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| <link rel="modulepreload" href="/docs/timm/pr_2213/en/_app/immutable/chunks/EditOnGithub.b65eee75.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"ResNeXt","local":"resnext","sections":[{"title":"How do I use this model on an image?","local":"how-do-i-use-this-model-on-an-image","sections":[],"depth":2},{"title":"How do I finetune this model?","local":"how-do-i-finetune-this-model","sections":[],"depth":2},{"title":"How do I train this model?","local":"how-do-i-train-this-model","sections":[],"depth":2},{"title":"Citation","local":"citation","sections":[],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="resnext" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#resnext"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>ResNeXt</span></h1> <p>A <strong data-svelte-h="svelte-ixiuf7">ResNeXt</strong> repeats a <a href="https://paperswithcode.com/method/resnext-block" rel="nofollow" data-svelte-h="svelte-60xv13">building block</a> that aggregates a set of transformations with the same topology. Compared to a <a href="https://paperswithcode.com/method/resnet" rel="nofollow" data-svelte-h="svelte-1fowv03">ResNet</a>, it exposes a new dimension, <em data-svelte-h="svelte-47b3di">cardinality</em> (the size of the set of transformations)<!-- HTML_TAG_START --><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>C</mi></mrow><annotation encoding="application/x-tex"> C </annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span></span></span></span><!-- HTML_TAG_END -->, as an essential factor in addition to the dimensions of depth and width.</p> <h2 class="relative group"><a id="how-do-i-use-this-model-on-an-image" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#how-do-i-use-this-model-on-an-image"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>How do I use this model on an image?</span></h2> <p data-svelte-h="svelte-18ywhxh">To load a pretrained model:</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> timm | |
| <span class="hljs-meta">>>> </span>model = timm.create_model(<span class="hljs-string">'resnext101_32x8d'</span>, pretrained=<span class="hljs-literal">True</span>) | |
| <span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">eval</span>()<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1c2ipa8">To load and preprocess the image:</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> urllib | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> timm.data <span class="hljs-keyword">import</span> resolve_data_config | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> timm.data.transforms_factory <span class="hljs-keyword">import</span> create_transform | |
| <span class="hljs-meta">>>> </span>config = resolve_data_config({}, model=model) | |
| <span class="hljs-meta">>>> </span>transform = create_transform(**config) | |
| <span class="hljs-meta">>>> </span>url, filename = (<span class="hljs-string">"https://github.com/pytorch/hub/raw/master/images/dog.jpg"</span>, <span class="hljs-string">"dog.jpg"</span>) | |
| <span class="hljs-meta">>>> </span>urllib.request.urlretrieve(url, filename) | |
| <span class="hljs-meta">>>> </span>img = Image.<span class="hljs-built_in">open</span>(filename).convert(<span class="hljs-string">'RGB'</span>) | |
| <span class="hljs-meta">>>> </span>tensor = transform(img).unsqueeze(<span class="hljs-number">0</span>) <span class="hljs-comment"># transform and add batch dimension</span><!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1n9qsq1">To get the model predictions:</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">with</span> torch.no_grad(): | |
| <span class="hljs-meta">... </span> out = model(tensor) | |
| <span class="hljs-meta">>>> </span>probabilities = torch.nn.functional.softmax(out[<span class="hljs-number">0</span>], dim=<span class="hljs-number">0</span>) | |
| <span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(probabilities.shape) | |
| <span class="hljs-meta">>>> </span><span class="hljs-comment"># prints: torch.Size([1000])</span><!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-19cnvx1">To get the top-5 predictions class names:</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-comment"># Get imagenet class mappings</span> | |
| <span class="hljs-meta">>>> </span>url, filename = (<span class="hljs-string">"https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt"</span>, <span class="hljs-string">"imagenet_classes.txt"</span>) | |
| <span class="hljs-meta">>>> </span>urllib.request.urlretrieve(url, filename) | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">with</span> <span class="hljs-built_in">open</span>(<span class="hljs-string">"imagenet_classes.txt"</span>, <span class="hljs-string">"r"</span>) <span class="hljs-keyword">as</span> f: | |
