I tried AutoGluon's Image Classification
Introduction
I tried the image classification of AutoGluon (https://autogluon.mxnet.io/index.html) which is an AutoML library in the environment of Google Colaboratory. Basically, it will be the content that is added to the content of the official Quick Start.
environment
It is carried out at Google Colaboratory.
Run
Google Colab settings
For image classification, the Deep Learning model is used, so set it to use the GPU (see the figure below).
Library installation on Google Colab
With only the above settings, when you run AutoGluon, you will get an error if you don't have a GPU. Therefore, I installed it referring to this article (https://qiita.com/tasmas/items/22cf80a4be80f7ad458e). There were no particular errors and I was able to do it well.
!pip uninstall -y mkl
!pip install --upgrade mxnet-cu100
!pip install autogluon
!pip install -U ipykernel
Restart the runtime after execution.
Run Auto Gluon
Follow the official Quick Start. https://autogluon.mxnet.io/tutorials/image_classification/beginner.html
First, import the library and download the data. The data is the image data of Shopee-IET that was in Kaggle, and the images such as clothes are classified into four categories, "Baby Pants", "Baby Shirt", "women casual shoes", and "women chingiffon top". However, the link to Kaggle on the Auto Gluon page was broken. (Although Kaggle also has a page, I couldn't find the data at once. It may have been deleted because it was data from an old competition.)
import autogluon as ag
from autogluon import ImageClassification as task
filename = ag.download('https://autogluon.s3.amazonaws.com/datasets/shopee-iet.zip')
ag.unzip(filename)
Load the training data and the evaluation data respectively.
train_dataset = task.Dataset('data/train')
test_dataset = task.Dataset('data/test', train=False)
Simply "fit" the training data to build a model for image identification. Insanely easy ...
classifier = task.fit(train_dataset,
epochs=5,
ngpus_per_trial=1,
verbose=False)
The following is the standard output result at the time of fit. Apparently I'm bringing ResNet 50. Finally, it also gives you a learning curve. This time I was learning only 5 epochs, so it took only a few minutes.
scheduler_options: Key 'training_history_callback_delta_secs': Imputing default value 60
scheduler_options: Key 'delay_get_config': Imputing default value True
Starting Experiments
Num of Finished Tasks is 0
Num of Pending Tasks is 2
scheduler: FIFOScheduler(
DistributedResourceManager{
(Remote: Remote REMOTE_ID: 0,
<Remote: 'inproc://172.28.0.2/371/1' processes=1 threads=2, memory=13.65 GB>, Resource: NodeResourceManager(2 CPUs, 1 GPUs))
})
100%
2/2 [03:58<00:00, 119.19s/it]
Model file not found. Downloading.
Downloading /root/.mxnet/models/resnet50_v1b-0ecdba34.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet50_v1b-0ecdba34.zip...
100%|██████████| 55344/55344 [00:01<00:00, 45529.30KB/s]
[Epoch 5] Validation: 0.456: 100%
5/5 [01:06<00:00, 13.29s/it]
Saving Training Curve in checkpoint/plot_training_curves.png
The accuracy at the time of learning was about 50%. I think it can't be helped with the current settings.
print('Top-1 val acc: %.3f' % classifier.results['best_reward'])
# Top-1 val acc: 0.469
Let's make a prediction for a certain image data. If you predict the data of "Baby Shirt", it is certainly classified as "Baby Shirt".
image = 'data/test/BabyShirt/BabyShirt_323.jpg'
ind, prob, _ = classifier.predict(image, plot=True)
print('The input picture is classified as [%s], with probability %.2f.' %
(train_dataset.init().classes[ind.asscalar()], prob.asscalar()))
# The input picture is classified as [BabyShirt], with probability 0.61.
When calculating Accuracy using the evaluation data, it was about 70%.
test_acc = classifier.evaluate(test_dataset)
print('Top-1 test acc: %.3f' % test_acc)
# Top-1 test acc: 0.703
It's really convenient because you can build a model in a blink of an eye like this, while I felt that the fit function was really important, so I did some research.
