MobileCLIP: Fast Image Text Models through Multi Modal Reinforced Training MobileCLIP was introduced in MobileCLIP: Fast Image Text Models through Multi Modal Reinforced Training (CVPR 2024), by Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel. This repository contains the MobileCLIP S2 checkpoint for OpenCLIP. Highlights Our smallest variant MobileCLIP S0 obtains similar zero shot performance as OpenAI's ViT B/16 model while being 4.8x faster and 2.8x smaller. MobileCLIP S2 obtains better avg zero shot performance than SigLIP's ViT B/16 model while being 2.3x faster and 2.1x smaller, and trained with 3x less seen samples. MobileCLIP B (LT) attains zero shot ImageNet performance of 77.2% which is significantly better than recent works like DFN and SigLIP with similar architectures or even OpenAI's ViT L/14@336. Checkpoints Model Seen Samples (B) Params (M) (img + txt) Latency (ms) (img + txt) IN 1k Zero Shot Top 1 Acc. (%) Avg. Perf. (%) on 38 datasets : : : : : : : : : : : MobileCLIP S0 13 11.4 + 42.4 1.5 + 1.6 67.8 58.1 MobileCLIP S1 13 21.5 + 63.4 2.5 + 3.3 72.6 61.3 MobileCLIP S2 13 35.7 + 63.4 3.6 + 3.3 74.4 63.7 MobileCLIP B 13 86…
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