RectLabel

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Post the problem to our Github issues.

Have questions? Send an email to support@rectlabel.com.

Export menus

Export Create ML JSON file

Annotation files are exported as a Create ML JSON file.

createml

[{
    "image": "sneakers-1.jpg",
    "annotations": [
    {
        "label": "sneakers",
        "coordinates":
        {
            "y": 838,
            "x": 393,
            "width": 62,
            "height": 118
        }
    },
    {
        "label": "sneakers",
        "coordinates":
        {
            "y": 881,
            "x": 392,
            "width": 51,
            "height": 102
        }
    }]
}]

Import Create ML JSON file

Export images for classification

All images are exported into object-named subfolders.

classification

└── saved_folder
    ├── object_name0
    ├── object_name1
    └── object_name2

Export COCO JSON file

Annotation files are exported as a COCO JSON file. This format is for Detectron2.

coco_export

For a box object, “segmentation” is exported as empty.

"annotations": [
{
    "area": 254521,
    "bbox": [2150, 419, 595, 428],
    "category_id": 14,
    "id": 1,
    "image_id": 1,
    "iscrowd": 0,
    "segmentation": []
},

For a rotated box/polygon/line/point object, “segmentation” is exported as polygons.

"annotations": [
{
    "area": 164608,
    "bbox": [132, 417, 594, 432],
    "category_id": 14,
    "id": 1,
    "image_id": 1,
    "iscrowd": 0,
    "segmentation": [
        [136, 557, 152, 532, 191, 509, 266, 482, 367, 456, 375, 428, 427, 417, 486, 443, 516, 481, 518, 499, 564, 522, 611, 518, 661, 536, 701, 557, 724, 574, 719, 597, 691, 645, 723, 654, 715, 678, 695, 719, 681, 759, 681, 791, 670, 801, 659, 789, 656, 765, 627, 756, 644, 778, 629, 832, 591, 842, 553, 834, 514, 809, 494, 781, 491, 767, 433, 769, 403, 761, 405, 794, 387, 823, 369, 840, 344, 847, 309, 837, 295, 810, 286, 776, 290, 755, 297, 741, 259, 723, 216, 693, 179, 658, 147, 629, 132, 601, 132, 577]
    ]
},

For a pixels object, “segmentation” is exported as RLE. RLE is encoding the mask image using the COCO API.

"annotations": [
{
    "area": 1022954,
    "bbox": [212, 0, 4204, 2960],
    "category_id": 26,
    "id": 1,
    "image_id": 1,
    "iscrowd": 0,
    "segmentation":
    {
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        "size": [3317, 4417]
    }
},

For a keypoints object, “keypoints” and “num_keypoints” are exported.

"annotations": [
{
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    "bbox": [732, 1446, 864, 1309],
    "category_id": 1,
    "id": 1,
    "image_id": 1,
    "iscrowd": 0,
    "keypoints": [1108, 1633, 2, 1104, 1603, 2, 1112, 1596, 1, 1149, 1593, 2, 1146, 1582, 1, 1108, 1730, 2, 1334, 1687, 2, 1061, 1890, 2, 1387, 1936, 2, 1017, 1665, 2, 1458, 2106, 2, 1160, 2060, 2, 1299, 2053, 2, 1174, 2291, 2, 1196, 2291, 2, 1321, 2609, 2, 1196, 2590, 2],
    "num_keypoints": 17,
    "segmentation": [
        [986, 1654, 1030, 1613, 1087, 1596, 1105, 1511, 1168, 1485, 1183, 1449, 1216, 1446, 1239, 1496, 1260, 1557, 1236, 1619, 1267, 1662, 1359, 1678, 1411, 1864, 1477, 2065, 1494, 2157, 1594, 2367, 1454, 2488, 1330, 2578, 1359, 2618, 1409, 2659, 1384, 2695, 1319, 2754, 1303, 2741, 1300, 2710, 1250, 2702, 1099, 2720, 1048, 2705, 1042, 2673, 1105, 2655, 1159, 2601, 1165, 2485, 1054, 2414, 895, 2340, 789, 2304, 732, 2278, 794, 2135, 889, 2013, 970, 1913, 968, 1714]
    ]
},

