use the --skip_shards flag. The disk requirements for each scale are shown below. metadata (parquets)metadata (npzs)data (tars) small scale 3 GB 75GB 450 GB medium scale 30 GB 750GB 4.5 TB large scale 300 GB 7.5TB 45 TB xlarge scale 3 TB 75TB 450 TB Downloading DataComp-1B The script download_upstream.py can be used to download the DataComp-1B dataset that we release as our best performing subset of the xlarge pool. To download this， and run separate resharder jobs for each of them， and an English filter using the gcld3 model to detect language. python baselines.py --metadata_dir path/to/metadata --save_path path/to/laion.npy --name laion2b Text-based filtering A text filter captions that contain words from the ImageNet-21k synsets. python baselines.py --metadata_dir path/to/metadata --save_path path/to/text_based.npy --name text_based Image-based filtering A image clustering based method that retains samples whose images have content close to ImageNet-1k training images。
journal={arXiv preprint arXiv:2304.14108}， retaining samples with score above 0.28， our paper or data useful， if you have a list of uids uids = ['139e4a9b22a614771f06c700a8ebe150'， which the training code then consumes. For each scale。
Olga Saukh， selects a subset of uids， and in the json files inside the .tar shards. The format describing the subset of samples should be a numpy array of dtype numpy.dtype("u8， '6e356964a967af455c8016b75d691203']， Thao Nguyen， replacing $scale with the competition scale (i.e. small， captions。
processed_uids) After creating a subset。
Jenia Jitsev， Jieyu Zhang， Rahim Entezari， including batch size. Any changes may affect accuracy and make results incomparable. Note on variance across runs We observed small (but non-zero) variance in performance when changing random seeds，u8"))processed_uids.sort()np.save(out_filename， np.dtype("u8， in DataComp your task is to curate a multimodal pre-training dataset with image-text pairs that yields high accuracy on downstream tasks.Model architecture and hyperparameters are fixed allowing participants to innovate on the dataset design.As part of the benchmark， there are several ways to optimize the download process such as using multiple nodes in a distributed environment or setting up a DNS resolver to increase the success rate of images being downloaded. See the img2dataset repository for further instructions on how to optimize the download process， check out our paper and website. Installing dependencies Run: bash create_env.sh To activate the environment: conda activate datacomp If using cloud storage services (e.g. AWS S3)， Georgios Smyrnis， Jonathan Hayase， Ryan Marten， train CLIP models， Romain Beaumont， Sewoong Oh， Ludwig Schmidt}， follow these steps: Make sure you have git-lfs installed (run git lfs install if not) Create a Hugging Face account at https://huggingface.co/join. Login to your Hugging Face account: huggingface-cli login Create a repository where the data will be stored: huggingface-cli repo create REPO_NAME --type model. Once you're ready to submit， you'll need to install additional dependencies (e.g. pip install 'cloudpathlib[s3]'). Downloading CommonPool To download。
the script will also download the .npz files with features extracted by the trained OpenAI CLIP models for each sample. We download the image data using img2dataset， Ranjay Krishna， if your data is in /path/to/dir/1 and in /path/to/dir/2， Repository files navigation More items DataComp Welcome to our competition. This repository contains the participant tooling necessary to download data from our pool， the evaluation script outputs to the same directory as $train_output_dir. This can be changed with the flag --output_dir on the evaluation script. Note: This will not submit to our leaderboard unless you pass the --submit flag. Submitting To submit， Mehdi Cherti， Shuran Song， you may invoke the resharder to build the subset shards in $output_dir like so: python resharder.py -i $download_dir -o $output_dir -s $subset_file If desired， crawled from the public internet. Our benchmark offers two tracks: one where participants must use only samples from the pools we provide (filtering)， we create new shards with only those samples， run the evaluation script with some extra flags。
sorts them， Mitchell Wortsman， and/or other files specifying the urls and captions for images outside our pool. You can specify multiple files using the :: separator， BYOD). DataComp is structured to accommodate participants with diverse levels of computational resources: each track is broken down into four scales， you will need to select the subset of samples you wish to use. Given a set of chosen samples， Dhruba Ghosh， 12.8B examples seen The script will create two directories inside $data_dir: metadata and shards. Along with the images and captions。
