Update README.md

README.md

@@ -19,3 +19,124 @@ task_ids:
 - multi-class-image-classification
 ---
 
+
+
+## General Information
+
+**Title**: ImageNet-AB
+
+**Description**: ImageNet-AB is an extended version of the ImageNet-1K training set, enriched with annotation byproducts (AB).
+In addition to the image and corresponding class labels, this dataset provides a rich history of interactions per input signal per front-end component during the annotation process.
+They include mouse traces, click locations, annotation times, as well as anonymised worker IDs.
+
+**Links**:
+
+- [ICCV'23 Paper](https://arxiv.org/abs/2303.17595)
+- [Main Repository](https://github.com/naver-ai/NeglectedFreeLunch)
+- [ImageNet Annotation Interface](https://github.com/naver-ai/imagenet-annotation-tool)
+
+
+## Collection Process
+
+**Collection Details**:
+The additional annotations for the ImageNet-AB dataset were collected using Amazon Mechanical Turk (MTurk) workers from the US region, due to the task being described in English. 
+The task was designed as a human intelligence task (HIT), and the qualification approval rate was set at 90% to ensure the task's quality.
+Each HIT contained 10 pages of annotation tasks, each page having 48 candidate images.
+We follow the original annotation interface of ImageNet as much as possible.
+See [GitHub repository](https://github.com/naver-ai/imagenet-annotation-tool) and [Paper](https://arxiv.org/abs/2303.17595) for further information.
+
+Annotators interact with different components in the annotation interface, using input devices. 
+This interaction results in time-series data for mouse movements (mouseTracking) and mouse clicks (selectedRecord) for every image.
+The dataset also records whether the image was ultimately selected by the annotator in the 'selected' field.
+
+**Annotator Compensation**:
+Annotators were paid 1.5 USD per HIT. 
+The median time taken to complete each HIT was 9.0 minutes, yielding an approximate hourly wage of 10.0 USD. 
+This wage is above the US federal minimum hourly wage. 
+A total of 20,304 USD was paid to the MTurk annotators, with an additional 20% fee paid to Amazon.
+
+**Annotation Rejection**: 
+We rejected a HIT under the following circumstances.
+
+- The recall rate was lower than 0.333.
+- The total number of selections among 480 candidates was lower than 30.
+- The annotator did not complete at least 9 out of the 10 pages of tasks.
+- The annotation was not found in our database, and the secret hash code for confirming their completion was incorrect.
+- In total, 1,145 out of 14,681 completed HITs (7.8%) were rejected.
+
+**Collection Time**:
+The entire annotation collection process took place between December 18, 2021, and December 31, 2021.
+
+## Data Schema
+
+```json
+{
+  "imageID": "n01440764/n01440764_105",
+  "originalImageHeight": 375,
+  "originalImageWidth": 500,
+  "selected": true,
+  "imageHeight": 243,
+  "imageWidth": 243,
+  "imagePosition": {"x": 857, "y": 1976},
+  "hoveredRecord": [
+    {"action": "enter", "time": 1641425051},
+    {"action": "leave", "time": 1641425319}
+  ],
+  "selectedRecord": [
+    {"x": 0.540, "y": 0.473, "time": 1641425052}
+  ],
+  "mouseTracking": [
+    {"x": 0.003, "y": 0.629, "time": 1641425051},
+    {"x": 0.441, "y": 0.600, "time": 1641425052}
+  ],
+  "worker_id": "47DBDD543E",
+  "assignment_id": "3AMYWKA6YLE80HK9QYYHI2YEL2YO6L",
+  "page_idx": 3
+}
+```
+
+## Usage
+
+One could use the annotation byproducts to improve the model generalisability and robustness.
+This is appealing, as the annotation byproducts do not incur extra annotation costs for the annotators.
+For more information, refer to our [ICCV'23 Paper](https://arxiv.org/abs/2303.17595).
+
+## Dataset Statistics
+There were two annotation rounds covering 1,281,167 ImageNet1K training images. 
+In the first round, annotators re-selected 71.8% of these images. 
+The remaining 28.2% were re-packaged into a second batch of HITs, from which an additional 14.9% were selected. 
+In total, 1,110,786 (86.7%) of ImageNet1K training images were re-selected, with annotation byproducts available for 1,272,225 (99.3%) of the images.
+
+Other dataset statistics are inherited from the parent dataset, ImageNet-1K. 
+
+
+## Ethics and Legalities
+The crowdsourced annotators were fairly compensated for their time at a rate well above the U.S. federal minimum wage. 
+In terms of data privacy, the dataset maintains the same ethical standards as the original ImageNet-1K dataset.
+Worker identifiers were anonymized using a non-reversible hashing function, ensuring privacy.
+
+Our data collection has obtained IRB approval from an author’s institute. 
+For the future collection of annotation byproducts, we note that there exist potential risks that annotation byproducts may contain annotators’ privacy. 
+Data collectors may even attempt to leverage more private information as byproducts. 
+We urge data collectors not to collect or exploit private information from annotators. 
+Whenever appropriate, one must ask for the annotators’ consent.
+
+## Citation Information
+Detailed citation information is to be provided.
+
+```
+@inproceedings{han2023iccv,
+  title = {Neglected Free Lunch – Learning Image Classifiers Using Annotation Byproducts},
+  author = {Han, Dongyoon and Choe, Junsuk and Chun, Seonghyeok and Chung, John Joon Young and Chang, Minsuk and Yun, Sangdoo and Song, Jean Y. and Oh, Seong Joon},
+  booktitle = {International Conference on Computer Vision (ICCV)},
+  year = {2023}
+}
+```
+
+## Maintenance and Updates
+This section will be updated as and when there are changes or updates to the dataset.
+
+## Known Limitations
+We have not been able to acquire annotation byproducts for all original ImageNet-1K dataset samples.
+This is because not all ImageNet-1K samples are re-selected by the annotators, potentially because of the errors in the original ImageNet-1K dataset.
+Given the budget constraint, we have not been able to acquire 10+ annotations per sample, as done in the original work.