yolov5/data/scripts/get_coco.sh
Glenn Jocher d3ea0df8b9
New YOLOv5 Classification Models (#8956)
* Update

* Logger step fix: Increment step with epochs (#8654)

* enhance

* revert

* allow training from scratch

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* Update --img argument from train.py 

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* fix image size from 640 to 128

* suport custom dataloader and augmentation

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* format

* Update dataloaders.py

* Single line return, single line comment, remove unused argument

* address PR comments

* fix spelling

* don't augment eval set

* use fstring

* update augmentations.py

* new maning convention for transforms

* reverse if statement, inline ops

* reverse if statement, inline ops

* updates

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* update dataloaders

* Remove additional if statement

* Remove is_train as redundant

* Cleanup

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* Cleanup2

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* Update classifier.py

* Update augmentations.py

* fix: imshow clip warning

* update

* Revert ToTensorV2 removal

* Update classifier.py

* Update normalize values, revert uint8

* normalize image using cv2

* remove dedundant comment

* Update classifier.py

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

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* Update

* Allow logging models from GenericLogger (#8676)

* enhance

* revert

* allow training from scratch

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update --img argument from train.py 

single line

* fix image size from 640 to 128

* suport custom dataloader and augmentation

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* format

* Update dataloaders.py

* Single line return, single line comment, remove unused argument

* address PR comments

* fix spelling

* don't augment eval set

* use fstring

* update augmentations.py

* new maning convention for transforms

* reverse if statement, inline ops

* reverse if statement, inline ops

* updates

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* update dataloaders

* Remove additional if statement

* Remove is_train as redundant

* Cleanup

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Cleanup2

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update classifier.py

* Update augmentations.py

* fix: imshow clip warning

* update

* Revert ToTensorV2 removal

* Update classifier.py

* Update normalize values, revert uint8

* normalize image using cv2

* remove dedundant comment

* Update classifier.py

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* replace print with logger

* commit steps

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* support final model logging

* update

* update

* update

* update

* remove curses

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* Update classifier.py

* Update __init__.py

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update

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* Update dataset download

* Update dataset download

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* Pass imgsz to classify_transforms()

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* Cos scheduler

* Cos scheduler

* Remove unused args

* Update

* Add seed

* Add seed

* Update

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* Add run(), main()

* Merge master

* Merge master

* Update

* Update

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* Update

* Create YOLOv5 BaseModel class (#8829)

* Create BaseModel

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* fix

* Hub load device fix

* Update

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* Add experiment

* Merge master

* Attach names

* weight decay = 1e-4

* weight decay = 5e-5

* update smart_optimizer console printout

* fashion-mnist fix

* Merge master

* Update Table

* Update Table

* Remove destroy process group

* add kwargs to forward()

* fuse fix for resnet50

* nc, names fix for resnet50

* nc, names fix for resnet50

* ONNX CPU inference fix

* revert

* cuda

* if augment or visualize

* if augment or visualize

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* New smart_inference_mode()

* Update README

* Refactor into /classify dir

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* reset defaults

* reset defaults

* fix gpu predict

* warmup

* ema half fix

* spacing

* remove data

* remove cache

* remove denormalize

* save run settings

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* verbose false on initial plots

* new save_yaml() function

* Update ci-testing.yml

* Path(data) CI fix

* Separate classification CI

* fix val

* fix val

* fix val

* smartCrossEntropyLoss

* skip validation on hub load

* autodownload with working dir root

* str(data)

* Dataset usage example

* im_show normalize

* im_show normalize

* add imagenet simple names to multibackend

* Add validation speeds

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* 24-space names

* Update bash scripts

* Update permissions

* Add bash script arguments

* remove verbose

* TRT data fix

* names generator fix

* optimize if names

* update usage

* Add local loading

* Verbose=False

* update names printing

* Add Usage examples

* Add Usage examples

* Add Usage examples

* Add Usage examples

* named_children

* reshape_classifier_outputs

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* update

* update

* fix CI

* fix incorrect class substitution

* fix incorrect class substitution

* remove denormalize

* ravel fix

* cleanup

* update opt file printing

* update opt file printing

* update defaults

* add opt to checkpoint

* Add warning

* Add comment

* plot half bug fix

* Use NotImplementedError

* fix export shape report

* Fix TRT load

* cleanup CI

* profile comment

* CI fix

* Add cls models

* avoid inplace error

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* Fix usage examples

* Update README

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* Update README

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* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

* Update README

Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
2022-08-17 11:59:01 +02:00

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#!/bin/bash
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Download COCO 2017 dataset http://cocodataset.org
# Example usage: bash data/scripts/get_coco.sh
# parent
# ├── yolov5
# └── datasets
# └── coco ← downloads here
# Arguments (optional) Usage: bash data/scripts/get_coco.sh --train --val --test --segments
if [ "$#" -gt 0 ]; then
for opt in "$@"; do
case "${opt}" in
--train) train=true ;;
--val) val=true ;;
--test) test=true ;;
--segments) segments=true ;;
esac
done
else
train=true
val=true
test=false
segments=false
fi
# Download/unzip labels
d='../datasets' # unzip directory
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
if [ "$segments" == "true" ]; then
f='coco2017labels-segments.zip' # 168 MB
else
f='coco2017labels.zip' # 168 MB
fi
echo 'Downloading' $url$f ' ...'
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
# Download/unzip images
d='../datasets/coco/images' # unzip directory
url=http://images.cocodataset.org/zips/
if [ "$train" == "true" ]; then
f='train2017.zip' # 19G, 118k images
echo 'Downloading' $url$f '...'
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
fi
if [ "$val" == "true" ]; then
f='val2017.zip' # 1G, 5k images
echo 'Downloading' $url$f '...'
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
fi
if [ "$test" == "true" ]; then
f='test2017.zip' # 7G, 41k images (optional)
echo 'Downloading' $url$f '...'
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
fi
wait # finish background tasks