Picture Classification on Small Datasets with Keras

Coaching a convnet with a small dataset

Having to coach an image-classification mannequin utilizing little or no information is a typical scenario, which you’ll probably encounter in observe should you ever do pc imaginative and prescient in an expert context. A “few” samples can imply wherever from just a few hundred to some tens of 1000’s of photos. As a sensible instance, we’ll deal with classifying photos as canine or cats, in a dataset containing 4,000 footage of cats and canine (2,000 cats, 2,000 canine). We’ll use 2,000 footage for coaching – 1,000 for validation, and 1,000 for testing.

In Chapter 5 of the Deep Studying with R guide we overview three strategies for tackling this drawback. The primary of those is coaching a small mannequin from scratch on what little information you’ve got (which achieves an accuracy of 82%). Subsequently we use characteristic extraction with a pretrained community (leading to an accuracy of 90%) and fine-tuning a pretrained community (with a last accuracy of 97%). On this submit we’ll cowl solely the second and third strategies.

The relevance of deep studying for small-data issues

You’ll typically hear that deep studying solely works when a lot of information is obtainable. That is legitimate partially: one elementary attribute of deep studying is that it will possibly discover attention-grabbing options within the coaching information by itself, with none want for guide characteristic engineering, and this may solely be achieved when a lot of coaching examples can be found. That is very true for issues the place the enter samples are very high-dimensional, like photos.

However what constitutes a lot of samples is relative – relative to the dimensions and depth of the community you’re attempting to coach, for starters. It isn’t attainable to coach a convnet to resolve a fancy drawback with only a few tens of samples, however just a few hundred can probably suffice if the mannequin is small and properly regularized and the duty is easy. As a result of convnets be taught native, translation-invariant options, they’re extremely information environment friendly on perceptual issues. Coaching a convnet from scratch on a really small picture dataset will nonetheless yield cheap outcomes regardless of a relative lack of information, with out the necessity for any customized characteristic engineering. You’ll see this in motion on this part.

What’s extra, deep-learning fashions are by nature extremely repurposable: you may take, say, an image-classification or speech-to-text mannequin educated on a large-scale dataset and reuse it on a considerably totally different drawback with solely minor modifications. Particularly, within the case of pc imaginative and prescient, many pretrained fashions (normally educated on the ImageNet dataset) at the moment are publicly accessible for obtain and can be utilized to bootstrap highly effective imaginative and prescient fashions out of little or no information. That’s what you’ll do within the subsequent part. Let’s begin by getting your palms on the info.

Downloading the info

The Canines vs. Cats dataset that you simply’ll use isn’t packaged with Keras. It was made accessible by Kaggle as a part of a computer-vision competitors in late 2013, again when convnets weren’t mainstream. You may obtain the unique dataset from https://www.kaggle.com/c/dogs-vs-cats/information (you’ll must create a Kaggle account should you don’t have already got one – don’t fear, the method is painless).

The photographs are medium-resolution shade JPEGs. Listed here are some examples:

Unsurprisingly, the dogs-versus-cats Kaggle competitors in 2013 was received by entrants who used convnets. The very best entries achieved as much as 95% accuracy. Beneath you’ll find yourself with a 97% accuracy, regardless that you’ll prepare your fashions on lower than 10% of the info that was accessible to the opponents.

This dataset comprises 25,000 photos of canine and cats (12,500 from every class) and is 543 MB (compressed). After downloading and uncompressing it, you’ll create a brand new dataset containing three subsets: a coaching set with 1,000 samples of every class, a validation set with 500 samples of every class, and a check set with 500 samples of every class.

Following is the code to do that:

original_dataset_dir <- "~/Downloads/kaggle_original_data"

base_dir <- "~/Downloads/cats_and_dogs_small"
dir.create(base_dir)

train_dir <- file.path(base_dir, "prepare")
dir.create(train_dir)
validation_dir <- file.path(base_dir, "validation")
dir.create(validation_dir)
test_dir <- file.path(base_dir, "check")
dir.create(test_dir)

train_cats_dir <- file.path(train_dir, "cats")
dir.create(train_cats_dir)

train_dogs_dir <- file.path(train_dir, "canine")
dir.create(train_dogs_dir)

validation_cats_dir <- file.path(validation_dir, "cats")
dir.create(validation_cats_dir)

