YOLOv5 v6.0 compatibility update (#1857)
* Initial commit * Initial commit * Cleanup * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix precommit errors * Remove TF builds from CI * export last.pt * Created using Colaboratory * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
Executable
+18
@@ -0,0 +1,18 @@
|
||||
#!/bin/bash
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Download latest models from https://github.com/ultralytics/yolov3/releases
|
||||
# Example usage: bash path/to/download_weights.sh
|
||||
# parent
|
||||
# └── yolov3
|
||||
# ├── yolov3.pt ← downloads here
|
||||
# ├── yolov3-spp.pt
|
||||
# └── ...
|
||||
|
||||
python - <<EOF
|
||||
from utils.downloads import attempt_download
|
||||
|
||||
models = ['yolov3', 'yolov3-spp', 'yolov3-tiny']
|
||||
for x in models:
|
||||
attempt_download(f'{x}.pt')
|
||||
|
||||
EOF
|
||||
@@ -1,61 +0,0 @@
|
||||
#!/bin/bash
|
||||
# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
|
||||
# Download command: bash data/scripts/get_argoverse_hd.sh
|
||||
# Train command: python train.py --data argoverse_hd.yaml
|
||||
# Default dataset location is next to YOLOv3:
|
||||
# /parent_folder
|
||||
# /argoverse
|
||||
# /yolov3
|
||||
|
||||
# Download/unzip images
|
||||
d='../argoverse/' # unzip directory
|
||||
mkdir $d
|
||||
url=https://argoverse-hd.s3.us-east-2.amazonaws.com/
|
||||
f=Argoverse-HD-Full.zip
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &# download, unzip, remove in background
|
||||
wait # finish background tasks
|
||||
|
||||
cd ../argoverse/Argoverse-1.1/
|
||||
ln -s tracking images
|
||||
|
||||
cd ../Argoverse-HD/annotations/
|
||||
|
||||
python3 - "$@" <<END
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
annotation_files = ["train.json", "val.json"]
|
||||
print("Converting annotations to YOLOv3 format...")
|
||||
|
||||
for val in annotation_files:
|
||||
a = json.load(open(val, "rb"))
|
||||
|
||||
label_dict = {}
|
||||
for annot in a['annotations']:
|
||||
img_id = annot['image_id']
|
||||
img_name = a['images'][img_id]['name']
|
||||
img_label_name = img_name[:-3] + "txt"
|
||||
|
||||
cls = annot['category_id'] # instance class id
|
||||
x_center, y_center, width, height = annot['bbox']
|
||||
x_center = (x_center + width / 2) / 1920. # offset and scale
|
||||
y_center = (y_center + height / 2) / 1200. # offset and scale
|
||||
width /= 1920. # scale
|
||||
height /= 1200. # scale
|
||||
|
||||
img_dir = "./labels/" + a['seq_dirs'][a['images'][annot['image_id']]['sid']]
|
||||
|
||||
Path(img_dir).mkdir(parents=True, exist_ok=True)
|
||||
if img_dir + "/" + img_label_name not in label_dict:
|
||||
label_dict[img_dir + "/" + img_label_name] = []
|
||||
|
||||
label_dict[img_dir + "/" + img_label_name].append(f"{cls} {x_center} {y_center} {width} {height}\n")
|
||||
|
||||
for filename in label_dict:
|
||||
with open(filename, "w") as file:
|
||||
for string in label_dict[filename]:
|
||||
file.write(string)
|
||||
|
||||
END
|
||||
|
||||
mv ./labels ../../Argoverse-1.1/
|
||||
+11
-11
@@ -1,27 +1,27 @@
|
||||
#!/bin/bash
|
||||
# COCO 2017 dataset http://cocodataset.org
|
||||
# Download command: bash data/scripts/get_coco.sh
|
||||
# Train command: python train.py --data coco.yaml
|
||||
# Default dataset location is next to YOLOv3:
|
||||
# /parent_folder
|
||||
# /coco
|
||||
# /yolov3
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Download COCO 2017 dataset http://cocodataset.org
|
||||
# Example usage: bash data/scripts/get_coco.sh
|
||||
# parent
|
||||
# ├── yolov3
|
||||
# └── datasets
|
||||
# └── coco ← downloads here
|
||||
|
||||
# Download/unzip labels
|
||||
d='../' # unzip directory
|
||||
d='../datasets' # unzip directory
|
||||
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||
f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB
|
||||
echo 'Downloading' $url$f ' ...'
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
|
||||
|
||||
# Download/unzip images
|
||||
d='../coco/images' # unzip directory
|
||||
d='../datasets/coco/images' # unzip directory
|
||||
url=http://images.cocodataset.org/zips/
|
||||
f1='train2017.zip' # 19G, 118k images
|
||||
f2='val2017.zip' # 1G, 5k images
|
||||
f3='test2017.zip' # 7G, 41k images (optional)
|
||||
for f in $f1 $f2; do
|
||||
echo 'Downloading' $url$f '...'
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
|
||||
done
|
||||
wait # finish background tasks
|
||||
|
||||
+10
-10
@@ -1,17 +1,17 @@
|
||||
#!/bin/bash
|
||||
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128
|
||||
# Download command: bash data/scripts/get_coco128.sh
|
||||
# Train command: python train.py --data coco128.yaml
|
||||
# Default dataset location is next to YOLOv3:
|
||||
# /parent_folder
|
||||
# /coco128
|
||||
# /yolov3
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
|
||||
# Example usage: bash data/scripts/get_coco128.sh
|
||||
# parent
|
||||
# ├── yolov3
|
||||
# └── datasets
|
||||
# └── coco128 ← downloads here
|
||||
|
||||
# Download/unzip images and labels
|
||||
d='../' # unzip directory
|
||||
d='../datasets' # unzip directory
|
||||
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||
f='coco128.zip' # or 'coco2017labels-segments.zip', 68 MB
|
||||
f='coco128.zip' # or 'coco128-segments.zip', 68 MB
|
||||
echo 'Downloading' $url$f ' ...'
