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---
title: vision
keywords: fastai
sidebar: home_sidebar
summary: "Application to Computer Vision"
---
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<h2 id="Computer-vision">Computer vision<a class="anchor-link" href="#Computer-vision">¶</a></h2>
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<p>The <a href="/vision.html#vision"><code>vision</code></a> module of the fastai library contains all the necessary functions to define a Dataset and train a model for computer vision tasks. It contains four different submodules to reach that goal:</p>
<ul>
<li><a href="/vision.image.html#vision.image"><code>vision.image</code></a> contains the basic definition of an <a href="/vision.image.html#Image"><code>Image</code></a> object and all the functions that are used behind the scenes to apply transformations to such an object.</li>
<li><a href="/vision.transform.html#vision.transform"><code>vision.transform</code></a> contains all the transforms we can use for data augmentation.</li>
<li><a href="/vision.data.html#vision.data"><code>vision.data</code></a> contains the definition of <a href="/vision.data.html#ImageDataBunch"><code>ImageDataBunch</code></a> as well as the utility function to easily build a <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a> for Computer Vision problems.</li>
<li><a href="/vision.learner.html#vision.learner"><code>vision.learner</code></a> lets you build and fine-tune models with a pretrained CNN backbone or train a randomly initialized model from scratch.</li>
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<p>Each of the four module links above includes a quick overview and examples of the functionality of that module, as well as complete API documentation. Below, we'll provide a walk-thru of end to end computer vision model training with the most commonly used functionality.</p>
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<h2 id="Minimal-training-example">Minimal training example<a class="anchor-link" href="#Minimal-training-example">¶</a></h2>
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<p>First, import everything you need from the fastai library.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">fastai.vision</span> <span class="k">import</span> <span class="o">*</span>
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<p>First, create a data folder containing a MNIST subset in <code>data/mnist_sample</code> using this little helper that will download it for you:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">path</span> <span class="o">=</span> <span class="n">untar_data</span><span class="p">(</span><span class="n">URLs</span><span class="o">.</span><span class="n">MNIST_SAMPLE</span><span class="p">)</span>
<span class="n">path</span>
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<pre>PosixPath('/home/ubuntu/.fastai/data/mnist_sample')</pre>
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<p>Since this contains standard <a href="/train.html#train"><code>train</code></a> and <code>valid</code> folders, and each contains one folder per class, you can create a <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a> in a single line:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">ImageDataBunch</span><span class="o">.</span><span class="n">from_folder</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
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<p>You load a pretrained model (from <a href="/vision.models.html#vision.models"><code>vision.models</code></a>) ready for fine tuning:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">cnn_learner</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="n">accuracy</span><span class="p">)</span>
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<p>And now you're ready to train!</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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Total time: 00:09 <p><table style='width:300px; margin-bottom:10px'>
<tr>
<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
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<th>1</th>
<th>0.140444</th>
<th>0.097685</th>
<th>0.968597</th>
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<p>Let's look briefly at each of the <a href="/vision.html#vision"><code>vision</code></a> submodules.</p>
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<h2 id="Getting-the-data">Getting the data<a class="anchor-link" href="#Getting-the-data">¶</a></h2>
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<p>The most important piece of <a href="/vision.data.html#vision.data"><code>vision.data</code></a> for classification is the <a href="/vision.data.html#ImageDataBunch"><code>ImageDataBunch</code></a>. If you've got labels as subfolders, then you can just say:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">ImageDataBunch</span><span class="o">.</span><span class="n">from_folder</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
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<p>It will grab the data in a train and validation sets from subfolders of classes. You can then access that training and validation set by grabbing the corresponding attribute in <a href="/vision.data.html#vision.data"><code>data</code></a>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ds</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">train_ds</span>
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<h2 id="Images">Images<a class="anchor-link" href="#Images">¶</a></h2>
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<p>That brings us to <a href="/vision.image.html#vision.image"><code>vision.image</code></a>, which defines the <a href="/vision.image.html#Image"><code>Image</code></a> class. Our dataset will return <a href="/vision.image.html#Image"><code>Image</code></a> objects when we index it. Images automatically display in notebooks:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">img</span><span class="p">,</span><span class="n">label</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">img</span>
