YOLOv5 Forward Compatibility Update (#1569)
* YOLOv5 forward compatibility update * add data dir * ci test yolov3 * update build_targets() * update build_targets() * update build_targets() * update yolov3-spp.yaml * add yolov3-tiny.yaml * add yolov3-tiny.yaml * Update yolov3-tiny.yaml * thop bug fix * Detection() device bug fix * Use torchvision.ops.nms() * Remove redundant download mirror * CI tests with yolov3-tiny * Update README.md * Synch train and test iou_thresh * update requirements.txt * Cat apriori autolabels * Confusion matrix * Autosplit * Autosplit * Update README.md * AP no plot * Update caching * Update caching * Caching bug fix * --image-weights bug fix * datasets bug fix * mosaic plots bug fix * plot_study * boxes.max() * boxes.max() * boxes.max() * boxes.max() * boxes.max() * boxes.max() * update * Update README * Update README * Update README.md * Update README.md * results png * Update README * Targets scaling bug fix * update plot_study * update plot_study * update plot_study * update plot_study * Targets scaling bug fix * Finish Readme.md * Finish Readme.md * Finish Readme.md * Update README.md * Creado con Colaboratory
This commit is contained in:
Glenn Jocher
GitHub
@@ -1,80 +0,0 @@
|
||||
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
|
||||
cell phone
|
||||
microwave
|
||||
oven
|
||||
toaster
|
||||
sink
|
||||
refrigerator
|
||||
book
|
||||
clock
|
||||
vase
|
||||
scissors
|
||||
teddy bear
|
||||
hair drier
|
||||
toothbrush
|
||||
@@ -0,0 +1,35 @@
|
||||
# COCO 2017 dataset http://cocodataset.org
|
||||
# Train command: python train.py --data coco.yaml
|
||||
# Default dataset location is next to /yolov3:
|
||||
# /parent_folder
|
||||
# /coco
|
||||
# /yolov3
|
||||
|
||||
|
||||
# download command/URL (optional)
|
||||
download: bash data/scripts/get_coco.sh
|
||||
|
||||
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
|
||||
train: ../coco/train2017.txt # 118287 images
|
||||
val: ../coco/val2017.txt # 5000 images
|
||||
test: ../coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
|
||||
|
||||
# number of classes
|
||||
nc: 80
|
||||
|
||||
# class names
|
||||
names: ['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', 'cell phone',
|
||||
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
|
||||
'hair drier', 'toothbrush']
|
||||
|
||||
# Print classes
|
||||
# with open('data/coco.yaml') as f:
|
||||
# d = yaml.load(f, Loader=yaml.FullLoader) # dict
|
||||
# for i, x in enumerate(d['names']):
|
||||
# print(i, x)
|
||||
@@ -1,4 +0,0 @@
|
||||
classes=80
|
||||
train=data/coco1.txt
|
||||
valid=data/coco1.txt
|
||||
names=data/coco.names
|
||||
@@ -1 +0,0 @@
|
||||
../coco/images/train2017/000000109622.jpg
|
||||
@@ -0,0 +1,28 @@
|
||||
# COCO 2017 dataset http://cocodataset.org - first 128 training images
|
||||
# Train command: python train.py --data coco128.yaml
|
||||
# Default dataset location is next to /yolov3:
|
||||
# /parent_folder
|
||||
# /coco128
|
||||
# /yolov3
|
||||
|
||||
|
||||
# download command/URL (optional)
|
||||