validation_dogs_dir <- file.path(validation_dir, "canine")
dir.create(validation_dogs_dir)

test_cats_dir <- file.path(test_dir, "cats")
dir.create(test_cats_dir)

test_dogs_dir <- file.path(test_dir, "canine")
dir.create(test_dogs_dir)

fnames <- paste0("cat.", 1:1000, ".jpg")
file.copy(file.path(original_dataset_dir, fnames), 
          file.path(train_cats_dir)) 

fnames <- paste0("cat.", 1001:1500, ".jpg")
file.copy(file.path(original_dataset_dir, fnames), 
          file.path(validation_cats_dir))

fnames <- paste0("cat.", 1501:2000, ".jpg")
file.copy(file.path(original_dataset_dir, fnames),
          file.path(test_cats_dir))

fnames <- paste0("canine.", 1:1000, ".jpg")
file.copy(file.path(original_dataset_dir, fnames),
          file.path(train_dogs_dir))

fnames <- paste0("canine.", 1001:1500, ".jpg")
file.copy(file.path(original_dataset_dir, fnames),
          file.path(validation_dogs_dir)) 

fnames <- paste0("canine.", 1501:2000, ".jpg")
file.copy(file.path(original_dataset_dir, fnames),
          file.path(test_dogs_dir))

Utilizing a pretrained convnet

A typical and extremely efficient method to deep studying on small picture datasets is to make use of a pretrained community. A pretrained community is a saved community that was beforehand educated on a big dataset, usually on a large-scale image-classification process. If this authentic dataset is giant sufficient and common sufficient, then the spatial hierarchy of options realized by the pretrained community can successfully act as a generic mannequin of the visible world, and therefore its options can show helpful for a lot of totally different computer-vision issues, regardless that these new issues might contain fully totally different lessons than these of the unique process. As an illustration, you may prepare a community on ImageNet (the place lessons are principally animals and on a regular basis objects) after which repurpose this educated community for one thing as distant as figuring out furnishings gadgets in photos. Such portability of realized options throughout totally different issues is a key benefit of deep studying in comparison with many older, shallow-learning approaches, and it makes deep studying very efficient for small-data issues.

On this case, let’s think about a big convnet educated on the ImageNet dataset (1.4 million labeled photos and 1,000 totally different lessons). ImageNet comprises many animal lessons, together with totally different species of cats and canine, and you may thus anticipate to carry out properly on the dogs-versus-cats classification drawback.

You’ll use the VGG16 structure, developed by Karen Simonyan and Andrew Zisserman in 2014; it’s a easy and extensively used convnet structure for ImageNet. Though it’s an older mannequin, removed from the present cutting-edge and considerably heavier than many different latest fashions, I selected it as a result of its structure is much like what you’re already accustomed to and is simple to know with out introducing any new ideas. This can be your first encounter with one in every of these cutesy mannequin names – VGG, ResNet, Inception, Inception-ResNet, Xception, and so forth; you’ll get used to them, as a result of they are going to come up regularly should you hold doing deep studying for pc imaginative and prescient.

There are two methods to make use of a pretrained community: characteristic extraction and fine-tuning. We’ll cowl each of them. Let’s begin with characteristic extraction.

Characteristic extraction consists of utilizing the representations realized by a earlier community to extract attention-grabbing options from new samples. These options are then run via a brand new classifier, which is educated from scratch.

As you noticed beforehand, convnets used for picture classification comprise two elements: they begin with a sequence of pooling and convolution layers, they usually finish with a densely linked classifier. The primary half known as the convolutional base of the mannequin. Within the case of convnets, characteristic extraction consists of taking the convolutional base of a beforehand educated community, working the brand new information via it, and coaching a brand new classifier on high of the output.

Why solely reuse the convolutional base? Might you reuse the densely linked classifier as properly? Usually, doing so ought to be prevented. The reason being that the representations realized by the convolutional base are more likely to be extra generic and subsequently extra reusable: the characteristic maps of a convnet are presence maps of generic ideas over an image, which is more likely to be helpful whatever the computer-vision drawback at hand. However the representations realized by the classifier will essentially be particular to the set of lessons on which the mannequin was educated – they are going to solely include details about the presence chance of this or that class in all the image. Moreover, representations present in densely linked layers not include any details about the place objects are situated within the enter picture: these layers do away with the notion of area, whereas the thing location continues to be described by convolutional characteristic maps. For issues the place object location issues, densely linked options are largely ineffective.

