CommonPool and DataComp models
As part of DataComp, we trained models on CommonPool using various data filtering strategies. We release models for all four scales of the competition, small, medium, large and xlarge, corresponding to a pool size and number of samples seen of 12.8M, 128M, 1.28B and 12.8B, respectively.
The models are specified below, see our paper DataComp: In seearch of the next generation of multimodal datasets for more details.
xlarge scale models
datacomp_xl_s13b_b90k: A ViT-L/14 trained on DataComp-1B for 12.8B steps and batch size 90k. Achieves 79.2% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K.commonpool_xl_clip_s13b_b90k: A ViT-L/14 trained on CommonPool-XL filtered using CLIP scores, for 12.8B steps and batch size 90k. Achieves 76.4% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-L-14-CommonPool.XL.clip-s13B-b90K.commonpool_xl_laion_s13b_b90k: A ViT-L/14 trained on CommonPool-XL filtered using the LAION-2B filtering scheme, for 12.8B steps and batch size 90k. Achieves 75.5% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-L-14-CommonPool.XL.laion-s13B-b90K.commonpool_xl_s13b_b90k: A ViT-L/14 trained on CommonPool-XL without any filtering, for 12.8B steps and batch size 90k. Achieves 72.3% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-L-14-CommonPool.XL-s13B-b90K.
large scale models
datacomp_l_s1b_b8k: A ViT-B/16 trained on a 140M subset of DataComp-1B, for 1.28B steps and batch size 8k. Achieves 63.1% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-DataComp.L-s1B-b8K.commonpool_l_clip_s1b_b8k: A ViT-B/16 trained on CommonPool-L filtered using CLIP scores, for 1.28B steps and batch size 8k. Achieves 57.8% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L.clip-s1B-b8K.commonpool_l_laion_s1b_b8k: A ViT-B/16 trained on CommonPool-L filtered using the LAION-2B filtering scheme, for 1.28B steps and batch size 8k. Achieves 55.3% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L.laion-s1B-b8K.commonpool_l_image_s1b_b8k: A ViT-B/16 trained on CommonPool-L filtered using image-based filtering, for 1.28B steps and batch size 8k. Achieves 57.2% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L.image-s1B-b8K.commonpool_l_text_s1b_b8k: A ViT-B/16 trained on CommonPool-L filtered using text-based filtering, for 1.28B steps and batch size 8k. Achieves 56.1% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L.text-s1B-b8K.commonpool_l_basic_s1b_b8k: A ViT-B/16 trained on CommonPool-L filtered using basic filtering (English filtering + caption length and image size filtering), for 1.28B steps and batch size 8k. Achieves 51.6% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L.basic-s1B-b8K.commonpool_l_s1b_b8k: A ViT-B/16 trained on CommonPool-L without any filtering, for 1.28B steps and batch size 8k. Achieves 45.9% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L-s1B-b8K.
medium scale models
datacomp_m_s128m_b4k: A ViT-B/32 trained on a 14M subset of DataComp-1B, for 128M steps and batch size 4k. Achieves 29.7% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-DataComp.M-s128M-b4K.commonpool_m_clip_s128m_b4k: A ViT-B/32 trained on CommonPool-M filtered using CLIP scores, for 128M steps and batch size 4k. Achieves 27.3% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M.clip-s128M-b4K.commonpool_m_laion_s128m_b4k: A ViT-B/32 trained on CommonPool-M filtered using the LAION-2B filtering scheme, for 128M steps and batch size 4k. Achieves 23.0% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M.laion-s128M-b4K.commonpool_m_image_s128m_b4k: A ViT-B/32 trained on CommonPool-M filtered using image-based filtering, for 128M steps and batch size 4k. Achieves 26.8% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M.image-s128M-b4K.commonpool_m_text_s128m_b4k: A ViT-B/32 trained on CommonPool-M filtered using text-based filtering, for 128M steps and batch size 4k. Achieves 25.5% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M.text-s128M-b4K.commonpool_m_basic_s128m_b4k: A ViT-B/32 trained on CommonPool-M filtered using basic filtering (English filtering + caption length and image size filtering), for 128M steps and batch size 4k. Achieves 22.6% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M.basic-s128M-b4K.commonpool_m_s128m_b4k: A ViT-B/32 trained on CommonPool-M without any filtering, for 128M steps and batch size 4k. Achieves 17.6% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M-s128M-b4K.
small scale models
datacomp_s_s13m_b4k: A ViT-B/32 trained on a 1.4M subset of DataComp-1B, for 12.8M steps and batch size 4k. Achieves 3.9% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-DataComp.S-s13M-b4K.commonpool_s_clip_s13m_b4k: A ViT-B/32 trained on CommonPool-S filtered using CLIP scores, for 12.8M steps and batch size 4k. Achieves 5.1% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S.clip-s13M-b4K.commonpool_s_laion_s13m_b4k: A ViT-B/32 trained on CommonPool-S filtered using the LAION-2B filtering scheme scores, for 12.8M steps and batch size 4k. Achieves 3.1% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S.laion-s13M-b4K.commonpool_s_image_s13m_b4k: A ViT-B/32 trained on CommonPool-S filtered using image-based filtering, for 12.8M steps and batch size 4k. Achieves 4.3% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S.image-s13M-b4K.commonpool_s_text_s13m_b4k: A ViT-B/32 trained on CommonPool-S filtered using text-based filtering, for 12.8M steps and batch size 4k. Achieves 4.6% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S.text-s13M-b4K.commonpool_s_basic_s13m_b4k: A ViT-B/32 trained on CommonPool-S filtered using basic filtering (English filtering + caption length and image size filtering), for 12.8M steps and batch size 4k. Achieves 3.0% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S.basic-s13M-b4K.commonpool_s_s13m_b4k: A ViT-B/32 trained on CommonPool-S without any filtering, for 12.8M steps and batch size 4k. Achieves 2.5% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S-s13M-b4K.