Model Card: FLAVA Model Details FLAVA model was developed by the researchers at FAIR to understand if a single model can work across different modalities with a unified architecture. The model was pretrained solely using publicly available multimodal datasets containing 70M image text pairs in total and thus fully reproducible. Unimodal datasets ImageNet and BookCorpus + CCNews were also used to provide unimodal data to the model. The model (i) similar to CLIP can be used for arbitrary image classification tasks in a zero shot manner (ii) used for image or text retrieval in a zero shot manner (iii) can also be fine tuned for natural language understanding (NLU) tasks such as GLUE and vision and language reasoning tasks such as VQA v2. The model is able to use the data available as images, text corpus and image text pairs. In the original paper, the authors evaluate FLAVA on 32 tasks from computer vision, NLU and vision and language domains and show impressive performance across the board scoring higher micro average than CLIP while being open. Model Date Model was originally released in November 2021. Model Type The FLAVA model uses a ViT B/32 transformer for both image encoder and…
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