download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
|
||||
|
||||
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
|
||||
train: ../coco128/images/train2017/ # 128 images
|
||||
val: ../coco128/images/train2017/ # 128 images
|
||||
|
||||
# number of classes
|
||||
nc: 80
|
||||
|
||||
# class names
|
||||
names: ['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', 'cell phone',
|
||||
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
|
||||
'hair drier', 'toothbrush']
|
||||
@@ -1,4 +0,0 @@
|
||||
classes=80
|
||||
train=data/coco16.txt
|
||||
valid=data/coco16.txt
|
||||
names=data/coco.names
|
||||
@@ -1,16 +0,0 @@
|
||||
../coco/images/train2017/000000109622.jpg
|
||||
../coco/images/train2017/000000160694.jpg
|
||||
../coco/images/train2017/000000308590.jpg
|
||||
../coco/images/train2017/000000327573.jpg
|
||||
../coco/images/train2017/000000062929.jpg
|
||||
../coco/images/train2017/000000512793.jpg
|
||||
../coco/images/train2017/000000371735.jpg
|
||||
../coco/images/train2017/000000148118.jpg
|
||||
../coco/images/train2017/000000309856.jpg
|
||||
../coco/images/train2017/000000141882.jpg
|
||||
../coco/images/train2017/000000318783.jpg
|
||||
../coco/images/train2017/000000337760.jpg
|
||||
../coco/images/train2017/000000298197.jpg
|
||||
../coco/images/train2017/000000042421.jpg
|
||||
../coco/images/train2017/000000328898.jpg
|
||||
../coco/images/train2017/000000458856.jpg
|
||||
@@ -1,4 +0,0 @@
|
||||
classes=1
|
||||
train=data/coco1cls.txt
|
||||
valid=data/coco1cls.txt
|
||||
names=data/coco.names
|
||||
@@ -1,16 +0,0 @@
|
||||
../coco/images/train2017/000000000901.jpg
|
||||
../coco/images/train2017/000000001464.jpg
|
||||
../coco/images/train2017/000000003220.jpg
|
||||
../coco/images/train2017/000000003365.jpg
|
||||
../coco/images/train2017/000000004772.jpg
|
||||
../coco/images/train2017/000000009987.jpg
|
||||
../coco/images/train2017/000000010498.jpg
|
||||
../coco/images/train2017/000000012455.jpg
|
||||
../coco/images/train2017/000000013992.jpg
|
||||
../coco/images/train2017/000000014125.jpg
|
||||
../coco/images/train2017/000000016314.jpg
|
||||
../coco/images/train2017/000000016670.jpg
|
||||
../coco/images/train2017/000000018412.jpg
|
||||
../coco/images/train2017/000000021212.jpg
|
||||
../coco/images/train2017/000000021826.jpg
|
||||
../coco/images/train2017/000000030566.jpg
|
||||
@@ -1,4 +0,0 @@
|
||||
classes=80
|
||||
train=../coco/trainvalno5k.txt
|
||||
valid=../coco/5k.txt
|
||||
names=data/coco.names
|
||||
@@ -1,4 +0,0 @@
|
||||
classes=80
|
||||
train=../coco/train2017.txt
|
||||
valid=../coco/val2017.txt
|
||||
names=data/coco.names
|
||||
@@ -1,4 +0,0 @@
|
||||
classes=80
|
||||
train=data/coco64.txt
|
||||
valid=data/coco64.txt
|
||||
names=data/coco.names
|
||||
@@ -1,64 +0,0 @@
|
||||
../coco/images/train2017/000000109622.jpg
|
||||
../coco/images/train2017/000000160694.jpg
|
||||
../coco/images/train2017/000000308590.jpg
|
||||
../coco/images/train2017/000000327573.jpg
|
||||
../coco/images/train2017/000000062929.jpg
|
||||
../coco/images/train2017/000000512793.jpg
