Dataset Versions

v3045

2024-01-31 11:07am

Generated on Jan 31, 2024

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YOLOv11

TXT annotations and YAML config used with YOLOv11.

YOLOv9

TXT annotations and YAML config used with YOLOv9.

YOLOv8

TXT annotations and YAML config used with YOLOv8.

YOLOv5

TXT annotations and YAML config used with YOLOv5.

YOLOv7

TXT annotations and YAML config used with YOLOv7.

COCO JSON

COCO JSON annotations are used with EfficientDet Pytorch and Detectron 2.

YOLO Darknet

Darknet TXT annotations used with YOLO Darknet (both v3 and v4) and YOLOv3 PyTorch.

Pascal VOC XML

Common XML annotation format for local data munging (pioneered by ImageNet).

TFRecord

TFRecord binary format used for both Tensorflow 1.5 and Tensorflow 2.0 Object Detection models.

PaliGemma

PaliGemma JSONL format used for fine-tuning PaliGemma, Google's open multimodal vision model.

CreateML JSON

CreateML JSON format is used with Apple's CreateML and Turi Create tools.

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5497 Total Images

View All Images

Dataset Split

Train Set 90%

4940Images

Valid Set 6%

350Images

Test Set 4%

207Images

Preprocessing

Auto-Orient: Applied

Isolate Objects: Applied

Augmentations

Outputs per training example: 2

Flip: Horizontal

Similar Projects

Annotations

Group:

vehicle-FxUj

527.jpg

42x95

0.00MP

Updated Jul 8, 56052

5:38AM

GMT+00:00

Generated by Roboflow

Training Set

Transforms

Auto-Orient

Applied

Static Crop

X: 87.019231% to 97.355769%
Y: 68.149038% to 90.985577%

Select annotation #1

Flip

Horizontal

Raw Data

Source Data

                            {
    "camera": "Generated by Roboflow",
    "classes": {
        "* collaborate with your team on computer vision projects": 1,
        "==============================": 213,
        "Roboflow is an end-to-end computer vision platform that helps you": 135,
        "This dataset was exported via roboflow.com on November 25, 2023 at 4:44 PM GMT": 71,
        "Vehicle Detection YOLO V5 - v1 YOLO v5 vehicle detection": 3344,
        "heavy vehicle": 0,
        "lightvehicle": 2
    },
    "datasets": [
        "DfU9yY9p0O7KQjSZe4SK"
    ],
    "destination": "32005c9dd87bac18f74c1bbe76ef1e72",
    "height": 95,
    "id": "m0uxFSwDdvxXSxfg7aqE",
    "label": [],
    "labels": [],
    "name": "527.jpg",
    "numSteps": 4,
    "owner": "26D5fqP0Y2Sv8oseGD5mV8IfSvy2",
    "preprocessing": [
        "auto-orient",
        "static-crop:[\"87.019231\",\"97.355769\",\"68.149038\",\"90.985577\"]",
        "select-annotation:[0]",
        "flip:[\"horizontal\"]"
    ],
    "preprocessingParsed": [
        {
            "name": "Auto-Orient",
            "value": "Applied"
        },
        {
            "name": "Static Crop",
            "value": "X: 87.019231% to 97.355769%<br/>Y: 68.149038% to 90.985577%"
        },
        {
            "value": "Select annotation #1"
        },
        {
            "name": "Flip",
            "value": "Horizontal"
        }
    ],
    "source": "m0uxFSwDdvxXSxfg7aqE",
    "split": "train",
    "split.DfU9yY9p0O7KQjSZe4SK.3045": "train",
    "status": "generated",
    "transforms": "[\n    \"auto-orient\",\n    \"static-crop:[\\\"87.019231\\\",\\\"97.355769\\\",\\\"68.149038\\\",\\\"90.985577\\\"]\",\n    \"select-annotation:[0]\",\n    \"flip:[\\\"horizontal\\\"]\"\n]",
    "updated": 1706679437892,
    "updatedDate": "Jul 8, 56052",
    "updatedTime": "5:38AM",
    "updatedTimezone": "+00:00",
    "versions": [
        "DfU9yY9p0O7KQjSZe4SK/3045"
    ],
    "width": 42
}

Annotation Data

{
    "boxes": [
        {
            "label": "Vehicle Detection YOLO V5 - v1 YOLO v5 vehicle detection",
            "x": 21,
            "y": 47.5,
            "width": 42,
            "height": 95
        }
    ],
    "height": 95,
    "key": "527_jpg.rf.258144ced92b403af88d3a3bbc96a68c.jpg",
    "width": 42
}

blind curve accident