from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="Vier_Onderdelen_Wit")
trainer.setTrainConfig(
object_names_array=["Schoepen_groot", "Schijf_zilver", "Kom", "Rotor"],
batch_size=4,
num_experiments=100,
train_from_pretrained_model="pretrained-yolov3.h5")
trainer.trainModel()
trainer.setDataDirectory(data_directory="Vier_Onderdelen_Wit")
evaluate = trainer.evaluateModel(
model_path="Vier_Onderdelen_Wit/models",
json_path="Vier_Onderdelen_Wit/json/detection_config.json",
iou_threshold=0.5,
object_threshold=0.3,
nms_threshold=0.5)
x = []
for i in range(len(evaluate)):
map = evaluate[i]['map']
experiment = evaluate[i]['model_file']
x.append((map, experiment))
x.sort()
text_file = open("maps_vier_onderdelen_wit.txt", "wt")
for i in x:
e = text_file.writelines(str(i) + "\n")
text_file.close()
from imageai.Detection.Custom import DetectionModelTrainer
'''
["Rick Owens Ramones", "Rick Owens Geobaskets", "Rick Owens x Adidas Runners", "Rick Owens x Adidas Tech Runners", "Rick Owens x Adidas Level Runners", "Balenciaga Arenas", "Balenciaga Speed Sneaker", "Balenciaga Tripple S", "Common Project Achillies Low", "Common Project Chelsea Boot", "Dr Martens 1460"]
'''
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="shoetect")
trainer.setTrainConfig(object_names_array=[
"Rick Owens Ramones", "Rick Owens Geobaskets",
"Rick Owens x Adidas Runners", "Rick Owens x Adidas Tech Runners",
"Rick Owens x Adidas Level Runners", "Balenciaga Arenas",
"Balenciaga Speed Sneaker", "Balenciaga Tripple S",
"Common Project Achillies Low", "Common Project Chelsea Boot",
"Dr Martens 1460"
],
batch_size=4,
num_experiments=50,
train_from_pretrained_model="pretrained-yolov3.h5")
trainer.trainModel()
from imageai.Detection.Custom import DetectionModelTrainer
# Initialize the model trainer and set it to YOLO (YOLOv3 is optional)
train = DetectionModelTrainer()
train.setModelTypeAsYOLOv3()
# Choose the name of your directory
d_name = "tires"
train.setDataDirectory(data_directory=d_name)
train.setTrainConfig(object_names_array=[d_name],
batch_size=4,
num_experiments=203)
train.trainModel()
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(
data_directory="/content/drive/My Drive/project data sets/data_cleaned")
trainer.evaluateModel(
model_path="/content/drive/My Drive/project data sets/data_cleaned/models",
json_path=
"/content/drive/My Drive/project data sets/data_cleaned/json/detection_config.json",
iou_threshold=0.5,
object_threshold=0.3,
nms_threshold=0.5)
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="SymDescribeImages")
metrics = trainer.evaluateModel(
model_path="SymDescribeImages/models",
json_path="SymDescribeImages/json/detection_config.json",
iou_threshold=0.5,
object_threshold=0.3,
nms_threshold=0.5)
print(metrics)
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="pallet")
trainer.setTrainConfig(object_names_array=["pallet"],
batch_size=10,
num_experiments=150,
train_from_pretrained_model="pretrained-yolov3.h5")
trainer.trainModel()
# -*- coding: utf-8 -*- #Run this in colab or in a jupyter notebook """Prerequisites""" !pip3 install tensorflow-gpu==1.13.1 !pip3 install imageai --upgrade !wget https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/pretrained-yolov3.h5 """Change data_directory and object_names_array appropriately""" from imageai.Detection.Custom import DetectionModelTrainer trainer = DetectionModelTrainer() trainer.setModelTypeAsYOLOv3() trainer.setDataDirectory(data_directory="/content/drive/My Drive/Football/") trainer.setTrainConfig(object_names_array=["Bundesliga", "EPL", "LaLiga"], batch_size=4, num_experiments=7, train_from_pretrained_model="pretrained-yolov3.h5") trainer.trainModel() """Pick the model with maximum mAP""" from imageai.Detection.Custom import DetectionModelTrainer trainer = DetectionModelTrainer() trainer.setModelTypeAsYOLOv3() trainer.setDataDirectory(data_directory="/content/drive/My Drive/Football") trainer.evaluateModel(model_path="/content/drive/My Drive/Football/models", json_path="/content/drive/My Drive/Football/json/detection_config.json", iou_threshold=0.5, object_threshold=0.3, nms_threshold=0.5)
# get the absolute image path for the image from the annotation
img_abs_path = os.path.join(images_directory, img_filename)