A little research on fit
Arguments of fit function
This time, I used the fit function in image identification. The source code (https://github.com/awslabs/autogluon/blob/15c105b0f1d8bdbebc86bd7e7a3a1b71e83e82b9/autogluon/task/image_classification/image_classification.py#L63) is excerpted below.
@staticmethod
def fit(dataset,
net=Categorical('ResNet50_v1b', 'ResNet18_v1b'),
optimizer=NAG(
learning_rate=Real(1e-3, 1e-2, log=True),
wd=Real(1e-4, 1e-3, log=True),
multi_precision=False
),
loss=SoftmaxCrossEntropyLoss(),
split_ratio=0.8,
batch_size=64,
input_size=224,
epochs=20,
final_fit_epochs=None,
ensemble=1,
metric='accuracy',
nthreads_per_trial=60,
ngpus_per_trial=1,
hybridize=True,
scheduler_options=None,
search_strategy='random',
search_options=None,
plot_results=False,
verbose=False,
num_trials=None,
time_limits=None,
resume=False,
output_directory='checkpoint/',
visualizer='none',
dist_ip_addrs=None,
auto_search=True,
lr_config=Dict(
lr_mode='cosine',
lr_decay=0.1,
lr_decay_period=0,
lr_decay_epoch='40,80',
warmup_lr=0.0,
warmup_epochs=0
),
tricks=Dict(
last_gamma=False,
use_pretrained=True,
use_se=False,
mixup=False,
mixup_alpha=0.2,
mixup_off_epoch=0,
label_smoothing=False,
no_wd=False,
teacher_name=None,
temperature=20.0,
hard_weight=0.5,
batch_norm=False,
use_gn=False),
**kwargs):
Because ResNet50 was called by default in `` `net``` which is one of the arguments You can see that it was called earlier. So when you want to try another model, the question is what model you can choose.
About the model to call
Gluoncv model_zoo (https://gluon-cv.mxnet.io/model_zoo/classification.html) as described on the official page (https://autogluon.mxnet.io/tutorials/image_classification/hpo.html) ) Seems to be able to fetch the model. The registered models as of September 4, 2020 are as shown in the figure below.
In addition to ResNet mentioned above, we found that there are some trained models such as MobileNet and VGG. The models are already displayed in order of accuracy, so I think it's easy to choose.
About my own model
On the other hand, if you want to build your own Neural Network, it seems that you can make it using `` `mxnet``` which is used in the base of AutoGluon.
https://github.com/awslabs/autogluon/blob/15c105b0f1d8bdbebc86bd7e7a3a1b71e83e82b9/autogluon/task/image_classification/nets.py#L52
def mnist_net():
mnist_net = gluon.nn.Sequential()
mnist_net.add(ResUnit(1, 8, hidden_channels=8, kernel=3, stride=2))
mnist_net.add(ResUnit(8, 8, hidden_channels=8, kernel=5, stride=2))
mnist_net.add(ResUnit(8, 16, hidden_channels=8, kernel=3, stride=2))
mnist_net.add(nn.GlobalAvgPool2D())
mnist_net.add(nn.Flatten())
mnist_net.add(nn.Activation('relu'))
mnist_net.add(nn.Dense(10, in_units=16))
return mnist_net
Besides that, you can also set `metric` and optimiser, so I thought it would be easy to do deep learning.
at the end
In the case of image analysis, there was an image of building calculation logic using Tensorflow, keras, etc., but if it becomes possible to build a model so easily, it will be applied to image data processing (noise removal, Augmentation, etc.). I thought it would be very good because the time I could spend would increase relatively.
Since AutoGluon is based on Deep Learning's library (or platform) mxnet, I'm convinced that AutoGluon also supports natural language processing (NLP). Rather, it seems that Auto Gluon-Tabular is a plus alpha element.
Since AWS supports both AutoGluon and mxnet, I'd like to finish this while wondering if the libraries around here are used for AWS ML services.
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