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
|
||||
|
||||
wait # finish background tasks
|
||||
|
||||
@@ -1,116 +0,0 @@
|
||||
#!/bin/bash
|
||||
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
|
||||
# Download command: bash data/scripts/get_voc.sh
|
||||
# Train command: python train.py --data voc.yaml
|
||||
# Default dataset location is next to YOLOv3:
|
||||
# /parent_folder
|
||||
# /VOC
|
||||
# /yolov3
|
||||
|
||||
start=$(date +%s)
|
||||
mkdir -p ../tmp
|
||||
cd ../tmp/
|
||||
|
||||
# Download/unzip images and labels
|
||||
d='.' # unzip directory
|
||||
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||
f1=VOCtrainval_06-Nov-2007.zip # 446MB, 5012 images
|
||||
f2=VOCtest_06-Nov-2007.zip # 438MB, 4953 images
|
||||
f3=VOCtrainval_11-May-2012.zip # 1.95GB, 17126 images
|
||||
for f in $f3 $f2 $f1; do
|
||||
echo 'Downloading' $url$f '...'
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
|
||||
done
|
||||
wait # finish background tasks
|
||||
|
||||
end=$(date +%s)
|
||||
runtime=$((end - start))
|
||||
echo "Completed in" $runtime "seconds"
|
||||
|
||||
echo "Splitting dataset..."
|
||||
python3 - "$@" <<END
|
||||
import os
|
||||
import xml.etree.ElementTree as ET
|
||||
from os import getcwd
|
||||
|
||||
sets = [('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
|
||||
|
||||
classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog",
|
||||
"horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
|
||||
|
||||
|
||||
def convert_box(size, box):
|
||||
dw = 1. / (size[0])
|
||||
dh = 1. / (size[1])
|
||||
x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
|
||||
return x * dw, y * dh, w * dw, h * dh
|
||||
|
||||
|
||||
def convert_annotation(year, image_id):
|
||||
in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml' % (year, image_id))
|
||||
out_file = open('VOCdevkit/VOC%s/labels/%s.txt' % (year, image_id), 'w')
|
||||
tree = ET.parse(in_file)
|
||||
root = tree.getroot()
|
||||
size = root.find('size')
|
||||
w = int(size.find('width').text)
|
||||
h = int(size.find('height').text)
|
||||
|
||||
for obj in root.iter('object'):
|
||||
difficult = obj.find('difficult').text
|
||||
cls = obj.find('name').text
|
||||
if cls not in classes or int(difficult) == 1:
|
||||
continue
|
||||
cls_id = classes.index(cls)
|
||||
xmlbox = obj.find('bndbox')
|
||||
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
|
||||
float(xmlbox.find('ymax').text))
|
||||
bb = convert_box((w, h), b)
|
||||
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
|
||||
|
||||
|
||||
cwd = getcwd()
|
||||
for year, image_set in sets:
|
||||
if not os.path.exists('VOCdevkit/VOC%s/labels/' % year):
|
||||
os.makedirs('VOCdevkit/VOC%s/labels/' % year)
|
||||
image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt' % (year, image_set)).read().strip().split()
|
||||
list_file = open('%s_%s.txt' % (year, image_set), 'w')
|
||||
for image_id in image_ids:
|
||||
list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n' % (cwd, year, image_id))
|
||||
convert_annotation(year, image_id)
|
||||
list_file.close()
|
||||
END
|
||||
|
||||
cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt >train.txt
|
||||
cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt >train.all.txt
|
||||
|
||||
mkdir ../VOC ../VOC/images ../VOC/images/train ../VOC/images/val
|
||||
mkdir ../VOC/labels ../VOC/labels/train ../VOC/labels/val
|
||||
|
||||
python3 - "$@" <<END
|
||||
import os
|
||||
|
||||
print(os.path.exists('../tmp/train.txt'))
|
||||
with open('../tmp/train.txt', 'r') as f:
|
||||
for line in f.readlines():
|
||||
line = "/".join(line.split('/')[-5:]).strip()
|
||||
if os.path.exists("../" + line):
|
||||
os.system("cp ../" + line + " ../VOC/images/train")
|
||||
|
||||
line = line.replace('JPEGImages', 'labels').replace('jpg', 'txt')
|
||||
if os.path.exists("../" + line):
|
||||
os.system("cp ../" + line + " ../VOC/labels/train")
|
||||
|
||||
print(os.path.exists('../tmp/2007_test.txt'))
|
||||
with open('../tmp/2007_test.txt', 'r') as f:
|
||||
for line in f.readlines():
|
||||
line = "/".join(line.split('/')[-5:]).strip()
|
||||
if os.path.exists("../" + line):
|
||||
os.system("cp ../" + line + " ../VOC/images/val")
|
||||
|
||||
line = line.replace('JPEGImages', 'labels').replace('jpg', 'txt')
|
||||
if os.path.exists("../" + line):
|
||||
os.system("cp ../" + line + " ../VOC/labels/val")
|
||||
END
|
||||
|
||||
rm -rf ../tmp # remove temporary directory
|
||||
echo "VOC download done."
|
||||
Reference in New Issue
Block a user