if os.path.isfile(img_abs_path):
# image corresponding to the annotation exists
# print(img_abs_path + " exists")
pass
else:
print(img_abs_path + " does not exists")
os.remove(abs_annotation_file_path)
print(abs_annotation_file_path + " Deleted")
print(count)
!wget https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/pretrained-yolov3.h5 -P '/gdrive/My Drive/BE Project 2019 - 2020/final_training_folder/models/'
import os
from imageai.Detection.Custom import DetectionModelTrainer
model_trainer = DetectionModelTrainer()
model_trainer.setModelTypeAsYOLOv3()
model_trainer.setDataDirectory(data_directory_path)
yolo_path = os.path.join(os.path.join(execution_path, "models"), "pretrained-yolov3.h5")
model_trainer.setTrainConfig(object_names_array=["glass", "metal", "paper", "plastic"], batch_size=10, num_experiments=50, train_from_pretrained_model=yolo_path)
model_trainer.trainModel()
from imageai.Detection.Custom import DetectionModelTrainer
dataset_name = 'dataset'
names = ['on', 'off']
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory=dataset_name)
metrics = trainer.evaluateModel(model_path=dataset_name + "/models",
json_path=dataset_name +
"/json/detection_config.json",
iou_threshold=0.5,
object_threshold=0.3,
nms_threshold=0.5)
print(metrics)
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="hololens") # dataSet path on drive
trainer.setTrainConfig(
object_names_array=["hololens"], # Symbol name
batch_size=4,
num_experiments=100,
train_from_pretrained_model="pretrained-yolov3.h5")
trainer.trainModel()
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="smoker")
trainer.setTrainConfig(object_names_array=["tobacco"],
batch_size=4,
num_experiments=200,
train_from_pretrained_model="pretrained-yolov3.h5")
#download pre-trained model via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/pretrained-yolov3.h5
# If you are training to detect more than 1 object, set names of objects above like object_names_array=["hololens", "google-glass", "oculus", "magic-leap"]
trainer.trainModel()
"""
SAMPLE RESULT
Using TensorFlow backend.
Generating anchor boxes for training images and annotation...
Average IOU for 9 anchors: 0.78
Anchor Boxes generated.
Detection configuration saved in hololens/json/detection_config.json
Training on: ['hololens']
Training with Batch Size: 4
Number of Experiments: 200
Epoch 1/200
- 733s - loss: 34.8253 - yolo_layer_1_loss: 6.0920 - yolo_layer_2_loss: 11.1064 - yolo_layer_3_loss: 17.6269 - val_loss: 20.5028 - val_yolo_layer_1_loss: 4.0171 - val_yolo_layer_2_loss: 7.5175 - val_yolo_layer_3_loss: 8.9683
Epoch 2/200
- 648s - loss: 11.1396 - yolo_layer_1_loss: 2.1209 - yolo_layer_2_loss: 4.0063 - yolo_layer_3_loss: 5.0124 - val_loss: 7.6188 - val_yolo_layer_1_loss: 1.8513 - val_yolo_layer_2_loss: 2.2446 - val_yolo_layer_3_loss: 3.5229
Epoch 3/200
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="custom_detect_train_data")
trainer.setTrainConfig(object_names_array=["wn"],
batch_size=4,
num_experiments=200,
train_from_pretrained_model="pretrained-yolov3.h5")
#download pre-trained model via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/pretrained-yolov3.h5
# If you are training to detect more than 1 object, set names of objects above like object_names_array=["hololens", "google-glass", "oculus", "magic-leap"]
trainer.trainModel()
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="barbijo")