"annotations": [
{
    "area": 2977340,
    "bbox": [1435, 1125, 1105, 3708],
    "category_id": 1,
    "id": 1,
    "image_id": 1,
    "iscrowd": 0,
    "keypoints": [1904, 1495, 2, 1970, 1417, 2, 1822, 1433, 2, 2068, 1481, 2, 1730, 1533, 2, 2308, 1951, 2, 1595, 2075, 2, 2369, 2555, 2, 1561, 2614, 2, 2371, 3076, 2, 1508, 3136, 2, 2172, 3035, 2, 1753, 3101, 2, 2280, 4034, 2, 1806, 4124, 2, 2367, 4833, 1, 1842, 4833, 1],
    "num_keypoints": 17,
    "segmentation":
    {
        "counts": "ZUhc6a0af4b0]Oc0^Oa0^Oc0]Ob0_Ob0]Oc0]Ob0_Ob0]Oc0nnKjJS_3h5T`LkKa^3g4d`LmLP^3f3VaLmM_]3d2haLoNl\\3d1YbLi8bS3jGdkLg8ST3iGTkLi8bT3gGgjLj8oT3gGYjLj8^U3eGjiLm8lU3dG\\iLm8[V3fGihLl8mV3oGQhLb8eW3YHXgLZ8^X3aH_fLP8WY3kHgeLf7oY3UIndL]7iZ3]IUdLT7a[3`b0G9F:F9H1N2N2O1N2O1N2N2O1N2N2O1N2O1N2N2O1N2N2K5J6J6J6K5J6J6J6J6J6J6J6J5K6J6J6J6J6J6J6J6J6J6J6J6J6PgMn]Nd[1]c1WdNh\\NVZ1kd1eeNY[NoY1Te1leNP[NSZ1Re1heNS[NVZ1od1eeNU[NZZ1md1aeNW[N^Z1kd1]eNY[NbZ1id1YeN\\[NeZ1fd1VeN^[NiZ1dd1ReN`[NlZ1cd1odNb[NoZ1`d1ldNd[NS[1^d1hdNf[NW[1\\d1ddNh[N[[1Zd1bdNi[N[[1Zd1bdNh[N\\[1[d1`dNh[N^[1[d1_dNh[N_[1Zd1^dNh[N`[1[d1]dNg[Na[1\\d1\\dNg[Na[1\\d1\\dNf[Nc[1\\d1ZdNf[Nd[1]d1YdNe[Ne[1^d1XdNe[Nf[1]d1VdNf[Nh[1]d1UdNe[Ni[1]d1UdNf[Nh[1]d1UdNe[Nj[1]d1SdNe[Nk[1^d1RdNd[N^[1md1_dNV[NlZ1_e1QeNcZNZZ1Sf1ceNoYNTZ1[f1heNiYNTZ1[f1ieNgYNUZ1\\f1heNfYNUZ1^f1heNeYNUZ1^f1heNdYNUZ1`f1heNbYNVZ1af1geNaYNVZ1bf1heNaYNTZ1cf1ieN_YNUZ1cf1heN`YNUZ1cf1ieN`YNTZ1cf1ieN_YNTZ1df1jeN^YNTZ1ef1ieN]YNTZ1ff1jeN]YNSZ1ef1keN]YNRZ1gf1keN[YNSZ1gf1jeN]YNoV1ji1nhNXVNPW1ji1nhNXVNoV1ki1ohNWVNnV1mi1ohNVVNmV1mi1QiNUVNlV1ni1RiNTVNkV1Pj1RiNSVNjV1Pj1TiNRVNjV1Qj1RiNRVNkV1Qj1SiNRVNiV1Qj1UiNQVNhV1ej1chN]UNZW1[k1ogNgTNnW1Pl1\\gNSTNaX1dl1hfN^SNUY1Ym1UfNiRNhY1mm1ceNVRNYZ1Xn1XeNlQNeZ1an1mdNcQNP[1jn1cdNYQN`CdIWd1aU2jgNPQNkCcIXd1iU2_gNhPNVDbIXd1TV2SgN^PNbDaIXd1^V2ifNUPNlD`IWd1iV2^fNkoMXE_IWd1SW2SfNboMcE^IWd1]W2heNZoMmE]IXd1fW2\\eNQoMZF[IWd1QX2ReNgnMdF[IWd1\\X2fdN]nMPG[IVd1eX2\\dNUnMZGYIWd1oX2QdNlmMeGXIWd1ZY2fcNamMQHXIUd1gY2YcNVmM^HVIWd1RZ2kbNllMkHUIWd1_Z2]bN`lMYIUIVd1jZ2RbNTlMfITIUd1W[2eaNjkMRJRIVd1d[2WaN^kM`JRIUd1o[2k`NSkMmJQIVd1[\\2\\`NijM[KnHVd1h\\2l_NajMkKkHUd1T]2S_NajMeL^H?mMfa1c_2Y`NcjM_MPH=oMha1m_2`_NejMXNbG:QNka1X`2f^NejMROUG7UNma1``2n]NfjMKhF5WNPb1j`2R]NijMe0ZF3XNSb1]m2h]N]TM0[NUb1[m2h]N]TMM^NXb1Ym2g]N]TMK_N[b1Wm2g]N]TMIaN]b1Um2h]N\\TMFdN`b1Rm2g]N[VMVb1ii2f]NZVMWb1ii2f]N[VMVb1hi2h]NZVMUb1[o2M3M3N2O1O100O1O1O2M2O1O1O1O1O1O1O1O1O2N1O1N2O1O1O1O1O1O1O1O2N1O1O1N2O1O1O1O1O1O1O2N1O1O1O1N200O1O1O1O2N1O100O1O1O1O1O1O100O2N1O1O1O1O1O100O1O1O2N1O1O100O1O1O1O1O1O101N1O1O1O1O1O100O1O1O9G?A>B?A>C=B?A>B?A>B>B?B4K5K5K4L5L4K5K5K5K5L4K5K4L5K5L4K5K5K5L4K4L5K5K5L4K5K5K5K4M4K1O1O100O1O1O1O100O1O1O1O100O1O1O100O1O1O1O100O10000O100O10000O10000O100O10000O10000O100O10000O10000O100O10000O100O10000O10000O100O10000O10000O100O10000O10000O100O10000O100O10000O10000O100O10000O100O1O100O1O1O100O100O100O100O100O100O100O100O100O100O10000O100O100O100O100O100O100O100O100O100O100O100O1000000001O00001O0000001O0000001O0000001O0000001O00000`N`1UMl2UOj0ZOf0ZOf0YOh0YOf0ZOg0N2O1O001O1O1O0O2O1O1O001O1O1O0O2O1O00001O00001N10001O0000001O0O101O1O1O1O1O1O1N2O1O1O1O1O1O1O1N2O1O1O1O1O1O001N2O1O1O1O1O1O1O1jhL\\bNkT3f]1TkL[bNkT3f]1TkLZbNlT3g]1SkLZbNlT3g]1SkLYbNmT3h]1RkLYbNmT3h]1RkLXbNnT3i]1QkLXbNnT3i]1QkLWbNnT3k]1QkLVbNnT3k]1QkLUbNoT3l]1PkLUbNoT3l]1PkLTbNPU3m]1ojLTbNPU3l]1ojLUbNQU3l]1mjLUbNSU3l]1kjLVbNSU3l]1ljLTbNTU3m]1jjLUbNUU3l]1ijLUbNWU3l]1gjLVbNXU3k]1fjLVbNZU3k]1ejLVbNZU3k]1djLWbN[U3j]1cjLYbNZU3i]1djLYbN[U3h]1cjLZbN\\U3g]1bjL[bN\\U3g]1cjL[bN[U3f]1cjL\\bN\\U3e]1\\jLcbNbU3_]1RjLmbNmU3V_1N1N2O2N1N2O1N3N1O1N2O2N2M3N3L3N2N2N2O2M2N2N2N2N3M2O1N2N2N