keremberke/yolov8m-csgo-player-detection
Object Detection
•
Updated
•
4.29k
•
6
image_id
int64 0
3.88k
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416
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119,
37,
35
],
[
33,
118,
99,
197
]
],
"category": [
3,
2
]
} |
|
169 | 416 | 416 | {
"id": [
385,
386
],
"area": [
1295,
19503
],
"bbox": [
[
68,
119,
37,
35
],
[
33,
118,
99,
197
]
],
"category": [
3,
2
]
} |
|
3,868 | 416 | 416 | {
"id": [
8095,
8096
],
"area": [
13172,
728
],
"bbox": [
[
61,
237,
74,
178
],
[
80,
238,
26,
28
]
],
"category": [
2,
3
]
} |
|
966 | 416 | 416 | {
"id": [
2047,
2048
],
"area": [
13172,
728
],
"bbox": [
[
61,
237,
74,
178
],
[
80,
238,
26,
28
]
],
"category": [
2,
3
]
} |
|
1,052 | 416 | 416 | {
"id": [
2234,
2235
],
"area": [
13172,
728
],
"bbox": [
[
61,
237,
74,
178
],
[
80,
238,
26,
28
]
],
"category": [
2,
3
]
} |
|
3,619 | 416 | 416 | {
"id": [
7573
],
"area": [
2288
],
"bbox": [
[
0,
171,
22,
104
]
],
"category": [
3
]
} |
|
856 | 416 | 416 | {
"id": [
1843
],
"area": [
2288
],
"bbox": [
[
0,
171,
22,
104
]
],
"category": [
3
]
} |
End of preview. Expand
in Dataset Viewer.
Dataset Labels
['ct', 'cthead', 't', 'thead']
Number of Images
{'train': 3879, 'valid': 383, 'test': 192}
How to Use
- Install datasets:
pip install datasets
- Load the dataset:
from datasets import load_dataset
ds = load_dataset("keremberke/csgo-object-detection", name="full")
example = ds['train'][0]
Roboflow Dataset Page
https://universe.roboflow.com/asd-culfr/wlots/dataset/1
Citation
@misc{ wlots_dataset,
title = { wlots Dataset },
type = { Open Source Dataset },
author = { asd },
howpublished = { \\url{ https://universe.roboflow.com/asd-culfr/wlots } },
url = { https://universe.roboflow.com/asd-culfr/wlots },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { may },
note = { visited on 2023-01-27 },
}
License
CC BY 4.0
Dataset Summary
This dataset was exported via roboflow.com on December 28, 2022 at 8:08 PM GMT
Roboflow is an end-to-end computer vision platform that helps you
- collaborate with your team on computer vision projects
- collect & organize images
- understand unstructured image data
- annotate, and create datasets
- export, train, and deploy computer vision models
- use active learning to improve your dataset over time
It includes 4454 images. Ct-cthead-t-thead are annotated in COCO format.
The following pre-processing was applied to each image:
- Auto-orientation of pixel data (with EXIF-orientation stripping)
- Resize to 416x416 (Fill (with center crop))
The following augmentation was applied to create 3 versions of each source image:
- Random brigthness adjustment of between -15 and +15 percent
- Downloads last month
- 3
Size of downloaded dataset files:
116 MB
Size of the auto-converted Parquet files:
117 MB
Number of rows:
4,463
Models trained or fine-tuned on keremberke/csgo-object-detection