Hugging Face's logo

Datasets:
blackbeedrones
/
bb-challenge-boxes

Tasks:
Object Detection
Modalities:
Image
Formats:
parquet
Size:
1K - 10K
Tags:
object-detection
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
bb-challenge-boxes
File size: 1,700 Bytes
550bf6b
2e44833
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
550bf6b
 
2e44833
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
550bf6b
2e44833
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
---
license: apache-2.0
task_categories:
  - object-detection
tags:
  - object-detection
size_categories:
  - 1K<n<10K
pretty_name: Bb Challenge Boxes
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: image
      dtype: image
    - name: image_id
      dtype: int64
    - name: width
      dtype: int32
    - name: height
      dtype: int32
    - name: objects
      struct:
        - name: id
          sequence: int64
        - name: bbox
          sequence:
            sequence: float32
            length: 4
        - name: category
          sequence:
            class_label:
              names:
                '0': box
        - name: area
          sequence: float64
---

# Bb Challenge Boxes

Object detection dataset hosted at `blackbeedrones/bb-challenge-boxes`.

## Dataset Structure

| Split | Images |
|-------|--------|
| train | 1565 |
| validation | 149 |
| test | 75 |

**Total images:** 1789

**Classes:** `box`

**Annotation format:** COCO bbox `[x_min, y_min, width, height]`.

## Usage

### Load with HuggingFace Datasets

```python
from datasets import load_dataset

dataset = load_dataset("blackbeedrones/bb-challenge-boxes")
example = dataset["train"][0]
print(example["objects"])
```

### Use with Nectar SDK

```python
from nectar.ai.detection.datasets import HuggingFaceHandler

handler = HuggingFaceHandler("data/local")
handler.download(repo_id="blackbeedrones/bb-challenge-boxes", format_type="coco")
# data/local now contains train/_annotations.coco.json and image files
```