|
||||
../coco/images/train2017/000000371735.jpg
|
||||
../coco/images/train2017/000000148118.jpg
|
||||
../coco/images/train2017/000000309856.jpg
|
||||
../coco/images/train2017/000000141882.jpg
|
||||
../coco/images/train2017/000000318783.jpg
|
||||
../coco/images/train2017/000000337760.jpg
|
||||
../coco/images/train2017/000000298197.jpg
|
||||
../coco/images/train2017/000000042421.jpg
|
||||
../coco/images/train2017/000000328898.jpg
|
||||
../coco/images/train2017/000000458856.jpg
|
||||
../coco/images/train2017/000000073824.jpg
|
||||
../coco/images/train2017/000000252846.jpg
|
||||
../coco/images/train2017/000000459590.jpg
|
||||
../coco/images/train2017/000000273650.jpg
|
||||
../coco/images/train2017/000000331311.jpg
|
||||
../coco/images/train2017/000000156326.jpg
|
||||
../coco/images/train2017/000000262985.jpg
|
||||
../coco/images/train2017/000000253580.jpg
|
||||
../coco/images/train2017/000000447976.jpg
|
||||
../coco/images/train2017/000000378077.jpg
|
||||
../coco/images/train2017/000000259913.jpg
|
||||
../coco/images/train2017/000000424553.jpg
|
||||
../coco/images/train2017/000000000612.jpg
|
||||
../coco/images/train2017/000000267625.jpg
|
||||
../coco/images/train2017/000000566012.jpg
|
||||
../coco/images/train2017/000000196664.jpg
|
||||
../coco/images/train2017/000000363331.jpg
|
||||
../coco/images/train2017/000000057992.jpg
|
||||
../coco/images/train2017/000000520047.jpg
|
||||
../coco/images/train2017/000000453903.jpg
|
||||
../coco/images/train2017/000000162083.jpg
|
||||
../coco/images/train2017/000000268516.jpg
|
||||
../coco/images/train2017/000000277436.jpg
|
||||
../coco/images/train2017/000000189744.jpg
|
||||
../coco/images/train2017/000000041128.jpg
|
||||
../coco/images/train2017/000000527728.jpg
|
||||
../coco/images/train2017/000000465269.jpg
|
||||
../coco/images/train2017/000000246833.jpg
|
||||
../coco/images/train2017/000000076784.jpg
|
||||
../coco/images/train2017/000000323715.jpg
|
||||
../coco/images/train2017/000000560463.jpg
|
||||
../coco/images/train2017/000000006263.jpg
|
||||
../coco/images/train2017/000000094701.jpg
|
||||
../coco/images/train2017/000000521359.jpg
|
||||
../coco/images/train2017/000000302903.jpg
|
||||
../coco/images/train2017/000000047559.jpg
|
||||
../coco/images/train2017/000000480583.jpg
|
||||
../coco/images/train2017/000000050025.jpg
|
||||
../coco/images/train2017/000000084512.jpg
|
||||
../coco/images/train2017/000000508913.jpg
|
||||
../coco/images/train2017/000000093708.jpg
|
||||
../coco/images/train2017/000000070493.jpg
|
||||
../coco/images/train2017/000000539270.jpg
|
||||
../coco/images/train2017/000000474402.jpg
|
||||
../coco/images/train2017/000000209842.jpg
|
||||
../coco/images/train2017/000000028820.jpg
|
||||
../coco/images/train2017/000000154257.jpg
|
||||
../coco/images/train2017/000000342499.jpg
|
||||
@@ -1,91 +0,0 @@
|
||||
person
|
||||
bicycle
|
||||
car
|
||||
motorcycle
|
||||
airplane
|
||||
bus
|
||||
train
|
||||
truck
|
||||
boat
|
||||
traffic light
|
||||
fire hydrant
|
||||
street sign
|
||||