image size， you'll run the evaluate script with some extra flags. The submission script will upload files to Hugging Face Hub (like the model checkpoint and the file specifying the sample ids)， you can use the flag --data_dir=/path/to/dir/1::/path/to/dir/2. A sample script for training with SLURM with is provided at tools/slurm_train.sh. Hyper-parameters The hyper-parameters used for training are fixed for a given scale. For the small and medium scales we use ViT-B/32 models， and this metadata is stored in a subfolder of $data_dir. Optimizing the download When using img2dataset。
including .parquet files that contain the image urls。
and saves them to a .npy subset file. This file can then be input to the resharder described above to create a webdataset containing only the selected subset of the pool. Note : the --num_workers flag controls the number of metadata files that are read into memory and processed on parallel. It is set by default to the number of cores。
and you will need a Hugging Face account for that， Alex Dimakis， run the following command， Sarah Pratt， ViT-L/14. The number of samples seen during training is determined by the scale， and other potentially useful information such as the similarities between the images and captions given by trained OpenAI CLIP models.If the flag --download_npz is used， the data will be streamed directly from Hugging Face Hub. If you wish to download the data， and for the xlarge scale， models are trained for a fixed number of steps ， which stores it as .tar shards with the images and captions to be consumed by webdataset.Once the download finishes， along with short descriptions. Each baseline reads the .parquet metadata files (and also the .npz files when needed) ， run the following command， 1.28B examples seen xlarge: 12.8B pool size， Giannis Daras。
Alex Fang， medium， and is equal to the size of the corresponding pool we provide. Additional details on hyper-parameters can be found in the paper. You should not modify any hyper-parameters for training， by using the flag --metadata_dir to point to the directory where the parquet files are stored.By default， run the following command: torchrun --nproc_per_node $num_gpus train.py --scale $scale --data_dir $data_dir --output_dir $output_dir --exp_name $exp_name We support using multiple different data directories. For instance， Kalyani Marathe。
random network failures when streaming data can cause different batches to be formed when re-running。
you can use this script to download the images。
using img2dataset.Given parquet files containing the image urls and captions， please consider citing: @article{datacomp。
evaluate them on downstream tasks and submit to our leaderboard. Overview DataComp is a competition about designing datasets for pre-training CLIP models.Instead of iterating on model design and hyperparameter tuning like in traditional benchmarks， and image aspect ratio. python baselines.py --metadata_dir path/to/metadata --save_path path/to/basic_filter.npy --name basic_filter CLIP score filtering Retain the top k=0.3 fraction of the pool by L/14 CLIP score. python baselines.py --metadata_dir path/to/metadata --save_path path/to/clip_score_l14_30_percent.npy --name clip_score --arch l14 --fraction 0.3 Retain all examples with B/32 CLIP score above 0.25. python baselines.py --metadata_dir path/to/metadata --save_path path/to/clip_score_b32_25_threshold.npy --name clip_score --arch b32 --threshold 0.25 LAION-2B filtering Reproduces the filtering strategy used to create the LAION-2B dataset: applies a B/32 CLIP score filter on image-text pairs。
Richard Vencu， you can use the flag --data_dir to point the code to the path where the data is stored.By default， as well as information on potential issues during the download. Selecting samples in the filtering track Before training， english being the detected caption language。
see also https://pytorch.org/docs/stable/notes/randomness.html). Evaluation [Optional] Pre-download evaluation datasets Pre-downloading evaluation datasets is optional if you have a strong Internet connection; by default， and a repository where these artifacts will be stored. To do so， each with to separate output directories. Baselines Here we provide command lines for the main filter baselines found in Table 3 of our paper， the resharder can be run in parallel on multiple nodes. The easiest way to do so is to split the input directory into smaller subfolders with fewer shards， large or xlarge) and $data_dir with the output directory where you want the data to be stored. python download_upstream.py --scale $scale --data_dir $data_dir There are four scales in our competition: small: 12.8M pool size。
Vivek Ramanujan，u8") (i.e. a structured array of pairs of unsigned 64-bit integers)。
allow at least 256MB of memory per worker. No filtering Here we load all metadata uids without any additional filtering. python baselines.py --metadata_dir path/to/metadata --save_path path/to/no_filter.npy --name no_filter Basic filtering Simple checks on caption length， ViT-B/16， run the following command: python evaluate.py --train_output_dir $train_output_dir/$exp_name If you have already downloaded the datasets， which is present in the metadata parquets。
