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Contrastive Language-Image Pretraining (CLIP) has emerged as a novel paradigmto learn visual models from language supervision. While researchers continue topush the frontier of CLIP, reproducing these works remains challenging. This isbecause researchers do not choose consistent training recipes and even usedifferent data, hampering the fair comparison between different methods. Inthis work, we propose CLIP-benchmark, a first attempt to evaluate, analyze, andbenchmark CLIP and its variants. We conduct a comprehensive analysis of threekey factors: data, supervision, and model architecture. We find considerableintuitive or counter-intuitive insights: (1). Data quality has a significantimpact on performance. (2). Certain supervision has different effects forConvolutional Networks (ConvNets) and Vision Transformers (ViT). Applying moreproper supervision can effectively improve the performance of CLIP. (3).Curtailing the text encoder reduces the training cost but not much affect thefinal performance. Moreover, we further combine DeCLIP with FILIP, bringing usthe strongest variant DeFILIP. The CLIP-benchmark would be released at:https://github.com/Sense-GVT/DeCLIP for future CLIP research.

Democratizing Contrastive Language-Image Pre-training: A CLIP Benchmark of Data, Model, and Supervision