3M2N2N2O1N3M2N2N2N2N3N1N2N2N2N2N1O2O1N2N2N2N2N1O2O1N2N2N2N2N5K4M4K4L5K4L5K4L5K4L5K4L5K4L5K>B?A?A?A?A`0@5K1N3N2N1O2N1O2N1O2N2N1O2N1O2N2N1O2M4M3M2N3M3M2N3M3M2N3M3M2N3M2N3M2M4L4L3M4kYMjSNXa2Xl1d^MkSNZa2Yl1a^MiSN^a2Zl1^^MhSNaa2\\l1Y^MhSNea2[l1W^MgSNia2\\l1R^MfSNma2]l1o]MeSNPb2_l1k]MdSNSb2_l1h]MdSNWb2`l1d]MbSN[b2bl1`]MaSN_b2fl1X]M\\SNgb2kl1Q]MWSNnb2Qm1i\\MRSNUc2Um1b\\MnRN]c2Zm1Z\\MhRNfc2_m1Q\\MdRNmc2cm1k[M_RNTd2im1c[MYRN\\d2om1Z[MURNed2Qn1S[MQRNmd2Vn1jZMlQNVe2QP2O00001O0000001O00001O00001O00001O00001O0003N1N3M2O2M2N3M2O2M2N2O2M2N3N1N3M2O2M2N2O2M2N3N1N3M2O2M2ZWNTYMRa1of2e^NWYM[a1kf2]^N[YMda1ff2Z^NZYMfa1if2W^NWYMia1kf2T^NVYMma1lf2P^NTYMPb1nf2m]NSYMSb1Pg2j]NPYMWb1Qg2g]NoXMYb1Tg2c]NmXM]b1Ug2a]NkXM`b1Wg2\\]NjXMdb1Xg2Z]NhXMfb1\\g2U]NeXMlb1]g2Q]NcXMob1`g2n\\N`XMRc1cg2W[NlWMcMb0Wg1dg2Q[NmWMgM?Xg1gg2lZNnWMiM<[g1ig2hZNmWMlM:]g1kg2bZNnWMoM8_g1mg2^ZNnWMQN5ag1Qh2XZNmWMVN2bg1Th2SZNmWMYN0eg1Uh2nYNnWM[NMgg1Xh2iYNnWM^NKig1Zh2eYNmWMaNIkg1\\h2_YNoWMdNEmg1_h2[YNnWMfNDog1ah2VYNnWMjNAQh1ch2PYNPXMlN^OTh1fh2kXNnWMPO\\OUh1ih2fXNnWMSOZOXh1jh2aXNoWMUOWOZh1mh2\\XNoWMYOTO[h1Pi2WXNoWM\\ORO^h1Ri2QXNoWM_OoN`h1Ui2lWNoWMBmNbh1Xi2gWNmWMFkNdh1Zi2aWNoWMIgNfh1^i2[WNnWMLfNih1_i2WWNnWMLeNnh1_i2QWNQYMoh1aj200001O000000000000001O000000000000001O000000000000001O000000000000001O0000000000001O000000000000001O001O001O001O001O001O001O001O001O001O001O001O001O001O1O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O2N3M2N2N3M2N3M2N2N3M2N3M2N3M2N2N3M2N3UC]TNQnMek1mQ2dTNjmM_k1SR2kTNcmMWk1[R2SUN[mMoj1VX1TTN\\VOW1TAhj1RX1nTNXVOd0\\A`j1oW1hUNTVO2cAZj1jW1`VNRVO@jAWj1bW1UWNSVOnNQBSj1[W1lWNRVO[NYBPj1RW1bXNTVOhM`Bmi1jV1WYNPgNZJU7l2gJii1[ObSNjm0\\6cmNWLa:k1nHWh1KiTN\\n0j5PkNUNk=l0RGcf1S1[UNoLUN^l0V>]^O\\I^ETe1V2lUN^LlN_l0U=^_OQI]EYe1e1]VNnK[Ogl0Z]1M4L4L4LlXZS5",
        "size": [4834, 3648]
    }
},

