![]() Most inspirational for CLIP is the work of Ang Li and his co-authors at FAIR who in 2016 demonstrated using natural language supervision to enable zero-shot transfer to several existing computer vision classification datasets, such as the canonical ImageNet dataset. The same year DeVISE scaled this approach and demonstrated that it was possible to fine-tune an ImageNet model so that it could generalize to correctly predicting objects outside the original 1000 training set. In 2013, Richer Socher and co-authors at Stanford developed a proof of concept by training a model on CIFAR-10 to make predictions in a word vector embedding space and showed this model could predict two unseen classes. A critical insight was to leverage natural language as a flexible prediction space to enable generalization and transfer. The idea of zero-data learning dates back over a decade but until recently was mostly studied in computer vision as a way of generalizing to unseen object categories. This is a key change: by not directly optimizing for the benchmark, we show that it becomes much more representative: our system closes this “robustness gap” by up to 75% while matching the performance of the original ResNet-50 on ImageNet zero-shot without using any of the original 1.28M labeled examples.ĬLIP ( Contrastive Language–Image Pre-training) builds on a large body of work on zero-shot transfer, natural language supervision, and multimodal learning. By design, the network can be instructed in natural language to perform a great variety of classification benchmarks, without directly optimizing for the benchmark’s performance, similar to the “ zero-shot” capabilities of GPT-2 and GPT-3. We present a neural network that aims to address these problems: it is trained on a wide variety of images with a wide variety of natural language supervision that’s abundantly available on the internet. Hopefully you see something like this: data/dog.jpg: Predicted in 0.160994 seconds.Although deep learning has revolutionized computer vision, current approaches have several major problems: typical vision datasets are labor intensive and costly to create while teaching only a narrow set of visual concepts standard vision models are good at one task and one task only, and require significant effort to adapt to a new task and models that perform well on benchmarks have disappointingly poor performance on stress tests, casting doubt on the entire deep learning approach to computer vision. darknet classify cfg/tiny.cfg tiny.weights data/dog.jpg Here's how to use it in Darknet (and also how to install Darknet): git clone The real winner here is clearly the Darknet reference model but if you insist on wanting a small model, use Tiny Darknet. Alexnet was a great first pass at classification but we shouldn't be stuck back in the days when networks this bad are also this slow!īut anyway, people are super into SqueezeNet so if you really insist on small networks, use this: Tiny Darknet Model So what about SqueezeNet? Sure the weights are only 4.8 MB but a forward pass is still 2.2 billion operations. Darknet is 2.9 times faster and it's small and it's 4% more accurate. When most high quality images are 10MB or more why do we care if our models are 5 MB or 50 MB? If you want a small model that's actually FAST, why not check out the Darknet reference network? It's only 28 MB but more importantly, it's only 800 million floating point operations. SqueezeNet is cool but it's JUST optimizing for parameter count. ![]() I've heard a lot of people talking about SqueezeNet.
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