Update README.md

README.md

@@ -17,8 +17,6 @@ dataset_summary: >
   This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 4838
   samples.
 
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-  
   ## Installation
 
 
@@ -30,8 +28,6 @@ dataset_summary: >
   pip install -U fiftyone
 
   ```
-
-
   ## Usage
 
 
@@ -60,9 +56,6 @@ dataset_summary: >
 
 ![image/png](imagenet-d.gif)
 
-
-
-
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 4838 samples.
 
 ## Installation
@@ -81,141 +74,53 @@ import fiftyone.utils.huggingface as fouh
 
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
-dataset = fouh.load_from_hub("harpreetsahota/ImageNet-D")
+dataset = fouh.load_from_hub("Voxel51/ImageNet-D")
 
 # Launch the App
 session = fo.launch_app(dataset)
 ```
 
-
-## Dataset Details
-
 ### Dataset Description
 
-<!-- Provide a longer summary of what this dataset is. -->
-
-
-
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** [More Information Needed]
-
-### Dataset Sources [optional]
-
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+ImageNet-D is a new benchmark created using diffusion models to generate realistic synthetic images with diverse backgrounds, textures, and materials[1]. The dataset contains 4,835 hard images that cause significant accuracy drops of up to 60% for a range of vision models, including ResNet, ViT, CLIP, LLaVa, and MiniGPT-4[1].
 
-## Uses
+To create ImageNet-D, a large pool of synthetic images is generated by combining object categories with various nuisance attributes using Stable Diffusion[1]. The most challenging images that cause shared failures across multiple surrogate models are selected for the final dataset[1]. Human labelling via Amazon Mechanical Turk is used for quality control to ensure the images are valid and high-quality[1].
 
-<!-- Address questions around how the dataset is intended to be used. -->
+Experiments show that ImageNet-D reveals significant robustness gaps in current vision models[1]. The synthetic images transfer well to unseen models, uncovering common failure modes[1]. ImageNet-D provides a more diverse and challenging test set than prior synthetic benchmarks like ImageNet-C, ImageNet-9, and Stylized ImageNet[1].
 
-### Direct Use
+Citations:
+[1] https://arxiv.org/html/2403.18775v1
 
-<!-- This section describes suitable use cases for the dataset. -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
-
-## Dataset Structure
-
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-
-[More Information Needed]
-
-## Dataset Creation
-
-### Curation Rationale
-
-<!-- Motivation for the creation of this dataset. -->
-
-[More Information Needed]
+- **Funded by :** KAIST, University of Michigan, Ann Arbor, McGill University, MILA
+- **License:** MIT License
 
 ### Source Data
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+See the [original repo](https://github.com/chenshuang-zhang/imagenet_d) for details
 
 #### Data Collection and Processing
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-[More Information Needed]
-
-#### Who are the source data producers?
+The ImageNet-D dataset was constructed using diffusion models to generate a large pool of realistic synthetic images covering various combinations of object categories and nuisance attributes. The key steps in the data collection and generation process were:
 
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+1. **Image generation**: The Stable Diffusion model was used to generate high-fidelity images based on user-defined text prompts specifying the desired object category (C) and nuisance attributes (N) such as background, material, and texture. The image generation is formulated as:
 
-[More Information Needed]
+   Image(C, N) = StableDiffusion(Prompt(C, N)) 
 
-### Annotations [optional]
+   For example, to generate an image of a backpack, the prompt might specify "a backpack in a wheat field" to control both the object category and background nuisance.
 
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+2. **Prompt design**: A set of prompts was carefully designed to cover a matrix of object categories and nuisance attributes (see Table 1 in the paper for an overview). This allows generating images with a much broader range of category-nuisance combinations compared to existing test sets.
 
-#### Annotation process
+3. **Labeling**: Each generated image is automatically labeled with the object category (C) specified in its generation prompt. This category label serves as the ground truth for evaluating classification models on the ImageNet-D dataset. A classification is considered incorrect if the model's predicted class does not match the ground truth category.
 
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-<!-- This section describes the people or systems who created the annotations. -->
-
-[More Information Needed]
-
-#### Personal and Sensitive Information
-
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
+#### Who are the source data producers?
 
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+Chenshuang Zhang, Fei Pan, Junmo Kim, In So Kweon, Chengzhi Mao
 
+## Citation 
 **BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
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-## Dataset Card Authors [optional]
-
-[More Information Needed]
-
-## Dataset Card Contact
-
-[More Information Needed]
+@article{zhang2024imagenet_d,
+  author    = {Zhang, Chenshuang and Pan, Fei and Kim, Junmo and Kweon, In So and Mao, Chengzhi},
+  title     = {ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object},
+  journal   = {CVPR},
+  year      = {2024},
+}