  Current object detectors are limited in vocabulary size due to the small
scale of detection datasets. Image classifiers, on the other hand, reason about
much larger vocabularies, as their datasets are larger and easier to collect.
We propose Detic, which simply trains the classifiers of a detector on image
classification data and thus expands the vocabulary of detectors to tens of
thousands of concepts. Unlike prior work, Detic does not need complex
assignment schemes to assign image labels to boxes based on model predictions,
making it much easier to implement and compatible with a range of detection
architectures and backbones. Our results show that Detic yields excellent
detectors even for classes without box annotations. It outperforms prior work
on both open-vocabulary and long-tail detection benchmarks. Detic provides a
gain of 2.4 mAP for all classes and 8.3 mAP for novel classes on the
open-vocabulary LVIS benchmark. On the standard LVIS benchmark, Detic obtains
41.7 mAP when evaluated on all classes, or only rare classes, hence closing the
gap in performance for object categories with few samples. For the first time,
we train a detector with all the twenty-one-thousand classes of the ImageNet
dataset and show that it generalizes to new datasets without finetuning. Code
is available at \url{https://github.com/facebookresearch/Detic}.
