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[Blog] [Paper] [Model Card] [Colab]
CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. It can be instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, similarly to the zero-shot capabilities of GPT-2 and 3. We found CLIP matches the performance of the original ResNet50 on ImageNet “zero-shot” without using any of the original 1.28M labeled examples, overcoming several major challenges in computer vision.
First, install PyTorch 1.7.1 (or later) and torchvision, as well as small additional dependencies, and then install this repo as a Python package. On a CUDA GPU machine, the following will do the trick:
Replace cudatoolkit=11.0 above with the appropriate CUDA version on your machine or cpuonly when installing on a machine without a GPU.
The CLIP module clip provides the following methods:
clip.available_models()
Returns the names of the available CLIP models.
clip.load(name, device=..., jit=False)
Returns the model and the TorchVision transform needed by the model, specified by the model name returned by clip.available_models(). It will download the model as necessary. The name argument can also be a path to a local checkpoint.
The device to run the model can be optionally specified, and the default is to use the first CUDA device if there is any, otherwise the CPU. When jit is False, a non-JIT version of the model will be loaded.
Zero-Shot Prediction
The code below performs zero-shot prediction using CLIP, as shown in Appendix B in the paper. This example takes an image from the CIFAR-100 dataset, and predicts the most likely labels among the 100 textual labels from the dataset. The output will look like the following (the exact numbers may be slightly different depending on the compute device): Note that this example uses the encode_image() and encode_text() methods that return the encoded features of given inputs.
Linear-probe evaluation
The example below uses scikit-learn to perform logistic regression on image features. Note that the C value should be determined via a hyperparameter sweep using a validation split.
•OpenCLIP: includes larger and independently trained CLIP models up to ViT-G/14
•Hugging Face implementation of CLIP: for easier integration with the HF ecosystem
5 gen 2021 · We’re introducing a neural network called CLIP which efficiently learns visual concepts from natural language supervision. CLIP can be applied to any visual classification benchmark by simply providing the names of the visual categories to be recognized, similar to the “zero-shot” capabilities of GPT-2 and GPT-3.
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