objectnet

# ObjectNet

A webp (lossless) encoded version of [ObjectNet-1.0](https://objectnet.dev/index.html) at original resolution.

## License / Usage Terms

ObjectNet is free to use for both research and commercial applications. The authors own the source images and allow their use under a license derived from Creative Commons Attribution 4.0 with only two additional clauses.

1. **ObjectNet may never be used to tune the parameters of any model.**
2. **Any individual images from ObjectNet may only be posted to the web including their 1 pixel red border**.
If you are using ObjectNet, please cite our work, the citation appears at the bottom of this page. Any derivative of ObjectNet must contain attribution as well.

## About

What is ObjectNet?
* A new kind of vision dataset borrowing the idea of controls from other areas of science.
* No training set, only a test set! Put your vision system through its paces.
* Collected to intentionally show objects from new viewpoints on new backgrounds.
* 50,000 image test set, same as ImageNet, with controls for rotation, background, and viewpoint.
* 313 object classes with 113 overlapping ImageNet
* Large performance drop, what you can expect from vision systems in the real world!
* Robust to fine-tuning and a very difficult transfer learning problem

## Why the Red Borders / How do I recognize if an image is in ObjectNet?

As training sets become huge, the risk that test and training sets overlap is serious. We provide ObjectNet with a 2 pixel red border around each image which must be removed before performing inference. The ObjectNet license requires that if you post images from ObjectNet to the web, you include this border. Any time you see an image with a solid 2 pixel red border, that's an indication it's in someone's test set and you should be careful about training on it. Reverse image search will allow you to figure out which test set it is from.

NOTE: original ObjectNet PNG files actually have a 2 pixel red border while their descriptions say 1.

## Preprocessing Steps for This timm Version
1. Re-encode PNG images with lossless WebP (~32% reduction in size), keeping red border.
2. Add `imagenet_labels` and `imagenet_synsets` consisting of lists of ImageNet-1k classes that overlap with ObjectNet class.

## Citation
```bibtex
@incollection{NIPS2019_9142,
title = {ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models},
author = {Barbu, Andrei and Mayo, David and Alverio, Julian and Luo, William and Wang, Christopher and Gutfreund, Dan and Tenenbaum, Josh and Katz, Boris},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {9448--9458},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/9142-objectnet-a-large-scale-bias-controlled-dataset-for-pushing-the-limits-of-object-recognition-models.pdf}
}
```

# ObjectNet
这是 [ObjectNet-1.0](https://objectnet.dev/index.html) 的无损 WebP 编码版本，分辨率保持不变。

## 许可/使用条款
ObjectNet 可免费用于研究和商业应用。作者拥有源图像，并允许在衍生自知识共享署名 4.0 的许可下使用，仅增加两个附加条款。
1. **ObjectNet 绝不能用于调整任何模型的参数。**
2. **ObjectNet 中的任何单张图像发布到网络上时，必须保留其 1 像素宽的红色边框。**

如果您使用 ObjectNet，请引用我们的工作，引用信息见本页底部。ObjectNet 的任何衍生作品也必须包含署名。

## 关于 ObjectNet
什么是 ObjectNet？
* 一种新型视觉数据集，借鉴了其他科学领域的控制思想。
* 没有训练集，只有测试集！让您的视觉系统接受严格考验。
* 收集时特意以新背景、新视角展示物体。
* 与 ImageNet 规模相同的 5 万张图像测试集，包含对旋转、背景和视角的控制。
* 313 个物体类别，其中 113 个与 ImageNet 重叠。
* 性能大幅下降——这正是现实世界中视觉系统可能遇到的情况！
* 对微调具有鲁棒性，是一个非常困难的迁移学习问题。

## 为什么有红色边框 / 如何识别一张图像是否属于 ObjectNet？
随着训练集变得越来越大，测试集与训练集重叠的风险变得严重。我们提供的 ObjectNet 每张图像周围都有一个 2 像素宽的红色边框，在进行推理前必须将其去除。ObjectNet 许可要求：如果您将 ObjectNet 中的图像发布到网络上，必须保留此边框。任何时候看到带有纯色 2 像素红色边框的图像，这表示它属于某个测试集，您应当谨慎避免将其用于训练。通过反向图像搜索可以查出它来自哪个测试集。

注意：原始的 ObjectNet PNG 文件实际有 2 像素红色边框，而描述中写的是 1 像素。

## 本 timm 版本的预处理步骤
1. 将 PNG 图像用无损 WebP 重新编码（大小减少约 32%），保留红色边框。
2. 添加 `imagenet_labels` 和 `imagenet_synsets`，包含与 ObjectNet 类别重叠的 ImageNet-1k 类别列表。

## 引用
```bibtex
@incollection{NIPS2019_9142,
title = {ObjectNet：一个用于推动物体识别模型极限的大规模偏差控制数据集},
author = {Barbu, Andrei and Mayo, David and Alverio, Julian and Luo, William and Wang, Christopher and Gutfreund, Dan and Tenenbaum, Josh and Katz, Boris},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {9448--9458},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/9142-objectnet-a-large-scale-bias-controlled-dataset-for-pushing-the-limits-of-object-recognition-models.pdf}
}

搜集汇总

背景与挑战

背景概述

ObjectNet是一个专门用于测试对象识别模型性能的数据集，仅包含50,000张图像的测试集，无训练集。它通过控制旋转、背景和视角来模拟真实世界场景，包含313个对象类别，其中113个与ImageNet重叠，旨在评估模型在复杂条件下的鲁棒性。数据集图像带有2像素红色边框以标识其测试集属性，使用Apache License 2.0许可证，但禁止用于模型参数调优。