SA-FARI

# SA-FARI Dataset
**License** CC-BY-NC 4.0

**SA-FARI** is a wildlife camera dataset collected through a collaboration between Meta and [CXL](https://www.conservationxlabs.com/).

All videos and pre-processed JPEGImages can be found in [cxl-public-camera-trap](https://console.cloud.google.com/storage/browser/cxl-public-camera-trap), which contains the following contents:
```
sa_fari/
├── sa_fari_test_tars/
│ ├── JPEGImages_6fps/
│ ├── videos/
├── sa_fari_test/
│ ├── JPEGImages_6fps/
│ ├── videos/
├── sa_fari_train_tars/
│ ├── JPEGImages_6fps/
│ ├── videos/
└── sa_fari_train/
├── JPEGImages_6fps/
└── videos/
```
* `videos`: The original full fps videos.
* `JPEGImages_6fps`: For annotation, the videos have been downsampled to 6fps. This folder contains the downsampled frames compatible with the annotation json files below.

This Hugging Face dataset repo contains the annotations:
```
datasets/facebook/SA-FARI/tree/main/
└── annotation/
├── sa_fari_test.json
├── sa_fari_test_ext.json
├── sa_fari_train.json
└── sa_fari_train_ext.json
```
* `sa_fari_test.json` and `sa_fari_train.json`
* Follow the same format as [SA-Co/VEval](https://huggingface.co/datasets/facebook/SACo-VEval/)
* `sa_fari_test_ext.json` and `sa_fari_train_ext.json`
* In additional to the [SA-Co/VEval] format, we added additional metadata to the following fields:
* `videos`:
* `video_num_frames`, `video_fps`, `video_creation_datetime` and `location_id` have been added as additional metadata to the `videos` field.
* `categories`:
* `Kingdom`, `Phylum`, `Class`, `Order`, `Family`, `Genus` and `Species` have been added when applicable as additional metadata to the `categories` field.

All the SA-FARI annotation files are compatible to use the visualization notebook and offline evaluator developed in [SAM 3 Github](https://github.com/facebookresearch/sam3/tree/main/scripts/eval/veval).

## Annotation Format
A format breakdown for `sa_fari_test.json` and `sa_fari_train.json`. The format is similar to the [YTVIS](https://youtube-vos.org/dataset/vis/) format.

In the annotation json, e.g. `sa_fari_test.json` there are 5 fields:
* info:
* A dict containing the dataset info
* E.g. {'version': 'v1', 'date': '2025-09-24', 'description': 'SA-FARI Test'}
* videos
* A list of videos that are used in the current annotation json
* It contains {id, video_name, file_names, height, width, length}
* annotations
* A list of **positive** masklets and their related info
* It contains {id, segmentations, bboxes, areas, iscrowd, video_id, height, width, category_id, noun_phrase}
* video_id should match to the `videos - id` field above
* category_id should match to the `categories - id` field below
* segmentations is a list of [RLE](https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/mask.py)
* categories
* A **globally** used noun phrase id map, which is true across all 3 domains.
* It contains {id, name}
* name is the noun phrase
* video_np_pairs
* A list of video-np pairs, including both **positive** and **negative** used in the current annotation json
* It contains {id, video_id, category_id, noun_phrase, num_masklets}
* video_id should match the `videos - id` above
* category_id should match the `categories - id` above
* when `num_masklets > 0` it is a positive video-np pair, and the presenting masklets can be found in the annotations field
* when `num_masklets = 0` it is a negative video-np pair, meaning no masklet presenting at all
```
data {
"info": info
"videos": [video]
"annotations": [annotation]
"categories": [category]
"video_np_pairs": [video_np_pair]
}
video {
"id": int
"video_name": str # e.g. sav_000000
"file_names": List[str]
"height": int
"width": width
"length": length
}
annotation {
"id": int
"segmentations": List[RLE]
"bboxes": List[List[int, int, int, int]]
"areas": List[int]
"iscrowd": int
"video_id": str
"height": int
"width": int
"category_id": int
"noun_phrase": str
}
category {
"id": int
"name": str
}
video_np_pair {
"id": int
"video_id": str
"category_id": int
"noun_phrase": str
"num_masklets" int
}
```

# SA-FARI数据集
**授权协议** CC-BY-NC 4.0

**SA-FARI**是由Meta与保护X实验室（Conservation X Labs，简称CXL）合作采集的野生动物相机数据集。

所有视频与预处理后的JPEG图像均可在[cxl-public-camera-trap](https://console.cloud.google.com/storage/browser/cxl-public-camera-trap)获取，该存储桶包含以下内容：

sa_fari/
├── sa_fari_test_tars/
│ ├── JPEGImages_6fps/
│ ├── videos/
├── sa_fari_test/
│ ├── JPEGImages_6fps/
│ ├── videos/
├── sa_fari_train_tars/
│ ├── JPEGImages_6fps/
│ ├── videos/
└── sa_fari_train/
├── JPEGImages_6fps/
└── videos/

