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[net] | |
# Testing | |
# batch=1 | |
# subdivisions=1 | |
# Training | |
batch=6 | |
subdivisions=1 | |
width=608 | |
height=608 | |
channels=3 |
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/content/images/190596972_AMI022888__.jpg | |
/content/images/190580794_AMI273089__2.jpg | |
/content/images/190581042_AMI106796__.jpg | |
/content/images/190581081_AMI063674__.jpg | |
/content/images/190583060_AMI271026__.jpg | |
/content/images/190587883_AMI091414__.jpg | |
/content/images/190499166_AMI182030__.jpg | |
/content/images/190578129_AMI279791__1.jpg | |
/content/images/190578129_AMI279791__2.jpg | |
/content/images/190594877_AMI280017__.jpg |
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/content/images/190561621_AMI083265__.jpg | |
/content/images/190565558_AMI059039__.jpg | |
/content/images/190587038_AMI005897__1.jpg | |
/content/images/190587038_AMI005897__2.jpg | |
/content/images/190399663_AMI216505__2.jpg | |
/content/images/190408911_AMI061137__1.jpg | |
/content/images/190450134_AMI077993__.jpg | |
/content/images/190439933_AMI275885__.jpg | |
/content/images/190400375_AMI109769__.jpg | |
/content/images/190420713_AMI275404__.jpg |
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classes= 15 | |
train = /content/train.txt | |
valid = /content/val.txt | |
names = /content/voc.names | |
backup = backup |
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单证名称Document Type | |
件数No. of Packages | |
毛重Gross Weight | |
体积Cube | |
包装 Packages | |
目的港 Final Destination | |
装运港 Place of Loading | |
发货人 Shipper | |
收货人 Consignee | |
通知人 Notify Party |
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[net] | |
# Testing | |
# batch=1 | |
# subdivisions=1 | |
# Training | |
batch=64 | |
subdivisions=16 | |
width=608 | |
height=608 | |
channels=3 |
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shipper | |
consignee | |
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classes= 3 | |
train = /home/botree/study/amass2/YOLO/model_training/merge_26/train.txt | |
valid = /home/botree/study/amass2/YOLO/model_training/merge_26/val.txt | |
names = /home/botree/study/amass2/YOLO/model_training/voc.names | |
backup = backupyol |
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import cv2 | |
import numpy as np | |
import pytesseract | |
# Load your trained model. | |
net = cv2.dnn.readNet("/home/botree/study/amass2/YOLO/models/yolov3-data_final_26.weights","/home/botree/study/amass2/YOLO/model_training/yolov3-data.cfg") | |
print(net) | |
classes = ["shipper","consignee","email"] |
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from detectron2.data import DatasetCatalog, MetadataCatalog | |
for type in ['train','val']: | |
DatasetCatalog.register("amass_images_"+type, lambda type=type: get_amass_dicts(type)) | |
MetadataCatalog.get("amass_images_" + type).set(thing_classes=["shipper", "consignee", "email"]) | |
amass_metadata = MetadataCatalog.get("amass_images_train") |
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