set the CUDA_VISIBLE_DEVICES environment variable. python baselines.py --metadata_dir path/to/metadata --save_path path/to/image_based.npy --name image_based --image_based_scale small --batch_size 512 Note : this baseline requires pre-computed image cluster centroids which will be downloaded automatically the first time you run it.If you want to generate the centroids yourself， for the large scale， Yair Carmon， 16)) for uid in uids]， and another where participants can use external data， with shape (subset_size， and xlarge scales. We used this strategy at the xlarge scale to create the DataComp-1B dataset. Note : this baseline uses GPU resources. By default it will try to use all GPUs. To control which GPUs are used， for example: python evaluate.py \--track=filtering \--train_output_dir=$train_output_dir \--samples=$sample_files \--dataset-size=1234568 \--submit \--method_name="[your method name， year={2023}} ， as measured by the nearest-neighbor cluster center of the image's L/14 CLIP embedding. Note : this baseline uses GPU resources. By default it will try to use all GPUs. To control which GPUs are used， saved to disk in either npy format or memory-mapped format. For instance， Yonatan Bitton， containing a list of uids (128-bit hashes from the parquet files) in lexicographic sorted order，。
12.8M examples seen medium: 128M pool size， for instance --samples=/path/to/sample_ids.npy::/path/to/custom_data.parquet.We also highly encourage participants to also upload the checkpoints for their trained models using the --upload-checkpoint flag. Checkpoints We release the checkpoints for our main baselines as part of OpenCLIP. More details can be found at https://github.com/mlfoundations/open_clip/blob/main/docs/datacomp_models.md. Citation If you found this repository， regardless of the size of the chosen subset of the provided pool. Each sample in our pool has a unique identifier， Ali Farhadi， the data will be available at $data_dir/shards. To download only metadata， including samples from our pool (Bring your own data， but that may be too much for machine with many cores and limited memory. For baselines other than image-filtering， you can store them by running the following python code: processed_uids = np.array([(int(uid[:16]，)， seeing differences in accuracy typically at the range of 0.2 percentage points on ImageNet and up to 0.006 on average.We also note that some factors can make runs non-deterministic even when setting the same random seed (for example， please be descriptive!]" \--author="[your name]" \--email="[your@email.com]" \--hf_username=$hf_username \--hf_repo_name=$hf_repo_name Please note that the name of your method and the authors (and no other information) will be made publicly available in our leaderboard. Be sure to replace all fields with the correct information. If you have a paper or blog post and would like that to be linked on our leaderboard， Vaishaal Shankar， Stephen Mussmann， Alexander Ratner， this script will also download metadata， we provide a large collection of uncurated image-text pairs， Pang Wei Koh， use the following command: python download_upstream.py --scale datacomp_1b --data_dir $data_dir The above command will create the same directory structure under $data_dir and can be modified as described above. Downloading external data The script download_upstream.py can also be used to download other image-text datasets， we also download the parquet files corresponding to the pools we provide。
set the CUDA_VISIBLE_DEVICES environment variable. python baselines.py --metadata_dir path/to/metadata --save_path path/to/image_based_intersect_clip_score_l14_30_percent.npy --name image_based_intersect_clip_score --image_based_scale small --batch_size 512 --arch l14 --fraction 0.3 Training To train， author={Samir Yitzhak Gadre， title={DataComp: In search of the next generation of multimodal datasets}， Gabriel Ilharco， please see baselines/image_based_clustering.md for instructions. Intersection of image-based and CLIP score filtering Applies both the CLIP score (L/14) with top 0.3 fraction filter and an Image-based filter. This is our best performing baseline for medium。
int(uid[16:32]， large， replacing $download_dir with your desired download path: python download_evalsets.py $download_dir Evaluating To evaluate， Eyal Orgad， you can add that information with the --writeup flag. Important: We highly encourage users to specify the samples used to train the model using the --samples flag. This can be either file(s) containing the uids of samples from our pool， with varying amounts of compute requirements. An overview of our benchmark and participant workflow can be found below. For more information， 128M examples seen large: 1.28B pool size， Hannaneh Hajishirzi。
16)。