f-rumblefish

## dataset.csv

          
            
             MNIST
             Fashion-MNIST
             Comment

            
              Training Dataset
               54000
               0
               data for training the autoencoder

            
              Validation Dataset
               6000
               0
               data for validating the autoencoder and defining the threshold

            
              Test Dataset
               500
               500
               data for testing the solution

## Performance Summary.csv

          
            Approach
                  Core Network
                        Tail Network
                     File
             Accuracy

            
              Multi-Input
                 3 Conv2D/MaxPooling CNN
               Dense(1024/512/256)
                101
                65%

            
              Multi-Input
                 MobileNet
                 ...
                      107

            
              Multi-Channel
               3 Conv2D/MaxPolling CNN
               ...
                                201
                22%

            
              Multi-Channel
               MobileNet
                             GAP(0124)/Dense(256)
               307
                100%

            
              Multi-Channel
               MobileNetV2
                           GAP(1024)/Dense(256)
               308
               2-->96%/10-->22%

## YOLOv3 Checklist.csv

          
            0. Source
                 https://github.com/qqwweee/keras-yolo3

            
              1. Training
                 1.1. create mydata_train.txt
               
               ref: train.txt

            
              
                            1.2. create mydata_class.txt
               
               ref: model_data/voc_classes.txt

            
              
                            1.3. change train.py to train_mydata.py
               1.3.1. annotation_path = 'mydata_train.txt'
               ref: 1.1

            
              
                                                        
               1.3.2. classes_path = 'model_data/mydata_classes.txt'
               ref: 1.2

            
              
                                                        
               1.3.3. create_model with load_pretrained=False
               optional (if pre-trained weight doesn't work)

            
              
                                                        
               1.3.4. model.save('mymodel.h5')
               save model and weight for prediction

            
              
                                                        
               1.3.5. change epochs from 50 to 5
               optional (check if it works or not)

            
              
                                                        
               1.3.6. change batch_size from 32 to 8
               optional (if it has some memory problems)

            
              
                                                        
               1.3.7. remove training in the second stage
               optional (if the firs

## MobileNet_log.txt
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
input_1 (InputLayer)         (None, 224, 224, 3)       0
_________________________________________________________________
conv1_pad (ZeroPadding2D)    (None, 225, 225, 3)       0
_________________________________________________________________
conv1 (Conv2D)               (None, 112, 112, 32)      864
_________________________________________________________________
conv1_bn (BatchNormalization (None, 112, 112, 32)      128

## Model_InceptionV3.py
from keras.applications.inception_v3 import InceptionV3
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D

# parameters for architecture
input_shape = (224, 224, 3)
num_classes = 6
conv_size = 32

# parameters for training

## Model_SimpleNet.py
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

# parameters for architecture
input_shape = (224, 224, 3)
num_classes = 6
conv_size = 32

# parameters for training
batch_size = 32

## Model_MobileNet.py
from keras.applications.mobilenet import MobileNet
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D

# parameters for architecture
input_shape = (224, 224, 3)
num_classes = 6
conv_size = 32

# parameters for training

## class_90.py
classes_90 = ["background", "person", "bicycle", "car", "motorcycle",
            "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant",
            "unknown", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse",
            "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "unknown", "backpack",
            "umbrella", "unknown", "unknown", "handbag", "tie", "suitcase", "frisbee", "skis",
            "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard",
            "surfboard", "tennis racket", "bottle", "unknown", "wine glass", "cup", "fork", "knife",
            "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog",
            "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "unknown", "dining table",
            "unknown", "unknown", "toilet", "unknown", "tv", "laptop", "mouse", "remote", "keyboard",

## class_80.py
classes_80 = ["person", "bicycle", "car", "motorcycle",
            "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant",
            "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse",
            "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack",
            "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis",
            "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard",
            "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife",
            "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog",
            "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table",
            "toilet", "tv", "laptop", "mouse", "remote", "keyboard",

## yolo_prediction.py
# loop 845 bounding boxes
for i in range(Net_YOLO_pred.shape[0]):
    # get the confidence on the object
    confidence_on_box = Net_YOLO_pred[i][4]
    # find the class index with the highest probability
    probability_list = Net_YOLO_pred[i][5:]
    class_index = probability_list.argmax(axis=0)
    probability_on_class = probability_list[class_index]
    # get the score
    score = confidence_on_box * probability_on_class