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yolov5/data/coco128-seg.yaml
Ayush Chaurasia f9869f7ffd
YOLOv5 segmentation model support (#9052) 
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* Fix duplicate plots.py

* Fix check_font()

* # torch.use_deterministic_algorithms(True)

* update doc detect->predict

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* Align NMS-seg closer to NMS

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* update

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* Remove unused ImageFont import

* Unified NMS

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* DetectMultiBackend compatibility

* segment/predict.py update

* update plot colors

* fix bbox shifted

* sort bbox by confidence

* enable overlap by default

* Merge detect/segment output_to_target() function

* Start segmentation CI

* fix plots

* Update ci-testing.yml

* fix training whitespace

* optimize process mask functions (can we merge both?)

* Update predict/detect

* Update plot_images

* Update plot_images_and_masks

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* fix

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* fix precommit pycocotools

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* Add CSV Logger

* fix val len(plot_masks)

* speed up evaluation

* fix process_mask

* fix plots

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* optimize utils/segment/general crop()

* optimize utils/segment/general crop() 2

* minor updates

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* torch.where revert

* downsample only if different shape

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* loss cleanup

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* loss cleanup 3

* update project names

* Rename -seg yamls from _underscore to -dash

* prepare for yolov5n-seg.pt

* precommit space fix

* add coco128-seg.yaml

* update coco128-seg comments

* cleanup val.py

* Major val.py cleanup

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* precommit fix

* precommit fix

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* optional pycocotools

* remove CI pip install pycocotools (auto-installed now)

* seg yaml fix

* optimize mask_iou() and masks_iou()

* threaded fix

* Major train.py update

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* Major segments/val/process_batch() update

* yolov5/val updates from segment

* process_batch numpy/tensor fix

* opt-in to pycocotools with --save-json

* threaded pycocotools ops for 2x speed increase

* Avoid permute contiguous if possible

* Add max_det=300 argument to both val.py and segment/val.py

* fix onnx_dynamic

* speed up pycocotools ops

* faster process_mask(upsample=True) for predict

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* eliminate permutations for process_mask(upsample=True)

* eliminate permute-contiguous in crop(), use native dimension order

* cleanup comment

* Add Proto() module

* fix class count

* fix anchor order

* broadcast mask_gti in loss for speed

* Cleanup seg loss

* faster indexing

* faster indexing fix

* faster indexing fix2

* revert faster indexing

* fix validation plotting

* Loss cleanup and mxyxy simplification

* Loss cleanup and mxyxy simplification 2

* revert validation plotting

* replace missing tanh

* Eliminate last permutation

* delete unneeded .float()

* Remove MaskIOULoss and crop(if HWC)

* Final v6.3 SegmentationModel architecture updates

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* Add support for TF export

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* Update dataloaders.py

* Restore CI

* Update dataloaders.py

* Fix TF/TFLite export for segmentation model

* Merge master

* Cleanup predict.py mask plotting

* cleanup scale_masks()

* rename scale_masks to scale_image

* cleanup/optimize plot_masks

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* Add Annotator.masks()

* Annotator.masks() fix

* Update plots.py

* Annotator mask optimization

* Rename crop() to crop_mask()

* Do not crop in predict.py

* crop always

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* Merge master

* Add vid-stride from master PR

* Update seg model outputs

* Update seg model outputs

* Add segmentation benchmarks

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* Add segmentation benchmarks

* Add segmentation benchmarks

* Add segmentation benchmarks

* Fix DetectMultiBackend for OpenVINO

* update Annotator.masks

* fix val plot

* revert val plot

* clean up

* revert pil

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* Fix CI error

* fix predict log

* remove upsample

* update interpolate

* fix validation plot logging

* Annotator.masks() cleanup

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* Remove segmentation_model definition

* Restore 0.99999 decimals

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>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: Jiacong Fang <zldrobit@126.com>
2022-09-16 00:12:46 +02:00

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
# Example usage: python train.py --data coco128.yaml
# parent
# ├── yolov5
# └── datasets
# └── coco128-seg ← downloads here (7 MB)
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/coco128-seg # dataset root dir
train: images/train2017 # train images (relative to 'path') 128 images
val: images/train2017 # val images (relative to 'path') 128 images
test: # test images (optional)
# Classes
names:
0: person
1: bicycle
2: car
3: motorcycle
4: airplane
5: bus
6: train
7: truck
8: boat
9: traffic light
10: fire hydrant
11: stop sign
12: parking meter
13: bench
14: bird
15: cat
16: dog
17: horse
18: sheep
19: cow
20: elephant
21: bear
22: zebra
23: giraffe
24: backpack
25: umbrella
26: handbag
27: tie
28: suitcase
29: frisbee
30: skis
31: snowboard
32: sports ball
33: kite
34: baseball bat
35: baseball glove
36: skateboard
37: surfboard
38: tennis racket
39: bottle
40: wine glass
41: cup
42: fork
43: knife
44: spoon
45: bowl
46: banana
47: apple
48: sandwich
49: orange
50: broccoli
51: carrot
52: hot dog
53: pizza
54: donut
55: cake
56: chair
57: couch
58: potted plant
59: bed
60: dining table
61: toilet
62: tv
63: laptop
64: mouse
65: remote
66: keyboard
67: cell phone
68: microwave
69: oven
70: toaster
71: sink
72: refrigerator
73: book
74: clock
75: vase
76: scissors
77: teddy bear
78: hair drier
79: toothbrush
# Download script/URL (optional)
download: https://ultralytics.com/assets/coco128-seg.zip
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