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352/352 [==============================] - 6s 16ms/step - loss: 1.1524 - accuracy: 0.7054 - top-5-accuracy: 0.9783 - val_loss: 1.1803 - val_accuracy: 0.7000 - val_top-5-accuracy: 0.9740 |
Epoch 11/30 |
352/352 [==============================] - 6s 16ms/step - loss: 1.1219 - accuracy: 0.7222 - top-5-accuracy: 0.9798 - val_loss: 1.1066 - val_accuracy: 0.7254 - val_top-5-accuracy: 0.9812 |
Epoch 12/30 |
352/352 [==============================] - 6s 16ms/step - loss: 1.1029 - accuracy: 0.7287 - top-5-accuracy: 0.9811 - val_loss: 1.0844 - val_accuracy: 0.7388 - val_top-5-accuracy: 0.9814 |
Epoch 13/30 |
352/352 [==============================] - 6s 16ms/step - loss: 1.0841 - accuracy: 0.7380 - top-5-accuracy: 0.9825 - val_loss: 1.1159 - val_accuracy: 0.7280 - val_top-5-accuracy: 0.9792 |
Epoch 14/30 |
352/352 [==============================] - 6s 16ms/step - loss: 1.0677 - accuracy: 0.7462 - top-5-accuracy: 0.9832 - val_loss: 1.0862 - val_accuracy: 0.7444 - val_top-5-accuracy: 0.9834 |
Epoch 15/30 |
352/352 [==============================] - 6s 16ms/step - loss: 1.0511 - accuracy: 0.7535 - top-5-accuracy: 0.9846 - val_loss: 1.0613 - val_accuracy: 0.7494 - val_top-5-accuracy: 0.9832 |
Epoch 16/30 |
352/352 [==============================] - 6s 16ms/step - loss: 1.0377 - accuracy: 0.7608 - top-5-accuracy: 0.9854 - val_loss: 1.0379 - val_accuracy: 0.7606 - val_top-5-accuracy: 0.9834 |
Epoch 17/30 |
352/352 [==============================] - 6s 16ms/step - loss: 1.0304 - accuracy: 0.7650 - top-5-accuracy: 0.9849 - val_loss: 1.0602 - val_accuracy: 0.7562 - val_top-5-accuracy: 0.9814 |
Epoch 18/30 |
352/352 [==============================] - 6s 16ms/step - loss: 1.0121 - accuracy: 0.7746 - top-5-accuracy: 0.9869 - val_loss: 1.0430 - val_accuracy: 0.7630 - val_top-5-accuracy: 0.9834 |
Epoch 19/30 |
352/352 [==============================] - 6s 16ms/step - loss: 1.0037 - accuracy: 0.7760 - top-5-accuracy: 0.9872 - val_loss: 1.0951 - val_accuracy: 0.7460 - val_top-5-accuracy: 0.9826 |
Epoch 20/30 |
352/352 [==============================] - 6s 16ms/step - loss: 0.9964 - accuracy: 0.7805 - top-5-accuracy: 0.9871 - val_loss: 1.0683 - val_accuracy: 0.7538 - val_top-5-accuracy: 0.9834 |
Epoch 21/30 |
352/352 [==============================] - 6s 16ms/step - loss: 0.9838 - accuracy: 0.7850 - top-5-accuracy: 0.9886 - val_loss: 1.0185 - val_accuracy: 0.7770 - val_top-5-accuracy: 0.9876 |
Epoch 22/30 |
352/352 [==============================] - 6s 16ms/step - loss: 0.9742 - accuracy: 0.7904 - top-5-accuracy: 0.9894 - val_loss: 1.0253 - val_accuracy: 0.7738 - val_top-5-accuracy: 0.9838 |
Epoch 23/30 |
352/352 [==============================] - 6s 16ms/step - loss: 0.9662 - accuracy: 0.7935 - top-5-accuracy: 0.9889 - val_loss: 1.0107 - val_accuracy: 0.7786 - val_top-5-accuracy: 0.9860 |
Epoch 24/30 |
352/352 [==============================] - 6s 16ms/step - loss: 0.9549 - accuracy: 0.7994 - top-5-accuracy: 0.9897 - val_loss: 1.0089 - val_accuracy: 0.7790 - val_top-5-accuracy: 0.9852 |
Epoch 25/30 |
352/352 [==============================] - 6s 16ms/step - loss: 0.9522 - accuracy: 0.8018 - top-5-accuracy: 0.9896 - val_loss: 1.0214 - val_accuracy: 0.7780 - val_top-5-accuracy: 0.9866 |
Epoch 26/30 |
352/352 [==============================] - 6s 16ms/step - loss: 0.9469 - accuracy: 0.8023 - top-5-accuracy: 0.9897 - val_loss: 0.9993 - val_accuracy: 0.7816 - val_top-5-accuracy: 0.9882 |
Epoch 27/30 |
352/352 [==============================] - 6s 16ms/step - loss: 0.9463 - accuracy: 0.8022 - top-5-accuracy: 0.9906 - val_loss: 1.0071 - val_accuracy: 0.7848 - val_top-5-accuracy: 0.9850 |
Epoch 28/30 |
352/352 [==============================] - 6s 16ms/step - loss: 0.9336 - accuracy: 0.8077 - top-5-accuracy: 0.9909 - val_loss: 1.0113 - val_accuracy: 0.7868 - val_top-5-accuracy: 0.9856 |
Epoch 29/30 |