In “categories”, “keypoints” and “skeleton” are exported.

"categories": [
{
    "id": 1,
    "keypoints": ["nose", "leftEye", "rightEye", "leftEar", "rightEar", "leftShoulder", "rightShoulder", "leftElbow", "rightElbow", "leftWrist", "rightWrist", "leftHip", "rightHip", "leftKnee", "rightKnee", "leftAnkle", "rightAnkle"],
    "name": "person",
    "skeleton": [
        [9, 11],
        [6, 12],
        [14, 16],
        [7, 13],
        [15, 17],
        [12, 13],
        [14, 12],
        [8, 6],
        [10, 8],
        [6, 7],
        [9, 7],
        [15, 13],
        [5, 3],
        [3, 1],
        [1, 2],
        [2, 4]
    ]
},

Import COCO JSON file

Import COCO JSON per image files

You can import the COCO RLE JSON files of the SA-1B dataset.

{
    "image":
    {
        "image_id": 1,
        "width": 1500,
        "height": 2060,
        "file_name": "sa_1.jpg"
    },
    "annotations": [
    {
        "bbox": [866.0, 946.0, 132.0, 199.0],
        "area": 14773,
        "segmentation":
        {
            "size": [2060, 1500],
            "counts": "TS_f15SP27K3N2iTNHWf1:bYN0Yf12cYN1\\f11mWN7SNKni11PVNS2OmMPj15aUN\\2;aMSj1m3`UNVL_j1m4N1O1O1O10000O10O10O100000000O10000O100O1O100O101O000000000O10O101N1N2N2O1O100O100\\KhUNT3Wj1jLnUNS3Tj1kLoUNR3Rj1mLTVNW3ci1hL`VNW3_i1hLdVNV3\\i1iLfVNV3Zi1jLgVNU3Yi1jLiVNV3Vi1jLlVNT3Ti1kLnVNT3Ri1lLoVNS3Qi1lLQWNS3oh1mLRWNR3nh1nLSWNQ3mh1nLVWNP3jh1PMWWNo2ih1QMXWNl2jh1TMWWNg2mh1XMUWNV1gNlN_j1NkVNS1jNkN]j12jVNQ1lNiN\\j16jVNm0mNjN[j19iVNk0nNjNZj1;iVNh0QOkNVj1=jVNe0TOjNTj1a0jVN3EXObi1e0YYNVOjf1j0\\4001O00001O00001O00001O10O01O001O1O1O1O1O2N1O1O1O1O1O1O100O101N10000O00100O1O1O100O1O1O0000lNRRNGQn17S1O2N1O101M4Mom^o0"
        },
        "predicted_iou": 0.9523417353630066,
        "point_coords": [
            [940.9375, 1034.5625]
        ],
        "crop_box": [622.0, 902.0, 567.0, 707.0],
        "id": 523353737,
        "stability_score": 0.9742233753204346
    },
    ...
}

Export YOLO txt files

Annotation files are exported as YOLO text files. A YOLO text file is saved per an image.

yolo_export

├── datasets
│   └── sneakers
│       ├── images
│       └── labels
└── yolov5
    └── data
        └── sneakers.yaml

For a box object, the bounding box is saved. center_x, center_y, width, and height are float values relative to width and height of the image.

class_index center_x center_y width height
0 0.464615 0.594724 0.680000 0.769784

For a rotated box/polygon/cubic bezier/line/point/pixels object, the points coordinates are saved. This format is for YOLOv5 and YOLOv8/YOLO11 Instance Segmentation.

class_index x1 y1 x2 y2 x3 y3 ...
0 0.180027 0.287930 0.181324 0.280698 0.183726 0.270573 ...

For a keypoints object, the bounding box and the points coordinates are saved. This format is for YOLOv8/YOLO11.

class_index center_x center_y width height x1 y1 v1 x2 y2 v2 x3 y3 v3 ...
0 0.545230 0.616880 0.298794 0.766239 0.522073 0.309332 2 0.540170 0.293193 2 0.499589 0.296503 2 ...

To save a track id for each box, use an attribute of “Text input” type and combine with the object name such as “sneakers-1”, the attribute name should be set as “track id” on the setting. For the YOLO Detection Track format, see the CVAT document.

class_index center_x center_y width height track_id
0 0.464615 0.594724 0.680000 0.769784 1

Import YOLO txt files

Export DOTA txt files

Annotation files are exported as DOTA text files. This format is for MMRotate.

dota_export

Settings menu.

x1 y1 x2 y2 x3 y3 x4 y4 category difficult
1300.536987 1413.503784 1192.848755 1535.568848 530.876038 951.562073 638.564270 829.497009 truck 0

Import DOTA txt files

Export CSV file

Annotation files are exported as a CSV file.

path,annotations
/Users/ryo/rcam/test_annotations/test/_test_min/images1500/daniel-garcia-472792.jpg,[{"label":"sneakers-converse-yellow","type":"rectangle","coordinates":{"x":565,"y":488,"width":300,"height":377}},{"label":"sneakers-converse-yellow","type":"rectangle","coordinates":{"x":907,"y":503,"width":235,"height":265}}]

Import CSV file

Export train/val/test folders

Export object names file

Export a yaml file as dictionary for YOLOv5 and YOLOv8/YOLO11.

path: ../datasets/keypoints
train: images
val: images

kpt_shape: [17, 3]
flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]

names:
  0: person

Export a yaml file as array for YOLOv5.

path: ../datasets/sneakers
train: images
val: images

nc: 2
names: ['sneakers', 'ignore']

Export an object names text file.

sneakers
ignore

Import object names file

You can import an object names file or import object names from xml files.

Export mask images

You can specify which mask image to export.

For the indexed color image.

mask

Export screenshots

You can export images and annotations as jpg images.

View menus.

Export augmented images

You can augment images and annotations using “Flip”, “Crop”, “Contrast”, and “Rotate”.

Settings menu.

augment

Export sliced images

You can slice images and annotations horizontally and vertically.

slice

Export objects and attributes stats