keremberke/yolov8m-valorant-detection
Object Detection
•
Updated
•
4.37k
•
5
image_id
int64 0
6.93k
| image
image 416
416
| width
int32 416
416
| height
int32 416
416
| objects
sequence |
|---|---|---|---|---|
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3,198 | 416 | 416 | {
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6,759 | 416 | 416 | {
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1,873 | 416 | 416 | {
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4,362 | 416 | 416 | {
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522 | 416 | 416 | {
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4,633 | 416 | 416 | {
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4,915 | 416 | 416 | {
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6,908 | 416 | 416 | {
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6,685 | 416 | 416 | {
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5,970 | 416 | 416 | {
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3,296 | 416 | 416 | {
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4,112 | 416 | 416 | {
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|
799 | 416 | 416 | {
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|
3,703 | 416 | 416 | {
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|
6,894 | 416 | 416 | {
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|
6,166 | 416 | 416 | {
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6,424 | 416 | 416 | {
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|
2,289 | 416 | 416 | {
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4,271 | 416 | 416 | {
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1,808 | 416 | 416 | {
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3,442 | 416 | 416 | {
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1,138 | 416 | 416 | {
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|
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|
3,620 | 416 | 416 | {
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5,253 | 416 | 416 | {
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|
2,166 | 416 | 416 | {
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|
6,886 | 416 | 416 | {
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5,061 | 416 | 416 | {
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|
2,615 | 416 | 416 | {
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|
1,248 | 416 | 416 | {
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|
2,851 | 416 | 416 | {
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157 | 416 | 416 | {
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2,146 | 416 | 416 | {
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3,091 | 416 | 416 | {
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6,333 | 416 | 416 | {
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1,215 | 416 | 416 | {
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|
3,183 | 416 | 416 | {
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|
4,844 | 416 | 416 | {
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|
1,148 | 416 | 416 | {
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|
3,480 | 416 | 416 | {
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|
97 | 416 | 416 | {
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|
2,716 | 416 | 416 | {
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|
6,371 | 416 | 416 | {
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|
1,840 | 416 | 416 | {
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|
2,157 | 416 | 416 | {
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|
221 | 416 | 416 | {
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|
6,049 | 416 | 416 | {
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|
1,654 | 416 | 416 | {
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1
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|
759 | 416 | 416 | {
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|
6,657 | 416 | 416 | {
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|
2,912 | 416 | 416 | {
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|
5,978 | 416 | 416 | {
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1
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|
466 | 416 | 416 | {
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|
6,138 | 416 | 416 | {
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1
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|
5,754 | 416 | 416 | {
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|
6,410 | 416 | 416 | {
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|
4,070 | 416 | 416 | {
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|
2,611 | 416 | 416 | {
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|
4,033 | 416 | 416 | {
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|
6,451 | 416 | 416 | {
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|
4,990 | 416 | 416 | {
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3
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|
2,695 | 416 | 416 | {
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|
6,791 | 416 | 416 | {
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|
3,434 | 416 | 416 | {
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|
4,493 | 416 | 416 | {
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330
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3
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|
23 | 416 | 416 | {
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3
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|
6,123 | 416 | 416 | {
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439 | 416 | 416 | {
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1,376 | 416 | 416 | {
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5,682 | 416 | 416 | {
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5,050 | 416 | 416 | {
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6,279 | 416 | 416 | {
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],
"area": [
255
],
"bbox": [
[
170,
221,
7,
36.5
]
],
"category": [
3
]
} |
|
949 | 416 | 416 | {
"id": [
983
],
"area": [
437
],
"bbox": [
[
163,
220,
11.5,
38
]
],
"category": [
3
]
} |
|
6,911 | 416 | 416 | {
"id": [
7168
],
"area": [
285
],
"bbox": [
[
161,
220,
7.5,
38
]
],
"category": [
3
]
} |
|
0 | 416 | 416 | {
"id": [
0
],
"area": [
315
],
"bbox": [
[
180,
217,
9,
35
]
],
"category": [
3
]
} |
|
5,438 | 416 | 416 | {
"id": [
5630
],
"area": [
408
],
"bbox": [
[
185,
216,
12,
34
]
],
"category": [
3
]
} |
|
4,520 | 416 | 416 | {
"id": [
4680
],
"area": [
346
],
"bbox": [
[
184,
216,
10.5,
33
]
],
"category": [
3
]
} |
|
3,286 | 416 | 416 | {
"id": [
3398
],
"area": [
368
],
"bbox": [
[
182,
216,
11,
33.5
]
],
"category": [
3
]
} |
|
727 | 416 | 416 | {
"id": [
753
],
"area": [
422
],
"bbox": [
[
187,
215,
13,
32.5
]
],
"category": [
3
]
} |
End of preview. Expand
in Dataset Viewer.
Dataset Labels
['dropped spike', 'enemy', 'planted spike', 'teammate']
Number of Images
{'valid': 1983, 'train': 6927, 'test': 988}
How to Use
- Install datasets:
pip install datasets
- Load the dataset:
from datasets import load_dataset
ds = load_dataset("keremberke/valorant-object-detection", name="full")
example = ds['train'][0]
Roboflow Dataset Page
https://universe.roboflow.com/daniels-magonis-0pjzx/valorant-9ufcp/dataset/3
Citation
@misc{ valorant-9ufcp_dataset,
title = { valorant Dataset },
type = { Open Source Dataset },
author = { Daniels Magonis },
howpublished = { \\url{ https://universe.roboflow.com/daniels-magonis-0pjzx/valorant-9ufcp } },
url = { https://universe.roboflow.com/daniels-magonis-0pjzx/valorant-9ufcp },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-01-27 },
}
License
CC BY 4.0
Dataset Summary
This dataset was exported via roboflow.com on December 22, 2022 at 5:10 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 9898 images. Planted are annotated in COCO format.
The following pre-processing was applied to each image:
- Resize to 416x416 (Stretch)
No image augmentation techniques were applied.
- Downloads last month
- 44
Size of downloaded dataset files:
251 MB
Size of the auto-converted Parquet files:
251 MB
Number of rows:
9,907
Models trained or fine-tuned on keremberke/valorant-object-detection