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---
title: vision
keywords: fastai
sidebar: home_sidebar
summary: "Application to Computer Vision"
---
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<h1 id="Computer-vision">Computer vision<a class="anchor-link" href="#Computer-vision">¶</a></h1>
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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#ImageClassificationDataset"><code>ImageClassificationDataset</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>
<span class="kn">from</span> <span class="nn">fastai</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/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">create_cnn</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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<pre>Total time: 00:09
epoch train loss valid loss accuracy
0 0.090708 0.041684 0.983808 (00:09)
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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#ImageClassificationDataset"><code>ImageClassificationDataset</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">ds</span> <span class="o">=</span> <span class="n">ImageClassificationDataset</span><span class="o">.</span><span class="n">from_folder</span><span class="p">(</span><span class="n">path</span><span class="o">/</span><span class="s1">'train'</span><span class="p">)</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,iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAYAAAByDd+UAAAABHNCSVQICAgIfAhkiAAAAmRJREFUSIntlr1rImEQxh/Pi6RSwiooIUtsJGgvBKs0iSKYaiWFRUCwtDeV/0KaNDYLaZQ0ISmChVEUIQS0EUOKRFijIopYKMawvMw1l+W89WOJxuK4B6YZZue38/HuuzoAhDXqxzph/4Hfop9agg4ODpDNZmG32xEKhRQ/x3GIRqMTsXd3d/D7/XPz0TyrVCokyzKNx2P6+PggxthCi8ViM/MtrDAcDuPk5AS7u7s4Pj5Gq9XC1dUVACCVSqFarcJms+H5+RkAwBhDqVT6eoWfZjAYiOM4MplME35BEKjX6xFjjMbjMQUCgUW5tAH/NIvFQuFwmNLpNA2HQ6WV9/f3Wp7XDrJarXR2dkaNRmNiZsVikQRBIKPRuDxwZ2eHBEGgTCZDkiSpFkSSJFWblwJ6PB7q9/szN/L9/Z0KhQL5fD5NQD2A+LyNent7A8/z2NrawuPjI15eXibMaDTC6XQiGAxCp9Mhn8/PSwfNM+R5fqrf4XDQ+fk5McZoMBjMjNPcUi1msVio2WwSY4yenp6+HwiALi4ulJnOi1vJx3tjYwMcx2mOX6oyl8tFNzc3ytbW6/XVt9RqtZLdbqdYLEavr68KrNvtUiQSWQ1wc3OTtre3aX9/n+r1uuo8NhoNcjqdWnLNh5jNZorH43R7ezv14MuyTJ1Oh05PTzW9uHI9HR0dwev1Tgx3b28Ph4eHqqEzxjAajZBMJvHw8ABRFNWbMUMK0O12q27vT8myDCJCrVaDKIpot9u4vLzUDJkKzOVyYIxBr9cDAMrlMq6vrwEAiUQCnU7nS4C/pcPvQa5L//5v4i9SPK4oLgXfwgAAAABJRU5ErkJggg==
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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:float=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 to an existing dataset:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tds</span> <span class="o">=</span> <span class="n">DatasetTfm</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">tfms</span><span class="p">)</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">tds</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">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#create_cnn"><code>create_cnn</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">create_cnn</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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<pre>Total time: 00:05
epoch train loss valid loss accuracy
0 0.129527 0.053491 0.985280 (00:05)
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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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