* `videos`：原始全帧率视频。
* `JPEGImages_6fps`：为便于标注，已将视频下采样至6帧每秒，该文件夹包含与下方标注json文件兼容的下采样帧。

本Hugging Face数据集仓库包含标注文件：

datasets/facebook/SA-FARI/tree/main/
└── annotation/
├── sa_fari_test.json
├── sa_fari_test_ext.json
├── sa_fari_train.json
└── sa_fari_train_ext.json

* `sa_fari_test.json` 与 `sa_fari_train.json`：格式与[SA-Co/VEval](https://huggingface.co/datasets/facebook/SACo-VEval/)完全一致。
* `sa_fari_test_ext.json` 与 `sa_fari_train_ext.json`：除遵循SA-Co/VEval格式外，还为以下字段添加了额外元数据：
* `videos`字段：新增`video_num_frames`、`video_fps`、`video_creation_datetime`及`location_id`作为补充元数据。
* `categories`字段：视应用场景为`categories`字段新增`界（Kingdom）`、`门（Phylum）`、`纲（Class）`、`目（Order）`、`科（Family）`、`属（Genus）`及`种（Species）`作为补充元数据。

所有SA-FARI标注文件均可兼容使用[SAM 3 GitHub](https://github.com/facebookresearch/sam3/tree/main/scripts/eval/veval)中开发的可视化脚本与离线评估工具。

## 标注格式
以下为`sa_fari_test.json`与`sa_fari_train.json`的格式拆解说明，其格式与[YTVIS](https://youtube-vos.org/dataset/vis/)格式类似。

在标注json文件（例如`sa_fari_test.json`）中包含5个核心字段：
* info：
一个包含数据集基础信息的字典，示例为`{"version": "v1", "date": "2025-09-24", "description": "SA-FARI Test"}`。
* videos：
当前标注json中使用的视频列表，每个元素包含字段`id`、`video_name`、`file_names`、`height`、`width`、`length`。
* annotations：
正样本掩码块（masklets）及其关联信息的列表，每个元素包含字段`id`、`segmentations`、`bboxes`、`areas`、`iscrowd`、`video_id`、`height`、`width`、`category_id`、`noun_phrase`。其中：
* `video_id`需与上述`videos`字段中的`id`一一匹配
* `category_id`需与上述`categories`字段中的`id`一一匹配
* `segmentations`为[游程编码（Run-Length Encoding，简称RLE）](https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/mask.py)格式的列表
* categories：
全局通用的名词短语ID映射表，适用于全部三类应用场景，每个元素包含字段`id`、`name`，其中`name`为对应的名词短语。
* video_np_pairs：
当前标注json中使用的视频-名词短语对列表，涵盖正样本与负样本对，每个元素包含字段`id`、`video_id`、`category_id`、`noun_phrase`、`num_masklets`。其中：
* `video_id`需与上述`videos`字段中的`id`匹配
* `category_id`需与上述`categories`字段中的`id`匹配
* 当`num_masklets > 0`时为正样本视频-名词短语对，其包含的有效掩码块可在`annotations`字段中查询
* 当`num_masklets = 0`时为负样本视频-名词短语对，代表该样本中无任何掩码块存在

data {
"info": info
"videos": [video]
"annotations": [annotation]
"categories": [category]
"video_np_pairs": [video_np_pair]
}
video {
"id": 整数类型
"video_name": 字符串类型 # 例如 sav_000000
"file_names": 字符串列表
"height": 整数类型
"width": 整数类型
"length": 整数类型
}
annotation {
"id": 整数类型
"segmentations": 游程编码列表
"bboxes": 整数四元组列表
"areas": 整数列表
"iscrowd": 整数类型
"video_id": 字符串类型
"height": 整数类型
"width": 整数类型
"category_id": 整数类型
"noun_phrase": 字符串类型
}
category {
"id": 整数类型
"name": 字符串类型
}
video_np_pair {
"id": 整数类型
"video_id": 字符串类型
"category_id": 整数类型
"noun_phrase": 字符串类型
"num_masklets": 整数类型
}

搜集汇总

背景与挑战

背景概述

SA-FARI是一个野生动物相机捕捉数据集，包含原始视频和降采样至6fps的JPEG图像，标注文件遵循特定格式并包含丰富的元数据信息。