TFRecord

TFRecord is a very flexible format, but we try to correspond the format that used in TF object detection with minimal modifications.

Used feature description:

image_feature_description = {
    'image/filename': tf.io.FixedLenFeature([], tf.string),
    'image/source_id': tf.io.FixedLenFeature([], tf.string),
    'image/height': tf.io.FixedLenFeature([], tf.int64),
    'image/width': tf.io.FixedLenFeature([], tf.int64),
    # Object boxes and classes.
    'image/object/bbox/xmin': tf.io.VarLenFeature(tf.float32),
    'image/object/bbox/xmax': tf.io.VarLenFeature(tf.float32),
    'image/object/bbox/ymin': tf.io.VarLenFeature(tf.float32),
    'image/object/bbox/ymax': tf.io.VarLenFeature(tf.float32),
    'image/object/class/label': tf.io.VarLenFeature(tf.int64),
    'image/object/class/text': tf.io.VarLenFeature(tf.string),
}

TFRecord export

Downloaded file: a zip archive with following structure:

taskname.zip/
├── default.tfrecord
└── label_map.pbtxt

# label_map.pbtxt
item {
	id: 1
	name: 'label_0'
}
item {
	id: 2
	name: 'label_1'
}
...

supported annotations: Rectangles, Polygons (as masks, manually over Datumaro)

How to export masks:

Export annotations in Datumaro format
Apply polygons_to_masks and boxes_to_masks transforms

datum transform -t polygons_to_masks -p path/to/proj -o ptm
datum transform -t boxes_to_masks -p ptm -o btm

Export in the TF Detection API format

datum export -f tf_detection_api -p btm [-- --save-images]

TFRecord import

Uploaded file: a zip archive of following structure:

taskname.zip/
└── <any name>.tfrecord

supported annotations: Rectangles

How to create a task from TFRecord dataset (from VOC2007 for example)

Create label_map.pbtxt file with the following content:

item {
    id: 1
    name: 'aeroplane'
}
item {
    id: 2
    name: 'bicycle'
}
item {
    id: 3
    name: 'bird'
}
item {
    id: 4
    name: 'boat'
}
item {
    id: 5
    name: 'bottle'
}
item {
    id: 6
    name: 'bus'
}
item {
    id: 7
    name: 'car'
}
item {
    id: 8
    name: 'cat'
}
item {
    id: 9
    name: 'chair'
}
item {
    id: 10
    name: 'cow'
}
item {
    id: 11
    name: 'diningtable'
}
item {
    id: 12
    name: 'dog'
}
item {
    id: 13
    name: 'horse'
}
item {
    id: 14
    name: 'motorbike'
}
item {
    id: 15
    name: 'person'
}
item {
    id: 16
    name: 'pottedplant'
}
item {
    id: 17
    name: 'sheep'
}
item {
    id: 18
    name: 'sofa'
}
item {
    id: 19
    name: 'train'
}
item {
    id: 20
    name: 'tvmonitor'
}

Use create_pascal_tf_record.py

to convert VOC2007 dataset to TFRecord format. As example:

python create_pascal_tf_record.py --data_dir <path to VOCdevkit> --set train --year VOC2007 --output_path pascal.tfrecord --label_map_path label_map.pbtxt

Zip train images

cat <path to VOCdevkit>/VOC2007/ImageSets/Main/train.txt | while read p; do echo <path to VOCdevkit>/VOC2007/JPEGImages/${p}.jpg  ; done | zip images.zip -j -@

Create a CVAT task with the following labels:

aeroplane bicycle bird boat bottle bus car cat chair cow diningtable dog horse motorbike person pottedplant sheep sofa train tvmonitor

Select images. zip as data. See Creating an annotation task guide for details.

Zip pascal.tfrecord and label_map.pbtxt files together

zip anno.zip -j <path to pascal.tfrecord> <path to label_map.pbtxt>

Click Upload annotation button, choose TFRecord 1.0 and select the zip file

with labels from the previous step. It may take some time.

Last modified June 22, 2021 : Update documentation sorting (#3320) (7cf6c7a0)