Comments (2)
script to download dataset
#!/usr/bin/env bash
## DOWNLOAD from JOSEPHS WEBSITE (SLOWER DOWNLOAD)
#wget https://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar
#wget https://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
#wget https://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
## OR DOWNLOAD FROM HERE (FASTER DOWNLOAD)
# VOC2007 DATASET
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.ta
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar #
# VOC2012 DATASET
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.ta
# Extract tar files
tar xf VOCtrainval_11-May-2012.tar
tar xf VOCtrainval_06-Nov-2007.tar
tar xf VOCtest_06-Nov-2007.tar
# Need voc_label.py to clean up data from xml files
wget https://pjreddie.com/media/files/voc_label.py
# Run python file to clean data from xml files
python voc_label.py
# Get train by using train+val from 2007 and 2012
# Then we only test on 2007 test set
# Unclear from paper what they actually just as a dev set
cat 2007_train.txt 2007_val.txt 2012_*.txt > train.txt
cp 2007_test.txt test.txt
# Move txt files we won't be using to clean up a little bit
mkdir old_txt_files
mv 2007* 2012* old_txt_files/
python generate_csv.py
mkdir data
mkdir data/images
mkdir data/labels
cp VOCdevkit/*.jpg data/images/
cp VOCdevkit/VOC2007/labels/*.txt data/labels/
cp VOCdevkit/VOC2012/labels/*.txt data/labels/
mkdir data
mkdir data/images
mkdir data/labels
mv VOCdevkit/VOC2007/JPEGImages/*.jpg data/images/
mv VOCdevkit/VOC2012/JPEGImages/*.jpg data/images/
mv VOCdevkit/VOC2007/labels/*.txt data/labels/
mv VOCdevkit/VOC2012/labels/*.txt data/labels/
# We don't need VOCdevkit folder anymore, can remove
# in order to save some space
rm -rf VOCdevkit/
mv test.txt old_txt_files/
mv train.txt old_txt_files/
from yolov1.
create train test csv files
import csv
read_train = open("train.txt", "r").readlines()
with open("train.csv", mode="w", newline="") as train_file:
for line in read_train:
image_file = line.split("/")[-1].replace("\n", "")
text_file = image_file.replace(".jpg", ".txt")
data = [image_file, text_file]
writer = csv.writer(train_file)
writer.writerow(data)
read_train = open("test.txt", "r").readlines()
with open("test.csv", mode="w", newline="") as train_file:
for line in read_train:
image_file = line.split("/")[-1].replace("\n", "")
text_file = image_file.replace(".jpg", ".txt")
data = [image_file, text_file]
writer = csv.writer(train_file)
writer.writerow(data)
from yolov1.
Related Issues (4)
- yolo loss function HOT 1
- train, evaluate HOT 4
- YOLO architecture HOT 2
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