ShayManor/gt_trainval

ACDC

ACDC, or the Adverse Conditions Dataset with Correspondences, is a large-scale dataset for dense semantic perception tasks in adverse visual conditions, including semantic segmentation, object detection, and panoptic segmentation. It consists of 4006 images which are evenly distributed between four common adverse conditions: fog, nighttime, rain, and snow. Each adverse-condition image comes with a high-quality fine pixel-level panoptic annotation, a corresponding image of the same scene taken under normal conditions, and a binary mask that distinguishes between intra-image regions of clear and uncertain semantic content. Moreover, ACDC includes high-quality fine pixel-level panoptic annotations for 1503 of the aforementioned corresponding normal-condition images.

Thus, ACDC supports:

1. semantic segmentation
2. object detection
3. panoptic segmentation
4. the new uncertainty-aware semantic segmentation

The ground-truth annotations of the training and validation sets are publicly available. The ground-truth annotations of the test set are withheld and they are used for the publicly available ACDC benchmark.

Download

ACDC can be downloaded through the associated website. Users need to register on this website first before accessing the dataset.

Note: due to data protection regulations, the original RGB images of the dataset, which contain personal data, are not made available by default. After having registered, users who are interested in the original images need to explicitly request access to them. Anonymized versions of the images - with minimal modifications - are available by default and can be used alternatively.

Dataset Structure

The directory structure of ACDC is as follows:

{root}/{type}/{condition}/{split}/{sequence}/{sequence}_frame_{frame:0>6}_{type}{ext}`

The meaning of the individual directory levels is:

• root the root directory where the dataset is stored.
• type the type/modality of data, e.g. rgb_anon for anonymized RGB images, or gt for ground-truth annotations.
• condition the adverse condition associated with the data, e.g. night.
• split the split, e.g. train. Note that not all types of data exist for all splits.
• sequence the image sequence, e.g. GOPR0351.

Possible values of type:

• rgb the original RGB png images in 8-bit format.
• rgb_anon the anonymized RGB png images in 8-bit format.
• gt the pixel-level semantic annotations and invalid mask annotations. Annotations are encoded using png images, where pixel values encode labels. There are three different formats for the semantic annotations and two formats for the invalid mask annotations. Details on these formats are given below.

For benchmarking on annotated normal-condition images, a test_ref_image_list.txt file which includes the paths to the set of annotated images in the test_ref split is provided under gt.

Possible values of condition:

• fog
• night
• rain
• snow

Possible values of split:

• train training set. It contains 400 images from each of the four examined adverse conditions.
• train_ref corresponding normal-condition images for the training set.
• val validation set. It contains 100 images from each of fog, rain, and snow, and 106 images from night.
• val_ref corresponding normal-condition images for the validation set.
• test test set. It contains 500 images from each of the four examined adverse conditions.
• test_ref corresponding normal-condition images for the test set.

The meaning of the individual constituents of the file name format is:

• ext an optional identifier _ref for files in the splits train_ref, val_ref and test_ref, followed by an optional suffix _{suffix} (only relevant for gt files), in turn followed by the extension of the file , e.g. _ref_labelIds.png for semantic annotations of corresponding normal-condition images.
• frame:0>6 the frame number within the respective sequence, composed of six digits.

Possible values of suffix (for gt files):

• labelIds semantic labels encoded using png images, where pixel values encode labels in Cityscapes IDs format. Please refer to the script helpers/labels.py in the Cityscapes GitHub repository for details on the 19 semantic classes included in our semantic annotations, which coincide with the classes that are included in Cityscapes evaluation.
• labelTrainIds semantic labels encoded using png images, where pixel values encode labels in Cityscapes trainIDs format.
• labelColor semantic labels encoded using png images, where pixel values encode labels in Cityscapes color format. Purposed for visualization.
• invIds invalid masks encoded using 8-bit png images, where the value of invalid pixels is set to 1 and the value of valid pixels is set to 0. This suffix is only relevant for files that are not in the *_ref splits.
• invGray invalid masks encoded using 8-bit png images, where the value of invalid pixels is set to 255 and the value of valid pixels is set to 0. Purposed for visualization. This suffix is only relevant for files that are not in the *_ref splits.

Dataset Structure for detection and panoptic segmentation annotations

The provided detection annotations follow the COCO format.

