Francesco/trail-camera

---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': game
'1': Deer
'2': Hog
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: trail-camera
tags:
- rf100
---

# Dataset Card for trail-camera

** The original COCO dataset is stored at `dataset.tar.gz`**

## Dataset Description

- **Homepage:** https://universe.roboflow.com/object-detection/trail-camera
- **Point of Contact:** [e-mail]

### Dataset Summary

trail-camera

### Supported Tasks and Leaderboards

- `object-detection`: The dataset can be used to train a model for Object Detection.

### Languages

English

## Dataset Structure

### Data Instances

A data point comprises an image and its object annotations.

```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```

### Data Fields

- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.

#### Who are the annotators?

Annotators are Roboflow users

## Additional Information

### Licensing Information

See original homepage https://universe.roboflow.com/object-detection/trail-camera

### Citation Information

```
@misc{ trail-camera,
title = { trail camera Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/trail-camera } },
url = { https://universe.roboflow.com/object-detection/trail-camera },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```

### Contributions

Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.

原始信息汇总

数据集概述

数据集名称

• 名称: trail-camera

数据集特征

• 特征列表:
  • image_id: 数据类型为 int64
  • image: 数据类型为 image
  • width: 数据类型为 int32
  • height: 数据类型为 int32
  • objects: 包含以下子特征
    • id: 数据类型为 int64
    • area: 数据类型为 int64
    • bbox: 数据类型为 float32，长度为4
    • category: 类别标签，包含以下名称
      • 0: game
      • 1: Deer
      • 2: Hog

数据集详情

• 任务类别: object-detection
• 语言: English
• 多语言性: monolingual
• 大小类别: 1K<n<10K
• 源数据集: original

数据集结构

• 数据实例: 每个数据点包含一张图片及其对象注释。
• 数据字段:
  • image_id: 图片ID
  • image: 图片对象
  • width: 图片宽度
  • height: 图片高度
  • objects: 包含对象的元数据
    • id: 注释ID
    • area: 边界框的面积
    • bbox: 对象的边界框
    • category: 对象的类别

注释者信息

• 注释者: Roboflow用户

许可证信息

• 许可证: cc

引用信息

@misc{ trail-camera, title = { trail camera Dataset }, type = { Open Source Dataset }, author = { Roboflow 100 }, howpublished = { url{ https://universe.roboflow.com/object-detection/trail-camera } }, url = { https://universe.roboflow.com/object-detection/trail-camera }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { nov }, note = { visited on 2023-03-29 }, }

搜集汇总

背景与挑战

背景概述

该数据集是一个用于对象检测的trail-camera图像数据集，包含约1.31k张图像，分为训练、验证和测试集，每张图像均带有边界框和类别注释。数据集规模适中，格式为parquet，适用于训练计算机视觉模型，特别针对野外监控场景。