---
library_name: keras-hub
---
### Model Overview
# Model Summary

This model is a CLIP (Contrastive Language-Image Pre-training) neural network. CLIP revolutionizes image understanding by learning visual concepts from natural language descriptions found online.  It's been trained on a massive dataset of image-text pairs, allowing it to excel at tasks like zero-shot image classification, image search based on text queries, and robust visual understanding. With CLIP, you can explore the power of aligning image and text representations within a shared embedding space.


Weights are released under the [MIT License](https://opensource.org/license/mit). Keras model code is released under the [Apache 2 License](https://github.com/keras-team/keras-hub/blob/master/LICENSE).

## Links

* [CLIP Quickstart Notebook](https://www.kaggle.com/code/laxmareddypatlolla/clip-quickstart-notebook)
* [CLIP API Documentation](https://keras.io/keras_hub/api/models/clip/)
* [CLIP Model Card](https://huggingface.co/docs/transformers/en/model_doc/clip)
* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/)
* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)

## Installation

Keras and KerasHub can be installed with:

```
pip install -U -q keras-hub
pip install -U -q keras

```

Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page.

## Presets

The following model checkpoints are provided by the Keras team. Full code examples for each are available below.
| Preset name                | Parameters | Description                                                                                                                                                                                                                                                                                                       |
|----------------------------|------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| clip-vit-base-patch16      | 149.62M    | The model uses a ViT-B/16 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. The model uses a patch size of 16 and input images of size (224, 224) |
| clip-vit-base-patch32      | 151.28M    | The model uses a ViT-B/32 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss.The model uses a patch size of 32 and input images of size (224, 224)  |
| clip-vit-large-patch14     | 427.62M    | The model uses a ViT-L/14 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss.The model uses a patch size of 14 and input images of size (224, 224)  |
| clip-vit-large-patch14-336 | 427.94M    | The model uses a ViT-L/14 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss.The model uses a patch size of 14 and input images of size (336, 336)  |
|  clip_vit_b_32_laion2b_s34b_b79k    | 151.28M    | 151 million parameter, 12-layer for vision and 12-layer for text, patch size of 32, Open CLIP model. |
|  clip_vit_h_14_laion2b_s32b_b79k    | 986.11M    | 986 million parameter, 32-layer for vision and 24-layer for text, patch size of 14, Open CLIP model. |
|  clip_vit_g_14_laion2b_s12b_b42k    | 1.37B   | 1.4 billion parameter, 40-layer for vision and 24-layer for text, patch size of 14, Open CLIP model. |
|  clip_vit_bigg_14_laion2b_39b_b160k   | 2.54B   | 2.5 billion parameter, 48-layer for vision and 32-layer for text, patch size of 14, Open CLIP model. |

## Example Usage
```python
import keras
import numpy as np
import matplotlib.pyplot as plt
from keras_hub.models import CLIPBackbone, CLIPTokenizer
from keras_hub.layers import CLIPImageConverter

# instantiate the model and preprocessing tools
clip = CLIPBackbone.from_preset("clip_vit_g_14_laion2b_s12b_b42k")
tokenizer = CLIPTokenizer.from_preset("clip_vit_g_14_laion2b_s12b_b42k",
sequence_length=5)
image_converter = CLIPImageConverter.from_preset("clip_vit_g_14_laion2b_s12b_b42k")

# obtain tokens for some input text
tokens = tokenizer.tokenize(["mountains", "cat on tortoise", "house"])

# preprocess image and text
image = keras.utils.load_img("cat.jpg")
image = image_converter(np.array([image]).astype(float))

# query the model for similarities
clip({
     "images": image,
     "token_ids": tokens,
})
```

## Example Usage with Hugging Face URI

```python
import keras
import numpy as np
import matplotlib.pyplot as plt
from keras_hub.models import CLIPBackbone, CLIPTokenizer
from keras_hub.layers import CLIPImageConverter

# instantiate the model and preprocessing tools
clip = CLIPBackbone.from_preset("hf://keras/clip_vit_g_14_laion2b_s12b_b42k")
tokenizer = CLIPTokenizer.from_preset("hf://keras/clip_vit_g_14_laion2b_s12b_b42k",
sequence_length=5)
image_converter = CLIPImageConverter.from_preset("hf://keras/clip_vit_g_14_laion2b_s12b_b42k")

# obtain tokens for some input text
tokens = tokenizer.tokenize(["mountains", "cat on tortoise", "house"])

# preprocess image and text
image = keras.utils.load_img("cat.jpg")
image = image_converter(np.array([image]).astype(float))

# query the model for similarities
clip({
     "images": image,
     "token_ids": tokens,
})
```