Word that the extent of generality (and subsequently reusability) of the representations extracted by particular convolution layers is dependent upon the depth of the layer within the mannequin. Layers that come earlier within the mannequin extract native, extremely generic characteristic maps (equivalent to visible edges, colours, and textures), whereas layers which might be larger up extract more-abstract ideas (equivalent to “cat ear” or “canine eye”). So in case your new dataset differs lots from the dataset on which the unique mannequin was educated, it’s possible you’ll be higher off utilizing solely the primary few layers of the mannequin to do characteristic extraction, reasonably than utilizing all the convolutional base.

On this case, as a result of the ImageNet class set comprises a number of canine and cat lessons, it’s more likely to be helpful to reuse the knowledge contained within the densely linked layers of the unique mannequin. However we’ll select to not, with a purpose to cowl the extra common case the place the category set of the brand new drawback doesn’t overlap the category set of the unique mannequin.

Let’s put this in observe through the use of the convolutional base of the VGG16 community, educated on ImageNet, to extract attention-grabbing options from cat and canine photos, after which prepare a dogs-versus-cats classifier on high of those options.

The VGG16 mannequin, amongst others, comes prepackaged with Keras. Right here’s the checklist of image-classification fashions (all pretrained on the ImageNet dataset) which might be accessible as a part of Keras:

• Xception
• Inception V3
• ResNet50
• VGG16
• VGG19
• MobileNet

Let’s instantiate the VGG16 mannequin.

library(keras)

conv_base <- application_vgg16(
  weights = "imagenet",
  include_top = FALSE,
  input_shape = c(150, 150, 3)
)

You move three arguments to the operate:

• weights specifies the load checkpoint from which to initialize the mannequin.
• include_top refers to together with (or not) the densely linked classifier on high of the community. By default, this densely linked classifier corresponds to the 1,000 lessons from ImageNet. Since you intend to make use of your personal densely linked classifier (with solely two lessons: cat and canine), you don’t want to incorporate it.
• input_shape is the form of the picture tensors that you simply’ll feed to the community. This argument is solely optionally available: should you don’t move it, the community will be capable of course of inputs of any measurement.

Right here’s the element of the structure of the VGG16 convolutional base. It’s much like the easy convnets you’re already accustomed to:

Layer (sort)                     Output Form          Param #  
================================================================
input_1 (InputLayer)             (None, 150, 150, 3)   0       
________________________________________________________________
block1_conv1 (Convolution2D)     (None, 150, 150, 64)  1792     
________________________________________________________________
block1_conv2 (Convolution2D)     (None, 150, 150, 64)  36928    
________________________________________________________________
block1_pool (MaxPooling2D)       (None, 75, 75, 64)    0        
________________________________________________________________
block2_conv1 (Convolution2D)     (None, 75, 75, 128)   73856    
________________________________________________________________
block2_conv2 (Convolution2D)     (None, 75, 75, 128)   147584   
________________________________________________________________
block2_pool (MaxPooling2D)       (None, 37, 37, 128)   0        
________________________________________________________________
block3_conv1 (Convolution2D)     (None, 37, 37, 256)   295168   
________________________________________________________________
block3_conv2 (Convolution2D)     (None, 37, 37, 256)   590080   
________________________________________________________________
block3_conv3 (Convolution2D)     (None, 37, 37, 256)   590080   
________________________________________________________________
block3_pool (MaxPooling2D)       (None, 18, 18, 256)   0        
________________________________________________________________
block4_conv1 (Convolution2D)     (None, 18, 18, 512)   1180160  
________________________________________________________________
block4_conv2 (Convolution2D)     (None, 18, 18, 512)   2359808  
________________________________________________________________
block4_conv3 (Convolution2D)     (None, 18, 18, 512)   2359808  
________________________________________________________________
block4_pool (MaxPooling2D)       (None, 9, 9, 512)     0        
________________________________________________________________
block5_conv1 (Convolution2D)     (None, 9, 9, 512)     2359808  
________________________________________________________________
block5_conv2 (Convolution2D)     (None, 9, 9, 512)     2359808  
________________________________________________________________
block5_conv3 (Convolution2D)     (None, 9, 9, 512)     2359808  
________________________________________________________________
block5_pool (MaxPooling2D)       (None, 4, 4, 512)     0        
================================================================
Whole params: 14,714,688
Trainable params: 14,714,688
Non-trainable params: 0

The ultimate characteristic map has form (4, 4, 512). That’s the characteristic on high of which you’ll stick a densely linked classifier.

