Implements MLP-Mixer: An all-MLP Architecture for Vision.

Related tags

Deep Learningtensorflowkerasvisionmlpnon-convnon-self-attention
Overview

MLP-Mixer-CIFAR10

This repository implements MLP-Mixer as proposed in MLP-Mixer: An all-MLP Architecture for Vision. The paper introduces an all MLP (Multi-layer Perceptron) architecture for computer vision tasks. Yannic Kilcher walks through the architecture in this video.

Experiments reported in this repository are on CIFAR-10.

What's included?

  • Distributed training with mixed-precision.
  • Visualization of the token-mixing MLP weights.
  • A TensorBoard callback to keep track of the learned linear projections of the image patches.
Screen.Recording.2021-05-25.at.5.49.20.PM.mov

Notebooks

Note: These notebooks are runnable on Colab. If you don't have access to a tensor-core GPU, please disable the mixed-precision block while running the code.

Results

MLP-Mixer achieves competitive results. The figure below summarizes top-1 accuracies on CIFAR-10 test set with respect to varying MLP blocks.


Notable hyperparameters are:

  • Image size: 72x72
  • Patch size: 9x9
  • Hidden dimension for patches: 64
  • Hidden dimension for patches: 128

The table below reports the parameter counts for the different MLP-Mixer variants:


ResNet20 (0.571969 Million) achieves 78.14% under the exact same training configuration. Refer to this notebook for more details.

Models

You can reproduce the results reported above. The model files are available here.

Acknowledgements

ML-GDE Program for providing GCP credits.

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Comments
  • Could patches number != MLP token mixing dimension?

    Could patches number != MLP token mixing dimension?

    I try to change the model into B/16 MLP-Mixer. is this setting, the patch number ( sequence length) != MLP token mixing dimension. But the code will report an error when it implements "x = layers.Add()([x, token_mixing])" because the two operation numbers have different shapes. Take an example, B/16 Settings: image 3232, 2D hidden layer 768, PP= 16*16, token mixing mlp dimentsion= 384, channel mlp dimension = 3072. Thus patch number ( sequence length) = 4, table value shape= (4, 768) When the code runs x = layers.Add()([x, token_mixing]) in the token mixing layer. rx shape=[4, 768], token_mixing shape = [384, 768]

    It is strange why the MLP-Mixer paper could set different parameters "patch number ( sequence length) != MLP token mixing dimensio"

    opened by LouiValley 2
  • Why the accuracy drops after epoch 100/100 (accuracy drops from 91% to 71%)

    Why the accuracy drops after epoch 100/100 (accuracy drops from 91% to 71%)

    I trained the Network ( NUM_MIXER_LAYERS =4 )

    At epoch 100:

    Epoch 100/100

    1/44 [..............................] - ETA: 1s - loss: 0.2472 - accuracy: 0.9160 3/44 [=>............................] - ETA: 1s - loss: 0.2424 - accuracy: 0.9162 5/44 [==>...........................] - ETA: 1s - loss: 0.2431 - accuracy: 0.9155 7/44 [===>..........................] - ETA: 1s - loss: 0.2424 - accuracy: 0.9154 9/44 [=====>........................] - ETA: 1s - loss: 0.2419 - accuracy: 0.9155 11/44 [======>.......................] - ETA: 1s - loss: 0.2423 - accuracy: 0.9150 13/44 [=======>......................] - ETA: 1s - loss: 0.2426 - accuracy: 0.9145 15/44 [=========>....................] - ETA: 1s - loss: 0.2430 - accuracy: 0.9142 17/44 [==========>...................] - ETA: 1s - loss: 0.2433 - accuracy: 0.9140 19/44 [===========>..................] - ETA: 1s - loss: 0.2435 - accuracy: 0.9138 21/44 [=============>................] - ETA: 0s - loss: 0.2438 - accuracy: 0.9136 23/44 [==============>...............] - ETA: 0s - loss: 0.2439 - accuracy: 0.9135 25/44 [================>.............] - ETA: 0s - loss: 0.2440 - accuracy: 0.9134 27/44 [=================>............] - ETA: 0s - loss: 0.2440 - accuracy: 0.9133 29/44 [==================>...........] - ETA: 0s - loss: 0.2442 - accuracy: 0.9132 31/44 [====================>.........] - ETA: 0s - loss: 0.2445 - accuracy: 0.9130 33/44 [=====================>........] - ETA: 0s - loss: 0.2447 - accuracy: 0.9129 35/44 [======================>.......] - ETA: 0s - loss: 0.2450 - accuracy: 0.9127 37/44 [========================>.....] - ETA: 0s - loss: 0.2454 - accuracy: 0.9125 39/44 [=========================>....] - ETA: 0s - loss: 0.2459 - accuracy: 0.9123 41/44 [==========================>...] - ETA: 0s - loss: 0.2463 - accuracy: 0.9121 43/44 [============================>.] - ETA: 0s - loss: 0.2469 - accuracy: 0.9119 44/44 [==============================] - 2s 46ms/step - loss: 0.2474 - accuracy: 0.9117 - val_loss: 1.1145 - val_accuracy: 0.7226

    Then it still have an extra training, 1/313 [..............................] - ETA: 24:32 - loss: 0.5860 - accuracy: 0.8125 8/313 [..............................] - ETA: 2s - loss: 1.2071 - accuracy: 0.6953  ..... 313/313 [==============================] - ETA: 0s - loss: 1.0934 - accuracy: 0.7161 313/313 [==============================] - 12s 22ms/step - loss: 1.0934 - accuracy: 0.7161 Test accuracy: 71.61

    opened by LouiValley 1
  • Consider either turning off auto-sharding or switching the auto_shard_policy to DATA

    Consider either turning off auto-sharding or switching the auto_shard_policy to DATA

    Excuse me, when I try to run it on the serve, it tips:

    Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new tf.data.Options() object then setting options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA before applying the options object to the dataset via dataset.with_options(options). 2021-11-21 11:59:20.861052: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.

    BTW, my TensorFlow version is 2.4.0, how to fix this problem?

    opened by LouiValley 1
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