stop sign
|
||||
parking meter
|
||||
bench
|
||||
bird
|
||||
cat
|
||||
dog
|
||||
horse
|
||||
sheep
|
||||
cow
|
||||
elephant
|
||||
bear
|
||||
zebra
|
||||
giraffe
|
||||
hat
|
||||
backpack
|
||||
umbrella
|
||||
shoe
|
||||
eye glasses
|
||||
handbag
|
||||
tie
|
||||
suitcase
|
||||
frisbee
|
||||
skis
|
||||
snowboard
|
||||
sports ball
|
||||
kite
|
||||
baseball bat
|
||||
baseball glove
|
||||
skateboard
|
||||
surfboard
|
||||
tennis racket
|
||||
bottle
|
||||
plate
|
||||
wine glass
|
||||
cup
|
||||
fork
|
||||
knife
|
||||
spoon
|
||||
bowl
|
||||
banana
|
||||
apple
|
||||
sandwich
|
||||
orange
|
||||
broccoli
|
||||
carrot
|
||||
hot dog
|
||||
pizza
|
||||
donut
|
||||
cake
|
||||
chair
|
||||
couch
|
||||
potted plant
|
||||
bed
|
||||
mirror
|
||||
dining table
|
||||
window
|
||||
desk
|
||||
toilet
|
||||
door
|
||||
tv
|
||||
laptop
|
||||
mouse
|
||||
remote
|
||||
keyboard
|
||||
cell phone
|
||||
microwave
|
||||
oven
|
||||
toaster
|
||||
sink
|
||||
refrigerator
|
||||
blender
|
||||
book
|
||||
clock
|
||||
vase
|
||||
scissors
|
||||
teddy bear
|
||||
hair drier
|
||||
toothbrush
|
||||
hair brush
|
||||
@@ -1,24 +0,0 @@
|
||||
#!/bin/bash
|
||||
# Zip coco folder
|
||||
# zip -r coco.zip coco
|
||||
# tar -czvf coco.tar.gz coco
|
||||
|
||||
# Download labels from Google Drive, accepting presented query
|
||||
filename="coco2014labels.zip"
|
||||
fileid="1s6-CmF5_SElM28r52P1OUrCcuXZN-SFo"
|
||||
curl -c ./cookie -s -L "https://drive.google.com/uc?export=download&id=${fileid}" >/dev/null
|
||||
curl -Lb ./cookie "https://drive.google.com/uc?export=download&confirm=$(awk '/download/ {print $NF}' ./cookie)&id=${fileid}" -o ${filename}
|
||||
rm ./cookie
|
||||
|
||||
# Unzip labels
|
||||
unzip -q ${filename} # for coco.zip
|
||||
# tar -xzf ${filename} # for coco.tar.gz
|
||||
rm ${filename}
|
||||
|
||||
# Download and unzip images
|
||||
cd coco/images
|
||||
f="train2014.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f
|
||||
f="val2014.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f
|
||||
|
||||
# cd out
|
||||
cd ../..
|
||||
@@ -1,24 +0,0 @@
|
||||
#!/bin/bash
|
||||
# Zip coco folder
|
||||
# zip -r coco.zip coco
|
||||
# tar -czvf coco.tar.gz coco
|
||||
|
||||
# Download labels from Google Drive, accepting presented query
|
||||
filename="coco2017labels.zip"
|
||||
fileid="1cXZR_ckHki6nddOmcysCuuJFM--T-Q6L"
|
||||
curl -c ./cookie -s -L "https://drive.google.com/uc?export=download&id=${fileid}" >/dev/null
|
||||
curl -Lb ./cookie "https://drive.google.com/uc?export=download&confirm=$(awk '/download/ {print $NF}' ./cookie)&id=${fileid}" -o ${filename}
|
||||
rm ./cookie
|
||||
|
||||
# Unzip labels
|
||||
unzip -q ${filename} # for coco.zip
|
||||
# tar -xzf ${filename} # for coco.tar.gz
|
||||
rm ${filename}
|
||||
|
||||
# Download and unzip images
|
||||
cd coco/images
|
||||
f="train2017.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f
|
||||
f="val2017.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f
|
||||
|
||||
# cd out
|
||||
cd ../..