At this level, there are two methods you could possibly proceed:

• Working the convolutional base over your dataset, recording its output to an array on disk, after which utilizing this information as enter to a standalone, densely linked classifier much like these you noticed partially 1 of this guide. This answer is quick and low-cost to run, as a result of it solely requires working the convolutional base as soon as for each enter picture, and the convolutional base is by far the most costly a part of the pipeline. However for a similar purpose, this method received’t help you use information augmentation.

• Extending the mannequin you’ve got (conv_base) by including dense layers on high, and working the entire thing finish to finish on the enter information. This can help you use information augmentation, as a result of each enter picture goes via the convolutional base each time it’s seen by the mannequin. However for a similar purpose, this method is much dearer than the primary.

On this submit we’ll cowl the second method intimately (within the guide we cowl each). Word that this method is so costly that it is best to solely try it when you’ve got entry to a GPU – it’s completely intractable on a CPU.

As a result of fashions behave identical to layers, you may add a mannequin (like conv_base) to a sequential mannequin identical to you’d add a layer.

mannequin <- keras_model_sequential() %>% 
  conv_base %>% 
  layer_flatten() %>% 
  layer_dense(models = 256, activation = "relu") %>% 
  layer_dense(models = 1, activation = "sigmoid")

That is what the mannequin appears like now:

Layer (sort)                     Output Form          Param #  
================================================================
vgg16 (Mannequin)                    (None, 4, 4, 512)     14714688                                     
________________________________________________________________
flatten_1 (Flatten)              (None, 8192)          0        
________________________________________________________________
dense_1 (Dense)                  (None, 256)           2097408  
________________________________________________________________
dense_2 (Dense)                  (None, 1)             257      
================================================================
Whole params: 16,812,353
Trainable params: 16,812,353
Non-trainable params: 0

As you may see, the convolutional base of VGG16 has 14,714,688 parameters, which may be very giant. The classifier you’re including on high has 2 million parameters.

Earlier than you compile and prepare the mannequin, it’s crucial to freeze the convolutional base. Freezing a layer or set of layers means stopping their weights from being up to date throughout coaching. For those who don’t do that, then the representations that have been beforehand realized by the convolutional base shall be modified throughout coaching. As a result of the dense layers on high are randomly initialized, very giant weight updates can be propagated via the community, successfully destroying the representations beforehand realized.

In Keras, you freeze a community utilizing the freeze_weights() operate:

size(mannequin$trainable_weights)

[1] 30

freeze_weights(conv_base)
size(mannequin$trainable_weights)

[1] 4

With this setup, solely the weights from the 2 dense layers that you simply added shall be educated. That’s a complete of 4 weight tensors: two per layer (the primary weight matrix and the bias vector). Word that to ensure that these modifications to take impact, you will need to first compile the mannequin. For those who ever modify weight trainability after compilation, it is best to then recompile the mannequin, or these modifications shall be ignored.

Utilizing information augmentation

Overfitting is brought on by having too few samples to be taught from, rendering you unable to coach a mannequin that may generalize to new information. Given infinite information, your mannequin can be uncovered to each attainable facet of the info distribution at hand: you’d by no means overfit. Information augmentation takes the method of producing extra coaching information from present coaching samples, by augmenting the samples by way of a variety of random transformations that yield believable-looking photos. The purpose is that at coaching time, your mannequin won’t ever see the very same image twice. This helps expose the mannequin to extra facets of the info and generalize higher.

In Keras, this may be performed by configuring a variety of random transformations to be carried out on the pictures learn by an image_data_generator(). For instance:

train_datagen = image_data_generator(
  rescale = 1/255,
  rotation_range = 40,
  width_shift_range = 0.2,
  height_shift_range = 0.2,
  shear_range = 0.2,
  zoom_range = 0.2,
  horizontal_flip = TRUE,
  fill_mode = "nearest"
)

These are only a few of the choices accessible (for extra, see the Keras documentation). Let’s shortly go over this code:

• rotation_range is a price in levels (0–180), a variety inside which to randomly rotate footage.
• width_shift and height_shift are ranges (as a fraction of whole width or top) inside which to randomly translate footage vertically or horizontally.
• shear_range is for randomly making use of shearing transformations.
• zoom_range is for randomly zooming inside footage.
• horizontal_flip is for randomly flipping half the pictures horizontally – related when there are not any assumptions of horizontal asymmetry (for instance, real-world footage).
• fill_mode is the technique used for filling in newly created pixels, which might seem after a rotation or a width/top shift.

