models-v3
diff --git a/examples/custom_multiple_models_predict.py b/examples/custom_multiple_models_predict.py
diff --git a/examples/detection_config.json b/examples/detection_config.json
diff --git a/examples/image_prediction.py b/examples/image_prediction.py
@@ -1,13 +1,13 @@
-from imageai.Prediction import ImagePrediction
+from imageai.Classification import ImageClassification
 import os
 
 execution_path = os.getcwd()
 
-prediction = ImagePrediction()
-prediction.setModelTypeAsResNet()
-prediction.setModelPath(os.path.join(execution_path, "resnet50_weights_tf_dim_ordering_tf_kernels.h5")) # Download the model via this link https://github.com/OlafenwaMoses/ImageAI/releases/tag/1.0
+prediction = ImageClassification()
+prediction.setModelTypeAsResNet50()
+prediction.setModelPath(os.path.join(execution_path, "resnet50_imagenet_tf.2.0.h5")) # Download the model via this link https://github.com/OlafenwaMoses/ImageAI/releases/tag/1.0
 prediction.loadModel()
 
-predictions, probabilities = prediction.predictImage(os.path.join(execution_path, "1.jpg"), result_count=10)
+predictions, probabilities = prediction.classifyImage(os.path.join(execution_path, "1.jpg"), result_count=10)
 for eachPrediction, eachProbability in zip(predictions, probabilities):
     print(eachPrediction , " : " , eachProbability)