We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re- purposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient la- beled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We in- vestigate and visualize the semantic clustering of deep convolutional features with respect to a va- riety of such tasks, including scene recognition, domain adaptation, and fine-grained recognition challenges. We compare the efficacy of relying on various network levels to define a fixed fea- ture, and report novel results that significantly outperform the state-of-the-art on several impor- tant vision challenges. We are releasing DeCAF, an open-source implementation of these deep convolutional activation features, along with all associated network parameters to enable vision researchers to be able to conduct experimenta- tion with deep representations across a range of visual concept learning paradigms.