--- annotations_creators: - crowdsourced license: cc-by-nc-sa-4.0 size_categories: - 10K<n<100K task_categories: - image-classification - image-feature-extraction pretty_name: Galaxy Zoo CANDELS arxiv: 2404.02973 tags: - galaxy zoo - physics - astronomy - galaxies - citizen science configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: id_str dtype: string - name: ra dtype: float64 - name: dec dtype: float64 - name: smooth-or-featured-candels_smooth dtype: int32 - name: smooth-or-featured-candels_features dtype: int32 - name: smooth-or-featured-candels_artifact dtype: int32 - name: how-rounded-candels_completely dtype: int32 - name: how-rounded-candels_in-between dtype: int32 - name: how-rounded-candels_cigar-shaped dtype: int32 - name: clumpy-appearance-candels_yes dtype: int32 - name: clumpy-appearance-candels_no dtype: int32 - name: clump-count-candels_1 dtype: int32 - name: clump-count-candels_2 dtype: int32 - name: clump-count-candels_3 dtype: int32 - name: clump-count-candels_4 dtype: int32 - name: clump-count-candels_5-plus dtype: int32 - name: clump-count-candels_cant-tell dtype: int32 - name: disk-edge-on-candels_yes dtype: int32 - name: disk-edge-on-candels_no dtype: int32 - name: edge-on-bulge-candels_yes dtype: int32 - name: edge-on-bulge-candels_no dtype: int32 - name: bar-candels_yes dtype: int32 - name: bar-candels_no dtype: int32 - name: has-spiral-arms-candels_yes dtype: int32 - name: has-spiral-arms-candels_no dtype: int32 - name: spiral-winding-candels_tight dtype: int32 - name: spiral-winding-candels_medium dtype: int32 - name: spiral-winding-candels_loose dtype: int32 - name: spiral-arm-count-candels_1 dtype: int32 - name: spiral-arm-count-candels_2 dtype: int32 - name: spiral-arm-count-candels_3 dtype: int32 - name: spiral-arm-count-candels_4 dtype: int32 - name: spiral-arm-count-candels_5-plus dtype: int32 - name: spiral-arm-count-candels_cant-tell dtype: int32 - name: bulge-size-candels_none dtype: int32 - name: bulge-size-candels_obvious dtype: int32 - name: bulge-size-candels_dominant dtype: int32 - name: merging-candels_merger dtype: int32 - name: merging-candels_tidal-debris dtype: int32 - name: merging-candels_both dtype: int32 - name: merging-candels_neither dtype: int32 splits: - name: train num_bytes: 5047445820.354 num_examples: 38478 download_size: 5012044096 dataset_size: 5047445820.354 --- # GZ Campaign Datasets ## Dataset Summary [Galaxy Zoo](www.galaxyzoo.org) volunteers label telescope images of galaxies according to their visible features: spiral arms, galaxy-galaxy collisions, and so on. These datasets share the galaxy images and volunteer labels in a machine-learning-friendly format. We use these datasets to train [our foundation models](https://arxiv.org/abs/2404.02973). We hope they'll help you too. - **Curated by:** [Mike Walmsley](https://walmsley.dev/) - **License:** [cc-by-nc-sa-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en). We specifically require **all models trained on these datasets to be released as source code by publication**. ## Downloading Install the Datasets library pip install datasets and then log in to your HuggingFace account huggingface-cli login All unpublished* datasets are temporarily "gated" i.e. you must have requested and been approved for access. Galaxy Zoo team members should go to https://huggingface.co/mwalmsley/datasets/gz_candels, click "request access", ping Mike, then wait for approval. Gating will be removed on publication. *Currently: the `gz_h2o` and `gz_ukidss` datasets ## Usage ```python from datasets import load_dataset # . split='train' picks which split to load dataset = load_dataset( 'mwalmsley/gz_candels', # each dataset has a random fixed train/test split split='train' # some datasets also allow name=subset (e.g. name="tiny" for gz_evo). see the viewer for subset options ) dataset.set_format('torch') # your framework of choice e.g. numpy, tensorflow, jax, etc