| <span class="hljs-meta">... </span> categories = [s.strip() <span class="hljs-keyword">for</span> s <span class="hljs-keyword">in</span> f.readlines()] | |
| <span class="hljs-meta">>>> </span><span class="hljs-comment"># Print top categories per image</span> | |
| <span class="hljs-meta">>>> </span>top5_prob, top5_catid = torch.topk(probabilities, <span class="hljs-number">5</span>) | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(top5_prob.size(<span class="hljs-number">0</span>)): | |
| <span class="hljs-meta">... </span> <span class="hljs-built_in">print</span>(categories[top5_catid[i]], top5_prob[i].item()) | |
| <span class="hljs-meta">>>> </span><span class="hljs-comment"># prints class names and probabilities like:</span> | |
| <span class="hljs-meta">>>> </span><span class="hljs-comment"># [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]</span><!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-j0haf3">Replace the model name with the variant you want to use, e.g. <code>resnext101_32x8d</code>. You can find the IDs in the model summaries at the top of this page.</p> <p data-svelte-h="svelte-1wmi3ea">To extract image features with this model, follow the <a href="../feature_extraction">timm feature extraction examples</a>, just change the name of the model you want to use.</p> <h2 class="relative group"><a id="how-do-i-finetune-this-model" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#how-do-i-finetune-this-model"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>How do I finetune this model?</span></h2> <p data-svelte-h="svelte-9sr7nh">You can finetune any of the pre-trained models just by changing the classifier (the last layer).</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model = timm.create_model(<span class="hljs-string">'resnext101_32x8d'</span>, pretrained=<span class="hljs-literal">True</span>, num_classes=NUM_FINETUNE_CLASSES)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1kguc51">To finetune on your own dataset, you have to write a training loop or adapt <a href="https://github.com/rwightman/pytorch-image-models/blob/master/train.py" rel="nofollow">timm’s training | |
| script</a> to use your dataset.</p> <h2 class="relative group"><a id="how-do-i-train-this-model" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#how-do-i-train-this-model"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>How do I train this model?</span></h2> <p data-svelte-h="svelte-1n0coha">You can follow the <a href="../scripts">timm recipe scripts</a> for training a new model afresh.</p> <h2 class="relative group"><a id="citation" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#citation"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Citation</span></h2> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="language-xml">@article</span><span class="hljs-template-variable">{DBLP:journals/corr/XieGDTH16, | |
| author = {Saining Xie and | |
| Ross B. Girshick and | |
| Piotr Doll{\'{a}</span><span class="language-xml">}r and | |
| Zhuowen Tu and | |
| Kaiming He}, | |
| title = </span><span class="hljs-template-variable">{Aggregated Residual Transformations for Deep Neural Networks}</span><span class="language-xml">, | |
| journal = </span><span class="hljs-template-variable">{CoRR}</span><span class="language-xml">, | |
| volume = </span><span class="hljs-template-variable">{abs/1611.05431}</span><span class="language-xml">, | |
| year = </span><span class="hljs-template-variable">{2016}</span><span class="language-xml">, | |
| url = </span><span class="hljs-template-variable">{http://arxiv.org/abs/1611.05431}</span><span class="language-xml">, | |
| archivePrefix = </span><span class="hljs-template-variable">{arXiv}</span><span class="language-xml">, | |
| eprint = </span><span class="hljs-template-variable">{1611.05431}</span><span class="language-xml">, | |
| timestamp = </span><span class="hljs-template-variable">{Mon, 13 Aug 2018 16:45:58 +0200}</span><span class="language-xml">, | |
| biburl = </span><span class="hljs-template-variable">{https://dblp.org/rec/journals/corr/XieGDTH16.bib}</span><span class="language-xml">, | |
| bibsource = </span><span class="hljs-template-variable">{dblp computer science bibliography, https://dblp.org}</span><span class="language-xml"> | |
| }</span><!-- HTML_TAG_END --></pre></div> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/pytorch-image-models/blob/main/hfdocs/source/models/resnext.mdx" target="_blank"><span data-svelte-h="svelte-1kd6by1"><</span> <span data-svelte-h="svelte-x0xyl0">></span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a> <p></p> | |
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