Now we will prepare our mannequin utilizing the picture information generator:

# Word that the validation information should not be augmented!
test_datagen <- image_data_generator(rescale = 1/255)  

train_generator <- flow_images_from_directory(
  train_dir,                  # Goal listing  
  train_datagen,              # Information generator
  target_size = c(150, 150),  # Resizes all photos to 150 × 150
  batch_size = 20,
  class_mode = "binary"       # binary_crossentropy loss for binary labels
)

validation_generator <- flow_images_from_directory(
  validation_dir,
  test_datagen,
  target_size = c(150, 150),
  batch_size = 20,
  class_mode = "binary"
)

mannequin %>% compile(
  loss = "binary_crossentropy",
  optimizer = optimizer_rmsprop(lr = 2e-5),
  metrics = c("accuracy")
)

historical past <- mannequin %>% fit_generator(
  train_generator,
  steps_per_epoch = 100,
  epochs = 30,
  validation_data = validation_generator,
  validation_steps = 50
)

Let’s plot the outcomes. As you may see, you attain a validation accuracy of about 90%.

Tremendous-tuning

One other extensively used method for mannequin reuse, complementary to characteristic extraction, is fine-tuning
Tremendous-tuning consists of unfreezing just a few of the highest layers of a frozen mannequin base used for characteristic extraction, and collectively coaching each the newly added a part of the mannequin (on this case, the totally linked classifier) and these high layers. That is referred to as fine-tuning as a result of it barely adjusts the extra summary
representations of the mannequin being reused, with a purpose to make them extra related for the issue at hand.

I said earlier that it’s essential to freeze the convolution base of VGG16 so as to have the ability to prepare a randomly initialized classifier on high. For a similar purpose, it’s solely attainable to fine-tune the highest layers of the convolutional base as soon as the classifier on high has already been educated. If the classifier isn’t already educated, then the error sign propagating via the community throughout coaching shall be too giant, and the representations beforehand realized by the layers being fine-tuned shall be destroyed. Thus the steps for fine-tuning a community are as follows:

• Add your customized community on high of an already-trained base community.
• Freeze the bottom community.
• Prepare the half you added.
• Unfreeze some layers within the base community.
• Collectively prepare each these layers and the half you added.

You already accomplished the primary three steps when doing characteristic extraction. Let’s proceed with step 4: you’ll unfreeze your conv_base after which freeze particular person layers inside it.

As a reminder, that is what your convolutional base appears like:

Layer (sort)                     Output Form          Param #  
================================================================
input_1 (InputLayer)             (None, 150, 150, 3)   0        
________________________________________________________________
block1_conv1 (Convolution2D)     (None, 150, 150, 64)  1792     
________________________________________________________________
block1_conv2 (Convolution2D)     (None, 150, 150, 64)  36928    
________________________________________________________________
block1_pool (MaxPooling2D)       (None, 75, 75, 64)    0        
________________________________________________________________
block2_conv1 (Convolution2D)     (None, 75, 75, 128)   73856    
________________________________________________________________
block2_conv2 (Convolution2D)     (None, 75, 75, 128)   147584   
________________________________________________________________
block2_pool (MaxPooling2D)       (None, 37, 37, 128)   0        
________________________________________________________________
block3_conv1 (Convolution2D)     (None, 37, 37, 256)   295168   
________________________________________________________________
block3_conv2 (Convolution2D)     (None, 37, 37, 256)   590080   
________________________________________________________________
block3_conv3 (Convolution2D)     (None, 37, 37, 256)   590080   
________________________________________________________________
block3_pool (MaxPooling2D)       (None, 18, 18, 256)   0        
________________________________________________________________
block4_conv1 (Convolution2D)     (None, 18, 18, 512)   1180160  
________________________________________________________________
block4_conv2 (Convolution2D)     (None, 18, 18, 512)   2359808  
________________________________________________________________
block4_conv3 (Convolution2D)     (None, 18, 18, 512)   2359808  
________________________________________________________________
block4_pool (MaxPooling2D)       (None, 9, 9, 512)     0        
________________________________________________________________
block5_conv1 (Convolution2D)     (None, 9, 9, 512)     2359808  
________________________________________________________________
block5_conv2 (Convolution2D)     (None, 9, 9, 512)     2359808  
________________________________________________________________
block5_conv3 (Convolution2D)     (None, 9, 9, 512)     2359808  
________________________________________________________________
block5_pool (MaxPooling2D)       (None, 4, 4, 512)     0        
================================================================
Whole params: 14714688