print(dataset_name, dataset[0]['image'].shape) ``` Then use the `dataset` object as with any other HuggingFace dataset, e.g., ```python from torch.utils.data import DataLoader dataloader = DataLoader(ds, batch_size=4, num_workers=1) for batch in dataloader: print(batch.keys()) # the image key, plus a key counting the volunteer votes for each answer # (e.g. smooth-or-featured-gz2_smooth) print(batch['image'].shape) break ``` You may find these HuggingFace docs useful: - [PyTorch loading options](https://huggingface.co/docs/datasets/en/use_with_pytorch#data-loading). - [Applying transforms/augmentations](https://huggingface.co/docs/datasets/en/image_process#apply-transforms). - [Frameworks supported](https://huggingface.co/docs/datasets/v2.19.0/en/package_reference/main_classes#datasets.Dataset.set_format) by `set_format`. ## Dataset Structure Each dataset is structured like: ```json { 'image': ..., # image of a galaxy 'smooth-or-featured-[campaign]_smooth': 4, 'smooth-or-featured-[campaign]_featured-or-disk': 12, ... # and so on for many questions and answers } ``` Images are loaded according to your `set_format` choice above. For example, ```set_format("torch")``` gives a (3, 424, 424) CHW `Torch.Tensor`. The other keys are formatted like `[question]_[answer]`, where `question` is what the volunteers were asked (e.g. "smooth or featured?" and `answer` is the choice selected (e.g. "smooth"). **The values are the count of volunteers who selected each answer.** `question` is appended with a string noting in which Galaxy Zoo campaign this question was asked e.g. `smooth-or-featured-gz2`. For most datasets, all questions were asked during the same campaign. For GZ DESI, there are three campaigns (`dr12`, `dr5`, and `dr8`) with very similar questions. GZ Evo combines all the published datasets (currently GZ2, GZ DESI, GZ CANDELS, GZ Hubble, and GZ UKIDSS) into a single dataset aimed at multi-task learning. This is helpful for [building models that adapt to new tasks and new telescopes]((https://arxiv.org/abs/2404.02973)). (we will shortly add keys for the astronomical identifiers i.e. the sky coordinates and telescope source unique ids) ## Key Limitations Because the volunteers are answering a decision tree, the questions asked depend on the previous answers, and so each galaxy and each question can have very different total numbers of votes. This interferes with typical metrics that use aggregated labels (e.g. classification of the most voted, regression on the mean vote fraction, etc.) because we have different levels of confidence in the aggregated labels for each galaxy. We suggest a custom loss to handle this. Please see the Datasets and Benchmarks paper for more details (under review, sorry). All labels are imperfect. The vote counts may not always reflect the true appearance of each galaxy. Additionally, the true appearance of each galaxy may be uncertain - even to expert astronomers. We therefore caution against over-interpreting small changes in performance to indicate a method is "superior". **These datasets should not be used as a precise performance benchmark.** ## Citation Information The machine-learning friendly versions of each dataset are described in a recently-submitted paper. Citation information will be added if accepted. For each specific dataset you use, please also cite the original Galaxy Zoo data release paper (listed below) and the telescope description paper (cited therein). ### Galaxy Zoo 2 @article{10.1093/mnras/stt1458, author = {Willett, Kyle W. and Lintott, Chris J. and Bamford, Steven P. and Masters, Karen L. and Simmons, Brooke D. and Casteels, Kevin R. V. and Edmondson, Edward M. and Fortson, Lucy F. and Kaviraj, Sugata and Keel, William C. and Melvin, Thomas and Nichol, Robert C. and Raddick, M. Jordan and Schawinski, Kevin and Simpson, Robert J. and Skibba, Ramin A. and Smith, Arfon M. and Thomas, Daniel}, title = "{Galaxy Zoo 2: detailed morphological classifications for 304 122 galaxies from the Sloan Digital Sky Survey}", journal = {Monthly Notices of the Royal Astronomical Society}, volume = {435}, number = {4}, pages = {2835-2860}, year = {2013}, month = {09}, issn = {0035-8711}, doi = {10.1093/mnras/stt1458}, } ### Galaxy Zoo Hubble @article{2017MNRAS.464.4176W, author = {Willett, Kyle W. and Galloway, Melanie A. and Bamford, Steven P. and Lintott, Chris J. and Masters, Karen L. and Scarlata, Claudia and Simmons, B.