Clarity regarding training data

[pra-dan]

I find much clarity lacking in the training process. Additional info on training dataset format is missing in toolkits/label_conversion/README.md. I understand that it will be update sometime soon.

The docs specify the training data to be formatted as:

# The id represent the correspondence relation
├─dataset root
│ ├─images/ id.jpg
│ ├─det_annotations/ id.json
│ ├─da_seg_annotations/ id.png
│ ├─ll_seg_annotations/ id.png

But the dataset downloaded from the bdd100k site has the following structure.

.
└── segmentation
    ├── __MACOSX
    │   └── test
    ├── test
    │   ├── __MACOSX
    │   │   └── test
    │   └── test
    │       └── raw_images
    ├── train
    │   ├── __MACOSX
    │   │   └── train
    │   └── train
    │       ├── class_color
    │       ├── class_id
    │       ├── instance_color
    │       ├── instance_id
    │       └── raw_images
    └── val
        ├── __MACOSX
        │   └── val
        └── val
            ├── class_color
            ├── class_id
            ├── instance_color
            ├── instance_id
            └── raw_images

1. Its unclear which among instance_color, class_id and instance_id denote det_annotations,da_seg_annotations,
   ll_seg_annotations. All of them are masks. I dont' intend to use the object detection part, so the json conversion shouldn't be very necessary for now.

2. The lib/config/default.py contains params such as

_C.DATASET.DATAROOT = '/home/zwt/bdd/bdd100k/images/100k'       # the path of images folder
_C.DATASET.LABELROOT = '/home/zwt/bdd/bdd100k/labels/100k'      # the path of det_annotations folder
_C.DATASET.MASKROOT = '/home/zwt/bdd/bdd_seg_gt'                # the path of da_seg_annotations folder
_C.DATASET.LANEROOT = '/home/zwt/bdd/bdd_lane_gt'

It would be better if more info can be provided for the paths such that it can be generalised.

[Riser6]

I find much clarity lacking in the training process. Additional info on training dataset format is missing in toolkits/label_conversion/README.md. I understand that it will be update sometime soon.

The docs specify the training data to be formatted as:

# The id represent the correspondence relation
├─dataset root
│ ├─images/ id.jpg
│ ├─det_annotations/ id.json
│ ├─da_seg_annotations/ id.png
│ ├─ll_seg_annotations/ id.png

But the dataset downloaded from the bdd100k site has the following structure.

.
└── segmentation
    ├── __MACOSX
    │   └── test
    ├── test
    │   ├── __MACOSX
    │   │   └── test
    │   └── test
    │       └── raw_images
    ├── train
    │   ├── __MACOSX
    │   │   └── train
    │   └── train
    │       ├── class_color
    │       ├── class_id
    │       ├── instance_color
    │       ├── instance_id
    │       └── raw_images
    └── val
        ├── __MACOSX
        │   └── val
        └── val
            ├── class_color
            ├── class_id
            ├── instance_color
            ├── instance_id
            └── raw_images

1. Its unclear which among instance_color, class_id and instance_id denote det_annotations,da_seg_annotations,
   ll_seg_annotations. All of them are masks. I dont' intend to use the object detection part, so the json conversion shouldn't be very necessary for now.
2. The lib/config/default.py contains params such as

_C.DATASET.DATAROOT = '/home/zwt/bdd/bdd100k/images/100k'       # the path of images folder
_C.DATASET.LABELROOT = '/home/zwt/bdd/bdd100k/labels/100k'      # the path of det_annotations folder
_C.DATASET.MASKROOT = '/home/zwt/bdd/bdd_seg_gt'                # the path of da_seg_annotations folder
_C.DATASET.LANEROOT = '/home/zwt/bdd/bdd_lane_gt'

It would be better if more info can be provided for the paths such that it can be generalised.

Thanks for your suggestion for our project, we will make improvement for it as soon as possible! Hope you can continue to pay attention to our project!

[Riser6]

I find much clarity lacking in the training process. Additional info on training dataset format is missing in toolkits/label_conversion/README.md. I understand that it will be update sometime soon.

The docs specify the training data to be formatted as:

# The id represent the correspondence relation
├─dataset root
│ ├─images/ id.jpg
│ ├─det_annotations/ id.json
│ ├─da_seg_annotations/ id.png
│ ├─ll_seg_annotations/ id.png

But the dataset downloaded from the bdd100k site has the following structure.

.
└── segmentation
    ├── __MACOSX
    │   └── test
    ├── test
    │   ├── __MACOSX
    │   │   └── test
    │   └── test
    │       └── raw_images
    ├── train
    │   ├── __MACOSX
    │   │   └── train
    │   └── train
    │       ├── class_color
    │       ├── class_id
    │       ├── instance_color
    │       ├── instance_id
    │       └── raw_images
    └── val
        ├── __MACOSX
        │   └── val
        └── val
            ├── class_color
            ├── class_id
            ├── instance_color
            ├── instance_id
            └── raw_images

1. Its unclear which among instance_color, class_id and instance_id denote det_annotations,da_seg_annotations,
   ll_seg_annotations. All of them are masks. I dont' intend to use the object detection part, so the json conversion shouldn't be very necessary for now.
2. The lib/config/default.py contains params such as

_C.DATASET.DATAROOT = '/home/zwt/bdd/bdd100k/images/100k'       # the path of images folder
_C.DATASET.LABELROOT = '/home/zwt/bdd/bdd100k/labels/100k'      # the path of det_annotations folder
_C.DATASET.MASKROOT = '/home/zwt/bdd/bdd_seg_gt'                # the path of da_seg_annotations folder
_C.DATASET.LANEROOT = '/home/zwt/bdd/bdd_lane_gt'

It would be better if more info can be provided for the paths such that it can be generalised.

I have uploaded our training label to Google Drive, which can be downloaded through the link in Readme.md. md. Thank you for your attention and suggestions to our project