---
license: cc-by-4.0
task_categories:
- object-detection
tags: [coco, object-detection, benchmark, edge, jetson]
pretty_name: COCO val2017 mini-500
size_categories:
- n<1K
---

# COCO val2017 mini-500

A **frozen, reproducible 500-image subset** of COCO `val2017` for fast object-detection
**latency/throughput benchmarking** on edge devices (e.g. LibreYOLO on NVIDIA Jetson), where the
full 5000-image val set is impractical.

## Selection (deterministic)
The 500 images are `sorted(COCO.getImgIds())[:500]` of the official `instances_val2017.json`
(the 500 lowest image IDs) — identical to running the
[vision-analysis-benchmark](https://github.com/LibreYOLO/vision-analysis-benchmark) harness with `--limit 500`.

| field | value |
|---|---|
| images | 500 |
| annotations | 3541 |
| image_ids SHA-256 | `74bea76eee276928bb1fb2f3b30e6a6e2760124a1de4931727b50080ccf81951` |
| subset annotations SHA-256 | `31c8e48b3e0bc957da945ad43697f8d519d7aaeb5971cef19e4cfaa4a8423a56` |
| source instances_val2017.json SHA-256 | `e8c7f7908f1d7278341fae127d0da654f102f11bd7b21d8aeefa635b8c810b6f` |

## Structure
```
annotations/instances_val2017_mini500.json   # COCO format, filtered to the 500 images
images/val2017/*.jpg                          # the 500 images
manifest.json                                 # image_ids + file_names + SHA-256 fingerprints
```

## Usage
```python
from pycocotools.coco import COCO
coco = COCO("annotations/instances_val2017_mini500.json")
print(len(coco.getImgIds()))  # 500
```

## Provenance, license & attribution
Derived from **COCO 2017** (Lin et al., *Microsoft COCO: Common Objects in Context*, ECCV 2014) — https://cocodataset.org.
**Annotations** are CC BY 4.0 (COCO Consortium). **Images** are from Flickr and retain their original licenses
as listed in the COCO `licenses` field (preserved in the annotations file); re-hosted unmodified only to make this
benchmark subset turnkey. No new annotations are added — this is a deterministic repackaged slice of the official release.