  Learning visual representations from natural language supervision has
recently shown great promise in a number of pioneering works. In general, these
language-augmented visual models demonstrate strong transferability to a
variety of datasets and tasks. However, it remains challenging to evaluate the
transferablity of these models due to the lack of easy-to-use evaluation
toolkits and public benchmarks. To tackle this, we build ELEVATER (Evaluation
of Language-augmented Visual Task-level Transfer), the first benchmark and
toolkit for evaluating(pre-trained) language-augmented visual models. ELEVATER
is composed of three components. (i) Datasets. As downstream evaluation suites,
it consists of 20 image classification datasets and 35 object detection
datasets, each of which is augmented with external knowledge. (ii) Toolkit. An
automatic hyper-parameter tuning toolkit is developed to facilitate model
evaluation on downstream tasks. (iii) Metrics. A variety of evaluation metrics
are used to measure sample-efficiency (zero-shot and few-shot) and
parameter-efficiency (linear probing and full model fine-tuning). ELEVATER is a
platform for Computer Vision in the Wild (CVinW), and is publicly released at
at https://computer-vision-in-the-wild.github.io/ELEVATER/
