![]() As you recall, in the previous chapter, you converted a tic-tac-toe game to tensors. We’ll also get to utilize some existing best practices. This is, of course, true in the world of images! This is an exciting chapter because you’ll start working with real data, and we’ll be able to see the effects of your tensor operations immediately. You’re going to need to learn how to deal with large tensors, which are more common. As you might guess, printing tensors can take you only so far and in so many dimensions. Additionally, we want to track the performance during training therefore we will push the Tensorboard logs along with the weights to the Hub to use the "Training Metrics" Feature to monitor our training in real-time.In the previous chapter, you created and destroyed simple tensors. Therefore we want to push our model weights, during training and after training to the Hub to version it. Model (inputs =pixel_values, outputs =classifier ) CallbacksĪs mentioned in the beginning we want to use the Hugging Face Hub for model versioning and monitoring. Dense ( 10, activation = 'softmax', name = 'outputs' ) (vit ) # model keras_model = tf. Input (shape = ( 3, 224, 224 ), name = 'pixel_values', dtype = 'float32' ) # model layer vit = base_model. from_pretrained ( 'google/vit-base-patch16-224-in21k' ) # inputs pixel_values = tf. # alternatively create Image Classification model using Keras Layer and ViTModel # here you can also add the processing layers of keras import tensorflow as tfīase_model = TFViTModel. Below you find an example on how you would create a classification head. If you want to create you own classification head or if you want to add the augmentation/processing layer to your model, you can directly use the functional Keras API. compile (optimizer =optimizer, loss =loss, metrics =metrics SparseCategoricalCrossentropy (from_logits = True ) # define metrics metrics = # compile model model. from_pretrained ( model_id, num_labels = len (img_class_labels ), id2label =id2label, label2id =label2id, ) # define loss loss =tf. Optimizer, lr_schedule = create_optimizer ( init_lr =learning_rate, num_train_steps =num_train_steps, weight_decay_rate =weight_decay_rate, num_warmup_steps =num_warmup_steps, ) # load pre-trained ViT model model = TFViTForImageClassification. # create optimizer wight weigh decay num_train_steps = len (tf_train_dataset ) * num_train_epochs map method with batched=True.įrom transformers import TFViTForImageClassification, create_optimizer # we are also renaming our label col to labels to use `.to_tf_dataset` later eurosat_ds = eurosat_ds. update (feature_extractor (examples, ) ) return examples # basic processing (only resizing) def process (examples ) : examples. Sequential (, name = "data_augmentation", ) # use keras image data augementation processing def augmentation (examples ) : # print(examples) examples = ] return examples from_pretrained (model_id ) # learn more about data augmentation here: data_augmentation = keras. Next, one adds absolute position embeddings and provides this sequence to the Transformer encoder.įrom transformers import ViTFeatureExtractorįeature_extractor = ViTFeatureExtractor. One also adds a token at the beginning of the sequence in order to classify images. In order to provide images to the model, each image is split into a sequence of fixed-size patches (typically of resolution 16x16 or 32x32), which are linearly embedded. It attains excellent results compared to state-of-the-art convolutional networks. ![]() The Vision Transformer (ViT) is basically BERT, but applied to images. Quick intro: Vision Transformer (ViT) by Google Brain We are going to use all of the great Features from the Hugging Face ecosystem like model versioning and experiment tracking as well as all the great features of Keras like Early Stopping and Tensorboard. More information for the dataset can be found at the repository. The dataset is based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes within total 27,000 labeled and geo-referenced images. We are going to use the EuroSAT dataset for land use and land cover classification. ![]() In this demo, we will use the Hugging Faces transformers and datasets library together with Tensorflow & Keras to fine-tune a pre-trained vision transformer for image classification. Welcome to this end-to-end Image Classification example using Keras and Hugging Face Transformers. ![]()
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