File size: 2,715 Bytes
689a987 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
# Setup Builtin Datasets
Detectron2 has builtin support for a few datasets.
The datasets are assumed to exist in a directory specified by the environment variable
`DETECTRON2_DATASETS`.
Under this directory, detectron2 expects to find datasets in the structure described below.
You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`.
If left unset, the default is `./datasets` relative to your current working directory.
The [model zoo](https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md)
contains configs and models that use these builtin datasets.
## Expected dataset structure for COCO instance/keypoint detection:
```
coco/
annotations/
instances_{train,val}2017.json
person_keypoints_{train,val}2017.json
{train,val}2017/
# image files that are mentioned in the corresponding json
```
You can use the 2014 version of the dataset as well.
Some of the builtin tests (`dev/run_*_tests.sh`) uses a tiny version of the COCO dataset,
which you can download with `./prepare_for_tests.sh`.
## Expected dataset structure for PanopticFPN:
```
coco/
annotations/
panoptic_{train,val}2017.json
panoptic_{train,val}2017/ # png annotations
panoptic_stuff_{train,val}2017/ # generated by the script mentioned below
```
Install panopticapi by:
```
pip install git+https://github.com/cocodataset/panopticapi.git
```
Then, run `python prepare_panoptic_fpn.py`, to extract semantic annotations from panoptic annotations.
## Expected dataset structure for LVIS instance segmentation:
```
coco/
{train,val,test}2017/
lvis/
lvis_v0.5_{train,val}.json
lvis_v0.5_image_info_test.json
```
Install lvis-api by:
```
pip install git+https://github.com/lvis-dataset/lvis-api.git
```
Run `python prepare_cocofied_lvis.py` to prepare "cocofied" LVIS annotations for evaluation of models trained on the COCO dataset.
## Expected dataset structure for cityscapes:
```
cityscapes/
gtFine/
train/
aachen/
color.png, instanceIds.png, labelIds.png, polygons.json,
labelTrainIds.png
...
val/
test/
leftImg8bit/
train/
val/
test/
```
Install cityscapes scripts by:
```
pip install git+https://github.com/mcordts/cityscapesScripts.git
```
Note: labelTrainIds.png are created using cityscapesescript with:
```
CITYSCAPES_DATASET=$DETECTRON2_DATASETS/cityscapes python cityscapesscripts/preparation/createTrainIdLabelImgs.py
```
They are not needed for instance segmentation.
## Expected dataset structure for Pascal VOC:
```
VOC20{07,12}/
Annotations/
ImageSets/
Main/
trainval.txt
test.txt
# train.txt or val.txt, if you use these splits
JPEGImages/
```
|