~D. and Beck, Melanie and {Cardamone}, Carolin N. and Cheung, Edmond and Edmondson, Edward M. and Fortson, Lucy F. and Griffith, Roger L. and H{\"a}u{\ss}ler, Boris and Han, Anna and Hart, Ross and Melvin, Thomas and Parrish, Michael and Schawinski, Kevin and Smethurst, R.~J. and {Smith}, Arfon M.}, title = "{Galaxy Zoo: morphological classifications for 120 000 galaxies in HST legacy imaging}", journal = {Monthly Notices of the Royal Astronomical Society}, year = 2017, month = feb, volume = {464}, number = {4}, pages = {4176-4203}, doi = {10.1093/mnras/stw2568} } ### Galaxy Zoo CANDELS @article{10.1093/mnras/stw2587, author = {Simmons, B. D. and Lintott, Chris and Willett, Kyle W. and Masters, Karen L. and Kartaltepe, Jeyhan S. and Häußler, Boris and Kaviraj, Sugata and Krawczyk, Coleman and Kruk, S. J. and McIntosh, Daniel H. and Smethurst, R. J. and Nichol, Robert C. and Scarlata, Claudia and Schawinski, Kevin and Conselice, Christopher J. and Almaini, Omar and Ferguson, Henry C. and Fortson, Lucy and Hartley, William and Kocevski, Dale and Koekemoer, Anton M. and Mortlock, Alice and Newman, Jeffrey A. and Bamford, Steven P. and Grogin, N. A. and Lucas, Ray A. and Hathi, Nimish P. and McGrath, Elizabeth and Peth, Michael and Pforr, Janine and Rizer, Zachary and Wuyts, Stijn and Barro, Guillermo and Bell, Eric F. and Castellano, Marco and Dahlen, Tomas and Dekel, Avishai and Ownsworth, Jamie and Faber, Sandra M. and Finkelstein, Steven L. and Fontana, Adriano and Galametz, Audrey and Grützbauch, Ruth and Koo, David and Lotz, Jennifer and Mobasher, Bahram and Mozena, Mark and Salvato, Mara and Wiklind, Tommy}, title = "{Galaxy Zoo: quantitative visual morphological classifications for 48 000 galaxies from CANDELS★}", journal = {Monthly Notices of the Royal Astronomical Society}, volume = {464}, number = {4}, pages = {4420-4447}, year = {2016}, month = {10}, doi = {10.1093/mnras/stw2587} } ### Galaxy Zoo DESI (two citations due to being released over two papers) @article{10.1093/mnras/stab2093, author = {Walmsley, Mike and Lintott, Chris and Géron, Tobias and Kruk, Sandor and Krawczyk, Coleman and Willett, Kyle W and Bamford, Steven and Kelvin, Lee S and Fortson, Lucy and Gal, Yarin and Keel, William and Masters, Karen L and Mehta, Vihang and Simmons, Brooke D and Smethurst, Rebecca and Smith, Lewis and Baeten, Elisabeth M and Macmillan, Christine}, title = "{Galaxy Zoo DECaLS: Detailed visual morphology measurements from volunteers and deep learning for 314 000 galaxies}", journal = {Monthly Notices of the Royal Astronomical Society}, volume = {509}, number = {3}, pages = {3966-3988}, year = {2021}, month = {09}, issn = {0035-8711}, doi = {10.1093/mnras/stab2093} } @article{10.1093/mnras/stad2919, author = {Walmsley, Mike and Géron, Tobias and Kruk, Sandor and Scaife, Anna M M and Lintott, Chris and Masters, Karen L and Dawson, James M and Dickinson, Hugh and Fortson, Lucy and Garland, Izzy L and Mantha, Kameswara and O’Ryan, David and Popp, Jürgen and Simmons, Brooke and Baeten, Elisabeth M and Macmillan, Christine}, title = "{Galaxy Zoo DESI: Detailed morphology measurements for 8.7M galaxies in the DESI Legacy Imaging Surveys}", journal = {Monthly Notices of the Royal Astronomical Society}, volume = {526}, number = {3}, pages = {4768-4786}, year = {2023}, month = {09}, issn = {0035-8711}, doi = {10.1093/mnras/stad2919} } ### Galaxy Zoo UKIDSS Not yet published. ### Galaxy Zoo Cosmic Dawn (a.k.a. H2O) Not yet published.