352/352 [==============================] - 6s 16ms/step - loss: 0.9352 - accuracy: 0.8071 - top-5-accuracy: 0.9909 - val_loss: 1.0073 - val_accuracy: 0.7856 - val_top-5-accuracy: 0.9830 |
Epoch 30/30 |
352/352 [==============================] - 6s 16ms/step - loss: 0.9273 - accuracy: 0.8112 - top-5-accuracy: 0.9908 - val_loss: 1.0144 - val_accuracy: 0.7792 - val_top-5-accuracy: 0.9836 |
313/313 [==============================] - 2s 6ms/step - loss: 1.0396 - accuracy: 0.7676 - top-5-accuracy: 0.9839 |
Test accuracy: 76.76% |
Test top 5 accuracy: 98.39% |
Let's now visualize the training progress of the model. |
plt.plot(history.history[\"loss\"], label=\"train_loss\") |
plt.plot(history.history[\"val_loss\"], label=\"val_loss\") |
plt.xlabel(\"Epochs\") |
plt.ylabel(\"Loss\") |
plt.title(\"Train and Validation Losses Over Epochs\", fontsize=14) |
plt.legend() |
plt.grid() |
plt.show() |
png |
The CCT model we just trained has just 0.4 million parameters, and it gets us to ~78% top-1 accuracy within 30 epochs. The plot above shows no signs of overfitting as well. This means we can train this network for longer (perhaps with a bit more regularization) and may obtain even better performance. This performance c... |
For a comparison, a ViT model takes about 4.7 million parameters and 100 epochs of training to reach a top-1 accuracy of 78.22% on the CIFAR-10 dataset. You can refer to this notebook to know about the experimental setup. |
The authors also demonstrate the performance of Compact Convolutional Transformers on NLP tasks and they report competitive results there. |
Training with consistency regularization for robustness against data distribution shifts. |
Deep learning models excel in many image recognition tasks when the data is independent and identically distributed (i.i.d.). However, they can suffer from performance degradation caused by subtle distribution shifts in the input data (such as random noise, contrast change, and blurring). So, naturally, there arises a ... |
In this example, we will be training an image classification model enforcing a sense of consistency inside it by doing the following: |
Train a standard image classification model. |
Train an equal or larger model on a noisy version of the dataset (augmented using RandAugment). |
To do this, we will first obtain predictions of the previous model on the clean images of the dataset. |
We will then use these predictions and train the second model to match these predictions on the noisy variant of the same images. This is identical to the workflow of Knowledge Distillation but since the student model is equal or larger in size this process is also referred to as Self-Training. |
This overall training workflow finds its roots in works like FixMatch, Unsupervised Data Augmentation for Consistency Training, and Noisy Student Training. Since this training process encourages a model yield consistent predictions for clean as well as noisy images, it's often referred to as consistency training or tra... |
This example requires TensorFlow 2.4 or higher, as well as TensorFlow Hub and TensorFlow Models, which can be installed using the following command: |
!pip install -q tf-models-official tensorflow-addons |
Imports and setup |
from official.vision.image_classification.augment import RandAugment |
from tensorflow.keras import layers |
import tensorflow as tf |
import tensorflow_addons as tfa |
import matplotlib.pyplot as plt |
tf.random.set_seed(42) |
Define hyperparameters |
AUTO = tf.data.AUTOTUNE |
BATCH_SIZE = 128 |
EPOCHS = 5 |
CROP_TO = 72 |
RESIZE_TO = 96 |
Load the CIFAR-10 dataset |
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() |
val_samples = 49500 |
new_train_x, new_y_train = x_train[: val_samples + 1], y_train[: val_samples + 1] |
val_x, val_y = x_train[val_samples:], y_train[val_samples:] |
Create TensorFlow Dataset objects |
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