The directory structure of ACDC detection annotations is as follows:

├── fog
│   ├── instancesonly_fog_test_image_info.json
│   ├── instancesonly_fog_train_gt_detection.json
│   └── instancesonly_fog_val_gt_detection.json
├── night
│   ├── instancesonly_night_test_image_info.json
│   ├── instancesonly_night_train_gt_detection.json
│   └── instancesonly_night_val_gt_detection.json
├── rain
│   ├── instancesonly_rain_test_image_info.json
│   ├── instancesonly_rain_train_gt_detection.json
│   └── instancesonly_rain_val_gt_detection.json
└── snow
│   ├── instancesonly_snow_test_image_info.json
│   ├── instancesonly_snow_train_gt_detection.json
│   └── instancesonly_snow_val_gt_detection.json
├── instancesonly_test_image_info.json
├── instancesonly_test_ref_image_info.json
├── instancesonly_train_gt_detection.json
├── instancesonly_train_gt_ref_detection.json
├── instancesonly_val_gt_detection.json
└── instancesonly_val_gt_ref_detection.json

Detection annotation json files for different conditions are placed in the path

{root}/gt_detection/{condition}/instancesonly_{condition}_{split}_{type}.json

Here condition is optional. It could be fog, night, rain, snow or it can be empty.

type could be gt_detection, gt_ref_detection or image_info. gt_detection refers to ground-truth annotations, which is applied to the train and val splits. gt_ref_detection refers to ground-truth annotations for corresponding normal-condition images, which is applied to the train_ref and val_ref splits. image_info only contains the list of images and the predefined categories, which is applied to the test and test_ref splits.

The provided panoptic segmentation annotations also follow the COCO format.

The directory structure of ACDC panoptic segmentation annotations is as follows:

├── fog
│   ├── fog_test_image_info.json
│   ├── fog_train_gt_panoptic.json
│   ├── fog_val_gt_panoptic.json
│   ├── test
│   ├── train
│   ├── val
│   ├── test_ref
│   ├── train_ref
│   └── val_ref
├── night
│   ├── night_test_image_info.json
│   ├── night_train_gt_panoptic.json
│   ├── night_val_gt_panoptic.json
│   ├── test
│   ├── train
│   ├── val
│   ├── test_ref
│   ├── train_ref
│   └── val_ref
├── rain
│   ├── rain_test_image_info.json
│   ├── rain_train_gt_panoptic.json
│   ├── rain_val_gt_panoptic.json
│   ├── test
│   ├── train
│   ├── val
│   ├── test_ref
│   ├── train_ref
│   └── val_ref
├── snow
│   ├── snow_test_image_info.json
│   ├── snow_train_gt_panoptic.json
│   ├── snow_val_gt_panoptic.json
│   ├── test
│   ├── train
│   ├── val
│   ├── test_ref
│   ├── train_ref
│   └── val_ref
├── test_image_info.json
├── test_ref_image_info.json
├── train_gt_panoptic.json
├── train_gt_ref_panoptic.json
├── val_gt_panoptic.json
└── val_gt_ref_panoptic.json

For panoptic segmentation, the semantic annotation json files for different conditions are placed in the path

{root}/gt_panoptic/{condition}/{condition}_{split}_{type}.json`

Here condition is optional. It could be fog, night, rain, snow or it can be empty.

type could be gt_panoptic, gt_ref_panoptic or image_info. gt_panoptic refers to ground-truth annotations, which is applied to the train and val splits. gt_ref_panoptic refers to ground-truth annotations for corresponding normal-condition images, which is applied to the train_ref and val_ref splits. image_info only contains the list of images and the predefined categories, which is applied to the test and test_ref splits.

The pixel-level panoptic mask PNG files are placed in the path

{root}/gt_panoptic/{condition}/{split}/{sequence}/{sequence}_frame_{frame:0>6}_{type}.png`

Citation

If you use ACDC in your work, please cite our publications as listed on the ACDC website.

License

ACDC is made available for non-commercial use under the license agreement which is contained in the attached file License.pdf.

Contact

Please feel free to contact us with any questions or comments:

Christos Sakaridis, Haoran Wang, Dengxin Dai acdc.dataset [at] zohomail.eu https://acdc.vision.ee.ethz.ch