trainer.evaluateModel(model_path="barbijo/models",
json_path="barbijo/json/detection_config.json",
iou_threshold=0.5,
object_threshold=0.3,
nms_threshold=0.5)
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="kangaroo")
trainer.setTrainConfig(object_names_array=["kangaroo"],
batch_size=8,
num_experiments=50,
train_from_pretrained_model="pretrained-yolov3.h5")
trainer.trainModel()
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 24 15:38:42 2019
@author: Gayatri
"""
from imageai.Detection.Custom import DetectionModelTrainer
import os
os.chdir('F:\Internship_CV\ImageAI_Model')
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory='hololens')
trainer.setTrainConfig(object_names_array=['hololens'],
batch_size=4,
num_experiments=20,
train_from_pretrained_model='pretrained-yolov3.h5')
trainer.trainModel()
from imageai.Detection.Custom import DetectionModelTrainer #Setup the trainer and use model Yolov3 trainer = DetectionModelTrainer() trainer.setModelTypeAsYOLOv3() #Set Data directory folder trainer.setDataDirectory(data_directory="tomato") #Objects, batch & experiments + pre-existing model # batch_size = number of training examples used in one iteration # num_experiments = number of iterations trainer.setTrainConfig(object_names_array=["red_cherry_tomato", "orange_cherry_tomato", "yellow_cherry_tomato", "green_cherry_tomato"], batch_size=1, num_experiments=1, train_from_pretrained_model="C:\\Users\\Max\\Desktop\\superAI\\tomato\\models\\detection_model-ex-013--loss-0108.641.h5") #Start training trainer.trainModel()
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="drone")
trainer.setTrainConfig(object_names_array=[
"person", "light_motor_vehicle", "heavy_motor_vehicle"
],
batch_size=4,
num_experiments=200,
train_from_pretrained_model="pretrained-yolov3.h5")
#download pre-trained model via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/pretrained-yolov3.h5
# If you are training to detect more than 1 object, set names of objects above like object_names_array=["hololens", "google-glass", "oculus", "magic-leap"]
trainer.trainModel()
"""
SAMPLE RESULT
Using TensorFlow backend.
Generating anchor boxes for training images and annotation...
Average IOU for 9 anchors: 0.78
Anchor Boxes generated.
Detection configuration saved in hololens/json/detection_config.json
Training on: ['hololens']
Training with Batch Size: 4
Number of Experiments: 200
Epoch 1/200
- 733s - loss: 34.8253 - yolo_layer_1_loss: 6.0920 - yolo_layer_2_loss: 11.1064 - yolo_layer_3_loss: 17.6269 - val_loss: 20.5028 - val_yolo_layer_1_loss: 4.0171 - val_yolo_layer_2_loss: 7.5175 - val_yolo_layer_3_loss: 8.9683
Epoch 2/200
from imageai.Detection.Custom import DetectionModelTrainer trainer = DetectionModelTrainer() trainer.setModelTypeAsYOLOv3() trainer.setDataDirectory(data_directory="dataset") metrics = trainer.evaluateModel(model_path="dataset/models", json_path="dataset/json/detection_config.json", iou_threshold=0.5, object_threshold=0.3, nms_threshold=0.5) print(metrics)
import os
import glob
from imageai.Detection.Custom import DetectionModelTrainer
execution_path = os.getcwd()
models_path = os.path.join(execution_path, "doge-identification/models/")
data_path = os.path.join(execution_path, "doge-identification/")
# This will force tensorflow to run on the cpu
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
list_of_files = glob.glob(models_path + "*.h5")
latest_model_path = max(list_of_files, key=os.path.getctime)
path, newest_model = os.path.split(latest_model_path)
print("Newest Model: ", newest_model)
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory=data_path)
metrics = trainer.evaluateModel(
model_path=latest_model_path,
json_path=os.path.join(execution_path, "doge-identification/json/detection_config.json"),
iou_threshold=0.5,
object_threshold=0.3,
