f-rumblefish’s gists

f-rumblefish / YOLOv3 Checklist.csv

Last active June 17, 2019 02:21

A Checklist for Training YOLOv3

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

f-rumblefish / MobileNet_log.txt

Last active February 7, 2019 12:17

	_________________________________________________________________
	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

f-rumblefish / Model_InceptionV3.py

Last active October 7, 2019 01:35

	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

f-rumblefish / Model_SimpleNet.py

Created February 7, 2019 01:17

	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

f-rumblefish / Model_MobileNet.py

Last active November 20, 2019 01:07

	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

f-rumblefish / class_90.py

Created October 23, 2018 15:57

	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",

f-rumblefish / class_80.py

Created October 23, 2018 15:56

	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",

f-rumblefish / yolo_prediction.py

Last active October 23, 2018 15:43

YOLOv2 Prediction

	# 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

f-rumblefish / ssd_prediction.py

Created October 23, 2018 15:23

SSD/MobileNet Prediction

f-rumblefish / object_detection_ssd.py

Last active October 23, 2018 01:59

Object Detection via SSD/MobileNet

	pb = 'frozen_inference_graph.pb'
	pbt = 'ssd_mobilenet_v1_coco_2017_11_17.pbtxt'

	Net_SSD = cv.dnn.readNetFromTensorflow(pb,pbt)

	Net_SSD.setInput(cv.dnn.blobFromImage(img, 1.0/127.5, (300, 300), (127.5, 127.5, 127.5), swapRB = True, crop = False))

	Net_SSD_pred = Net_SSD.forward()

	print(">>> SSD/MobileNet prediction shape = ", Net_SSD_pred.shape)