KerasCV

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form.onsubmit = function(e) { e.preventDefault(); var query = document.getElementById('search-input').value; window.location.href = '/search.html?query=' + query; return False } </script> </div> <div class='k-main-inner' id='k-main-id'> <div class='k-location-slug'> ► KerasCV </div> <div class='k-content'> <h1 id="kerascv">KerasCV</h1> <a class="github-button" href="https://github.com/keras-team/keras-cv" data-size="large" data-show-count="true" aria-label="Star keras-team/keras-cv on GitHub">Star</a> KerasCV is a library of modular computer vision components that work natively with TensorFlow, JAX, or PyTorch. Built on Keras 3, these models, layers, metrics, callbacks, etc., can be trained and serialized in any framework and re-used in another without costly migrations. KerasCV can be understood as a horizontal extension of the Keras API: the components are new first-party Keras objects that are too specialized to be added to core Keras. They receive the same level of polish and backwards compatibility guarantees as the core Keras API, and they are maintained by the Keras team. Our APIs assist in common computer vision tasks such as data augmentation, classification, object detection, segmentation, image generation, and more. Applied computer vision engineers can leverage KerasCV to quickly assemble production-grade, state-of-the-art training and inference pipelines for all of these common tasks. <img style="width: 440px; max-width: 90%;" src="/img/keras-cv-augmentations.gif"> <h2 id="quick-links">Quick Links</h2> <ul> <li><a href="https://keras.io/api/keras_cv/models/">List of available models and presets</a></li> <li><a href="https://keras.io/guides/keras_cv/">Developer Guides</a></li> <li><a href="https://github.com/keras-team/keras-cv/blob/master/CONTRIBUTING.md">Contributing Guide</a></li> <li><a href="https://github.com/keras-team/keras-cv/blob/master/API_DESIGN.md">API Design Guidelines</a></li> </ul> <h2 id="installation">Installation</h2> KerasCV supports both Keras 2 and Keras 3. We recommend Keras 3 for all new users, as it enables using KerasCV models and layers with JAX, TensorFlow and PyTorch. <h3 id="keras-2-installation">Keras 2 Installation</h3> To install the latest KerasCV release with Keras 2, simply run: <div class="codehilite"><pre><code>pip install --upgrade keras-cv tensorflow </code></pre></div> <h3 id="keras-3-installation">Keras 3 Installation</h3> There are currently two ways to install Keras 3 with KerasCV. To install the stable versions of KerasCV and Keras 3, you should install Keras 3 after installing KerasCV. This is a temporary step while TensorFlow is pinned to Keras 2, and will no longer be necessary after TensorFlow 2.16. <div class="codehilite"><pre><code>pip install --upgrade keras-cv tensorflow pip install --upgrade keras </code></pre></div> To install the latest changes nightly for KerasCV and Keras, you can use our nightly package. <div class="codehilite"><pre><code>pip install --upgrade keras-cv-nightly tf-nightly </code></pre></div> Note: Keras 3 will not function with TensorFlow 2.14 or earlier. See <a href="/getting_started/">Getting started with Keras</a> for more information on installing Keras generally and compatibility with different frameworks. <h2 id="quickstart">Quickstart</h2> <div class="codehilite"><pre><code>import os os.environ["KERAS_BACKEND"] = "tensorflow" # Or "jax" or "torch"! import tensorflow as tf import keras_cv import tensorflow_datasets as tfds import keras # Create a preprocessing pipeline with augmentations BATCH_SIZE = 16 NUM_CLASSES = 3 augmenter = keras_cv.layers.Augmenter( [ keras_cv.layers.RandomFlip(), keras_cv.layers.RandAugment(value_range=(0, 255)), keras_cv.layers.CutMix(), ], ) def preprocess_data(images, labels, augment=False): labels = tf.one_hot(labels, NUM_CLASSES) inputs = {"images": images, "labels": labels} outputs = inputs if augment: outputs = augmenter(outputs) return outputs['images'], outputs['labels'] train_dataset, test_dataset = tfds.load( 'rock_paper_scissors', as_supervised=True, split=['train', 'test'], ) train_dataset = train_dataset.batch(BATCH_SIZE).map( lambda x, y: preprocess_data(x, y, augment=True), num_parallel_calls=tf.data.AUTOTUNE).prefetch( tf.data.AUTOTUNE) test_dataset = test_dataset.batch(BATCH_SIZE).map( preprocess_data, num_parallel_calls=tf.data.AUTOTUNE).prefetch( tf.data.AUTOTUNE) # Create a model using a pretrained backbone backbone = keras_cv.models.EfficientNetV2Backbone.from_preset( "efficientnetv2_b0_imagenet" ) model = keras_cv.models.ImageClassifier( backbone=backbone, num_classes=NUM_CLASSES, activation="softmax", ) model.compile( loss='categorical_crossentropy', optimizer=keras.optimizers.Adam(learning_rate=1e-5), metrics=['accuracy'] ) # Train your model model.fit( train_dataset, validation_data=test_dataset, epochs=8, ) </code></pre></div> <h2 id="disclaimer">Disclaimer</h2> KerasCV provides access to pre-trained models via the <code>keras_cv.models</code> API. These pre-trained models are provided on an "as is" basis, without warranties or conditions of any kind. The following underlying models are provided by third parties, and are subject to separate licenses: StableDiffusion, Vision Transformer <h2 id="citing-kerascv">Citing KerasCV</h2> If KerasCV helps your research, we appreciate your citations. Here is the BibTeX entry: <div class="codehilite"><pre><code>@misc{wood2022kerascv, title={KerasCV}, author={Wood, Luke and Tan, Zhenyu and Stenbit, Ian and Bischof, Jonathan and Zhu, Scott and Chollet, Fran\c{c}ois and Sreepathihalli, Divyashree and Sampath, Ramesh and others}, year={2022}, howpublished={\url{https://github.com/keras-team/keras-cv}}, } </code></pre></div> </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#kerascv'>KerasCV</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#quick-links'>Quick Links</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#installation'>Installation</a> </div> <div class='k-outline-depth-3'> <a href='#keras-2-installation'>Keras 2 Installation</a> </div> <div class='k-outline-depth-3'> <a href='#keras-3-installation'>Keras 3 Installation</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#quickstart'>Quickstart</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#disclaimer'>Disclaimer</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#citing-kerascv'>Citing KerasCV</a> </div> </div> </div> </div> </div> </body> <footer style="float: left; width: 100%; padding: 1em; border-top: solid 1px #bbb;"> <a href="https://policies.google.com/terms">Terms</a> | <a href="https://policies.google.com/privacy">Privacy</a> </footer> </html>

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