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.
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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.
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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.
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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.
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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.