|
||||
@@ -0,0 +1,38 @@
|
||||
# Hyperparameters for VOC finetuning
|
||||
# python train.py --batch 64 --weights yolov5m.pt --data voc.yaml --img 512 --epochs 50
|
||||
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
|
||||
|
||||
|
||||
# Hyperparameter Evolution Results
|
||||
# Generations: 306
|
||||
# P R mAP.5 mAP.5:.95 box obj cls
|
||||
# Metrics: 0.6 0.936 0.896 0.684 0.0115 0.00805 0.00146
|
||||
|
||||
lr0: 0.0032
|
||||
lrf: 0.12
|
||||
momentum: 0.843
|
||||
weight_decay: 0.00036
|
||||
warmup_epochs: 2.0
|
||||
warmup_momentum: 0.5
|
||||
warmup_bias_lr: 0.05
|
||||
box: 0.0296
|
||||
cls: 0.243
|
||||
cls_pw: 0.631
|
||||
obj: 0.301
|
||||
obj_pw: 0.911
|
||||
iou_t: 0.2
|
||||
anchor_t: 2.91
|
||||
# anchors: 3.63
|
||||
fl_gamma: 0.0
|
||||
hsv_h: 0.0138
|
||||
hsv_s: 0.664
|
||||
hsv_v: 0.464
|
||||
degrees: 0.373
|
||||
translate: 0.245
|
||||
scale: 0.898
|
||||
shear: 0.602
|
||||
perspective: 0.0
|
||||
flipud: 0.00856
|
||||
fliplr: 0.5
|
||||
mosaic: 1.0
|
||||
mixup: 0.243
|
||||
@@ -0,0 +1,33 @@
|
||||
# Hyperparameters for COCO training from scratch
|
||||
# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300
|
||||
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
|
||||
|
||||
|
||||
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf)
|
||||
momentum: 0.937 # SGD momentum/Adam beta1
|
||||
weight_decay: 0.0005 # optimizer weight decay 5e-4
|
||||
warmup_epochs: 3.0 # warmup epochs (fractions ok)
|
||||
warmup_momentum: 0.8 # warmup initial momentum
|
||||
warmup_bias_lr: 0.1 # warmup initial bias lr
|
||||
box: 0.05 # box loss gain
|
||||
cls: 0.5 # cls loss gain
|
||||
cls_pw: 1.0 # cls BCELoss positive_weight
|
||||
obj: 1.0 # obj loss gain (scale with pixels)
|
||||
obj_pw: 1.0 # obj BCELoss positive_weight
|
||||
iou_t: 0.20 # IoU training threshold
|
||||
anchor_t: 4.0 # anchor-multiple threshold
|
||||
# anchors: 3 # anchors per output layer (0 to ignore)
|
||||
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
|
||||
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
|
||||
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
|
||||
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
|
||||
degrees: 0.0 # image rotation (+/- deg)
|
||||
translate: 0.1 # image translation (+/- fraction)
|
||||
scale: 0.5 # image scale (+/- gain)
|
||||
shear: 0.0 # image shear (+/- deg)
|
||||
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
|
||||
flipud: 0.0 # image flip up-down (probability)
|
||||
fliplr: 0.5 # image flip left-right (probability)
|
||||
mosaic: 1.0 # image mosaic (probability)
|
||||
mixup: 0.0 # image mixup (probability)
|
||||
|
Before Width: | Height: | Size: 476 KiB After Width: | Height: | Size: 476 KiB |
|
Before Width: | Height: | Size: 165 KiB After Width: | Height: | Size: 165 KiB |
Executable
+24
@@ -0,0 +1,24 @@
|
||||
#!/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
|
||||
|
||||
# Download/unzip labels
|
||||
d='../' # unzip directory
|
||||
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||
f='coco2017labels.zip' # 68 MB
|
||||
echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
|
||||
|
||||
# Download/unzip images
|
||||
d='../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 $f # download, unzip, remove
|
||||
done
|
||||
@@ -0,0 +1,137 @@
|
||||
#!/bin/bash
|
||||
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
|
||||
# Download command: bash data/scripts/get_voc.sh
|
||||
# Train command: python train.py --data voc.yaml
|
||||
# Default dataset location is next to /yolov5:
|
||||
# /parent_folder
|
||||
# /VOC
|
||||
# /yolov5
|
||||
|
||||
start=$(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 $f1 $f2 $f3; do
|
||||
echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
|
||||
done
|
||||
|
||||
end=$(date +%s)
|
||||
runtime=$((end - start))
|
||||
echo "Completed in" $runtime "seconds"
|
||||
|
||||
echo "Splitting dataset..."