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<img src="data:image/png;base64,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<p>You can change the way they're displayed:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">img</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">title</span><span class="o">=</span><span class="s1">'MNIST digit'</span><span class="p">)</span>
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<p>And you can transform them in various ways:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">img</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="mi">35</span><span class="p">)</span>
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<h2 id="Data-augmentation">Data augmentation<a class="anchor-link" href="#Data-augmentation">¶</a></h2>
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<p><a href="/vision.transform.html#vision.transform"><code>vision.transform</code></a> lets us do data augmentation. Simplest is to choose from a standard set of transforms, where the defaults are designed for photos:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">help</span><span class="p">(</span><span class="n">get_transforms</span><span class="p">)</span>
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<pre>Help on function get_transforms in module fastai.vision.transform:
get_transforms(do_flip: bool = True, flip_vert: bool = False, max_rotate: float = 10.0, max_zoom: float = 1.1, max_lighting: float = 0.2, max_warp: float = 0.2, p_affine: float = 0.75, p_lighting: float = 0.75, xtra_tfms: Union[Collection[fastai.vision.image.Transform], NoneType] = None) -> Collection[fastai.vision.image.Transform]
Utility func to easily create a list of flip, rotate, `zoom`, warp, lighting transforms.
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<p>...or create the exact list you want:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tfms</span> <span class="o">=</span> <span class="p">[</span><span class="n">rotate</span><span class="p">(</span><span class="n">degrees</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">20</span><span class="p">,</span><span class="mi">20</span><span class="p">)),</span> <span class="n">symmetric_warp</span><span class="p">(</span><span class="n">magnitude</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mf">0.3</span><span class="p">,</span><span class="mf">0.3</span><span class="p">))]</span>
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<p>You can apply these transforms to your images by using their <code>apply_tfms</code> method.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span><span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axes</span><span class="p">:</span> <span class="n">ds</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">apply_tfms</span><span class="p">(</span><span class="n">tfms</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
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<p>You can create a <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a> with your transformed training and validation data loaders in a single step, passing in a tuple of <em>(train_tfms, valid_tfms)</em>:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">ImageDataBunch</span><span class="o">.</span><span class="n">from_folder</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">ds_tfms</span><span class="o">=</span><span class="p">(</span><span class="n">tfms</span><span class="p">,</span> <span class="p">[]))</span>
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<h2 id="Training-and-interpretation">Training and interpretation<a class="anchor-link" href="#Training-and-interpretation">¶</a></h2>
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<p>Now you're ready to train a model. To create a model, simply pass your <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a> and a model creation function (such as one provided by <a href="/vision.models.html#vision.models"><code>vision.models</code></a> or <a href="https://pytorch.org/docs/stable/torchvision/models.html#torchvision-models"><code>torchvision.models</code></a>) to <a href="/vision.learner.html#cnn_learner"><code>cnn_learner</code></a>, and call <a href="/basic_train.html#fit"><code>fit</code></a>:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">cnn_learner</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="n">accuracy</span><span class="p">)</span>
<span class="n">learn</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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Total time: 00:08 <p><table style='width:300px; margin-bottom:10px'>
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<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
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<th>1</th>
<th>0.194779</th>
<th>0.131709</th>
<th>0.950932</th>
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<p>Now we can take a look at the most incorrect images, and also the classification matrix.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">interp</span> <span class="o">=</span> <span class="n">ClassificationInterpretation</span><span class="o">.</span><span class="n">from_learner</span><span class="p">(</span><span class="n">learn</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">interp</span><span class="o">.</span><span class="n">plot_top_losses</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">interp</span><span class="o">.</span><span class="n">plot_confusion_matrix</span><span class="p">()</span>
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<p>To simply predict the result of a new image (of type <a href="/vision.image.html#Image"><code>Image</code></a>, so opened with <a href="/vision.image.html#open_image"><code>open_image</code></a> for instance), just use <code>learn.predict</code>. It returns the class, its index and the probabilities of each class.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">img</span> <span class="o">=</span> <span class="n">learn</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">train_ds</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="n">learn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
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<pre>(Category 3, tensor(0), tensor([0.5551, 0.4449]))</pre>
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