nms_threshold=0.5)
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(
data_directory="C:/Users/ABC/Desktop/New folder/TDAI/Data/hololens")
trainer.evaluateModel(
model_path="C:/Users/ABC/Desktop/New folder/TDAI/Data/hololens/models",
json_path=
"C:/Users/ABC/Desktop/New folder/TDAI/Data/hololens/json/detection_config.json",
iou_threshold=0.5,
object_threshold=0.3,
nms_threshold=0.5)
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="../data/")
metrics = trainer.evaluateModel(
model_path="../models/detection_model-ex-015--loss-0004.540.h5",
json_path="../json/detection_config.json",
iou_threshold=0.5,
object_threshold=0.3,
nms_threshold=0.5,
)
print(metrics)
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="hololens")
trainer.evaluateModel(model_path="hololens-ex-60--loss-2.76.h5",
json_path="detection_config.json",
iou_threshold=0.5,
object_threshold=0.3,
nms_threshold=0.5)
# download JSON file via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/detection_config.json
# download detection model via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/hololens-ex-60--loss-2.76.h5
"""
SAMPLE RESULT
Model File: hololens_detection_model-ex-09--loss-4.01.h5
Using IoU : 0.5
Using Object Threshold : 0.3
Using Non-Maximum Suppression : 0.5
hololens: 0.9613
mAP: 0.9613
===============================
"""
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="data/camembert")
trainer.setTrainConfig(object_names_array=["camembert"],
batch_size=4,
num_experiments=100,
train_from_pretrained_model="pretrained-yolov3.h5")
trainer.trainModel()
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="ImageDataSet")
trainer.setTrainConfig(object_names_array=["SwimmingPool"],
batch_size=4,
num_experiments=200,
train_from_pretrained_model="yolov3.h5")
trainer.trainModel()
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer = DetectionModelTrainer()
trainer.setDataDirectory(data_directory="headsets2")
trainer.setTrainConfig(object_names_array=["surfer"],
batch_size=4,
num_experiments=5,
train_from_pretrained_model=
"/Users/hollands/dev/cfmodelserver/models/yolo-tiny.h5")
trainer.trainModel()
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="apple_dataset")
trainer.setTrainConfig(object_names_array=["apple", "damaged_apple"],
batch_size=8,
num_experiments=50,
train_from_pretrained_model="pretrained-yolov3.h5")
trainer.trainModel()
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="characters")
trainer.setTrainConfig(object_names_array=[
'aeolianDune', 'beach', 'crevasseRiverBedLagDeposit',
'crevasseRiverChannelBar', 'distalBar', 'distributaryChannelFill',
'distributaryMouthBar', 'floodPlain', 'floodSwamp',
'interdistributaryEstuary', 'majorRiverBedLagDeposit',
'majorRiverChannelBar', 'meanderPointBar', 'mouthBar',
'proximalDeepseaFan', 'underwaterDistributaryChannel',
'underwaterNaturalLevee'
],
batch_size=2,
num_experiments=10,
train_from_pretrained_model="pretrained-yolov3.h5")
trainer.trainModel()
metrics = trainer.evaluateModel(
model_path="characters/models",
json_path="characters/json/detection_config.json",
iou_threshold=0.5,
object_threshold=0.3,
nms_threshold=0.5)
print(metrics)
from imageai.Detection.Custom import DetectionModelTrainer trainer = DetectionModelTrainer() trainer.setModelTypeAsYOLOv3() trainer.setDataDirectory(data_directory="qr") metrics = trainer.evaluateModel(model_path="detection_model-ex-001--loss-0000.000.h5", json_path="detection_config.json", iou_threshold=0.5, object_threshold=0.3, nms_threshold=0.5)
from imageai.Detection.Custom import DetectionModelTrainer
names = ['on', 'off']
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory='dataset')
trainer.setTrainConfig(object_names_array=names,
batch_size=4,
num_experiments=100,
train_from_pretrained_model="pretrained-yolov3.h5")
trainer.trainModel()