|
||||
python3 - "$@" <<END
|
||||
import xml.etree.ElementTree as ET
|
||||
import pickle
|
||||
import os
|
||||
from os import listdir, getcwd
|
||||
from os.path import join
|
||||
|
||||
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(size, box):
|
||||
dw = 1./(size[0])
|
||||
dh = 1./(size[1])
|
||||
x = (box[0] + box[1])/2.0 - 1
|
||||
y = (box[2] + box[3])/2.0 - 1
|
||||
w = box[1] - box[0]
|
||||
h = box[3] - box[2]
|
||||
x = x*dw
|
||||
w = w*dw
|
||||
y = y*dh
|
||||
h = h*dh
|
||||
return (x,y,w,h)
|
||||
|
||||
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((w,h), b)
|
||||
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
|
||||
|
||||
wd = 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'%(wd, 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
|
||||
|
||||
python3 - "$@" <<END
|
||||
|
||||
import shutil
|
||||
import os
|
||||
os.system('mkdir ../VOC/')
|
||||
os.system('mkdir ../VOC/images')
|
||||
os.system('mkdir ../VOC/images/train')
|
||||
os.system('mkdir ../VOC/images/val')
|
||||
|
||||
os.system('mkdir ../VOC/labels')
|
||||
os.system('mkdir ../VOC/labels/train')
|
||||
os.system('mkdir ../VOC/labels/val')
|
||||
|
||||
import os
|
||||
print(os.path.exists('../tmp/train.txt'))
|
||||
f = open('../tmp/train.txt', 'r')
|
||||
lines = f.readlines()
|
||||
|
||||
for line in lines:
|
||||
line = "/".join(line.split('/')[-5:]).strip()
|
||||
if (os.path.exists("../" + line)):
|
||||
os.system("cp ../"+ line + " ../VOC/images/train")
|
||||
|
||||
line = line.replace('JPEGImages', 'labels')
|
||||
line = line.replace('jpg', 'txt')
|
||||
if (os.path.exists("../" + line)):
|
||||
os.system("cp ../"+ line + " ../VOC/labels/train")
|
||||
|
||||
|
||||
print(os.path.exists('../tmp/2007_test.txt'))
|
||||
f = open('../tmp/2007_test.txt', 'r')
|
||||
lines = f.readlines()
|
||||
|
||||
for line in lines:
|
||||
line = "/".join(line.split('/')[-5:]).strip()
|
||||
if (os.path.exists("../" + line)):
|
||||
os.system("cp ../"+ line + " ../VOC/images/val")
|
||||
|
||||
line = line.replace('JPEGImages', 'labels')
|
||||
line = line.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."
|
||||
@@ -0,0 +1,21 @@
|
||||
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
|
||||
# Train command: python train.py --data voc.yaml
|
||||
# Default dataset location is next to /yolov3:
|
||||
# /parent_folder
|
||||
# /VOC
|
||||
# /yolov3
|
||||
|
||||
|
||||
# download command/URL (optional)
|
||||
download: bash data/scripts/get_voc.sh
|
||||
|
||||
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
|
||||
train: ../VOC/images/train/ # 16551 images
|
||||
val: ../VOC/images/val/ # 4952 images
|
||||
|
||||
# number of classes
|
||||
nc: 20
|
||||
|
||||
# class names
|
||||
names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
|
||||
'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
|
||||
Reference in New Issue
Block a user