--- license: cc-by-4.0 size_categories: - n<1K task_categories: - object-detection - image-segmentation language: - en pretty_name: COCO Detection --- # Dataset Card for "COCO Detection" ## Quick Start ### Usage ```python >>> from datasets.load import load_dataset >>> dataset = load_dataset('whyen-wang/coco_detection') >>> example = dataset['train'][500] >>> print(example) {'image': , 'bboxes': [ [192.4199981689453, 220.17999267578125, 129.22999572753906, 148.3800048828125], [76.94000244140625, 146.6300048828125, 104.55000305175781, 109.33000183105469], [302.8800048828125, 115.2699966430664, 99.11000061035156, 119.2699966430664], [0.0, 0.800000011920929, 592.5700073242188, 420.25]], 'categories': [46, 46, 46, 55], 'inst.rles': { 'size': [[426, 640], [426, 640], [426, 640], [426, 640]], 'counts': [ 'gU`2b0d;...', 'RXP16m<=...', ']Xn34S=4...', 'n:U2o8W2...' ]}} ``` ### Visualization ```python >>> import cv2 >>> import numpy as np >>> from PIL import Image >>> def transforms(examples): inst_rles = examples.pop('inst.rles') annotation = [] for i in inst_rles: inst_rles = [ {'size': size, 'counts': counts} for size, counts in zip(i['size'], i['counts']) ] annotation.append(maskUtils.decode(inst_rles)) examples['annotation'] = annotation return examples >>> def visualize(example, names, colors): image = np.array(example['image']) bboxes = np.array(example['bboxes']).round().astype(int) bboxes[:, 2:] += bboxes[:, :2] categories = example['categories'] masks = example['annotation'] n = len(bboxes) for i in range(n): c = categories[i] color, name = colors[c], names[c] cv2.rectangle(image, bboxes[i, :2], bboxes[i, 2:], color.tolist(), 2) cv2.putText( image, name, bboxes[i, :2], cv2.FONT_HERSHEY_SIMPLEX, 1, color.tolist(), 2, cv2.LINE_AA, False ) image[masks[..., i] == 1] = image[masks[..., i] == 1] // 2 + color // 2 return image >>> dataset.set_transform(transforms) >>> names = dataset['train'].features['categories'].feature.names >>> colors = np.ones((80, 3), np.uint8) * 255 >>> colors[:, 0] = np.linspace(0, 255, 80) >>> colors = cv2.cvtColor(colors[None], cv2.COLOR_HSV2RGB)[0] >>> example = dataset['train'][500] >>> Image.fromarray(visualize(example, names, colors)) ``` ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://cocodataset.org/ - **Repository:** None - **Paper:** [Microsoft COCO: Common Objects in Context](https://arxiv.org/abs/1405.0312) - **Leaderboard:** [Papers with Code](https://paperswithcode.com/dataset/coco) - **Point of Contact:** None ### Dataset Summary COCO is a large-scale object detection, segmentation, and captioning dataset. ### Supported Tasks and Leaderboards [Object Detection](https://huggingface.co/tasks/object-detection) [Image Segmentation](https://huggingface.co/tasks/image-segmentation) ### Languages en ## Dataset Structure ### Data Instances An example looks as follows. ``` { "image": PIL.Image(mode="RGB"), "bboxes": [ [192.4199981689453, 220.17999267578125, 129.22999572753906, 148.3800048828125], [76.94000244140625, 146.6300048828125, 104.55000305175781, 109.33000183105469], [302.8800048828125, 115.2699966430664, 99.11000061035156, 119.2699966430664], [0.0, 0.800000011920929, 592.5700073242188, 420.25]], "categories": [46, 46, 46, 55], "inst.rles": { "size": [[426, 640], [426, 640], [426, 640], [426, 640]], "counts": ["gU`2b0d;...", "RXP16m<=...", "]Xn34S=4...", "n:U2o8W2..."] } } ``` ### Data Fields [More Information Needed] ### Data Splits | name | train | validation | | ------- | ------: | ---------: | | default | 118,287 | 5,000 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Creative Commons Attribution 4.0 License ### Citation Information ``` @article{cocodataset, author = {Tsung{-}Yi Lin and Michael Maire and Serge J. Belongie and Lubomir D. Bourdev and Ross B. Girshick and James Hays and Pietro Perona and Deva Ramanan and Piotr Doll{'{a} }r and C. Lawrence Zitnick}, title = {Microsoft {COCO:} Common Objects in Context}, journal = {CoRR}, volume = {abs/1405.0312}, year = {2014}, url = {http://arxiv.org/abs/1405.0312}, archivePrefix = {arXiv}, eprint = {1405.0312}, timestamp = {Mon, 13 Aug 2018 16:48:13 +0200}, biburl = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@github-whyen-wang](https://github.com/whyen-wang) for adding this dataset.