You’ll fine-tune the entire layers from block3_conv1 and on. Why not fine-tune all the convolutional base? You could possibly. However you’ll want to think about the next:

• Earlier layers within the convolutional base encode more-generic, reusable options, whereas layers larger up encode more-specialized options. It’s extra helpful to fine-tune the extra specialised options, as a result of these are those that should be repurposed in your new drawback. There can be fast-decreasing returns in fine-tuning decrease layers.
• The extra parameters you’re coaching, the extra you’re prone to overfitting. The convolutional base has 15 million parameters, so it might be dangerous to aim to coach it in your small dataset.

Thus, on this scenario, it’s technique to fine-tune solely among the layers within the convolutional base. Let’s set this up, ranging from the place you left off within the earlier instance.

unfreeze_weights(conv_base, from = "block3_conv1")

Now you may start fine-tuning the community. You’ll do that with the RMSProp optimizer, utilizing a really low studying fee. The explanation for utilizing a low studying fee is that you simply need to restrict the magnitude of the modifications you make to the representations of the three layers you’re fine-tuning. Updates which might be too giant might hurt these representations.

mannequin %>% compile(
  loss = "binary_crossentropy",
  optimizer = optimizer_rmsprop(lr = 1e-5),
  metrics = c("accuracy")
)

historical past <- mannequin %>% fit_generator(
  train_generator,
  steps_per_epoch = 100,
  epochs = 100,
  validation_data = validation_generator,
  validation_steps = 50
)

Let’s plot our outcomes:

You’re seeing a pleasant 6% absolute enchancment in accuracy, from about 90% to above 96%.

Word that the loss curve doesn’t present any actual enchancment (in truth, it’s deteriorating). It’s possible you’ll marvel, how might accuracy keep steady or enhance if the loss isn’t lowering? The reply is easy: what you show is a median of pointwise loss values; however what issues for accuracy is the distribution of the loss values, not their common, as a result of accuracy is the results of a binary thresholding of the category chance predicted by the mannequin. The mannequin should still be bettering even when this isn’t mirrored within the common loss.

Now you can lastly consider this mannequin on the check information:

test_generator <- flow_images_from_directory(
  test_dir,
  test_datagen,
  target_size = c(150, 150),
  batch_size = 20,
  class_mode = "binary"
)

mannequin %>% evaluate_generator(test_generator, steps = 50)

$loss
[1] 0.2158171

$acc
[1] 0.965

Right here you get a check accuracy of 96.5%. Within the authentic Kaggle competitors round this dataset, this is able to have been one of many high outcomes. However utilizing fashionable deep-learning strategies, you managed to achieve this consequence utilizing solely a small fraction of the coaching information accessible (about 10%). There’s a enormous distinction between with the ability to prepare on 20,000 samples in comparison with 2,000 samples!

Take-aways: utilizing convnets with small datasets

Right here’s what it is best to take away from the workouts previously two sections:

• Convnets are the very best sort of machine-learning fashions for computer-vision duties. It’s attainable to coach one from scratch even on a really small dataset, with respectable outcomes.
• On a small dataset, overfitting would be the principal situation. Information augmentation is a strong approach to combat overfitting while you’re working with picture information.
• It’s simple to reuse an present convnet on a brand new dataset by way of characteristic extraction. It is a beneficial method for working with small picture datasets.
• As a complement to characteristic extraction, you need to use fine-tuning, which adapts to a brand new drawback among the representations beforehand realized by an present mannequin. This pushes efficiency a bit additional.

Now you’ve got a strong set of instruments for coping with image-classification issues – specifically with small datasets.