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+.. image:: https://github.com/unifyai/unifyai.github.io/blob/main/img/externally_linked/logo.png?raw=true#gh-light-mode-only
+ :width: 100%
+ :class: only-light
+
+.. image:: https://github.com/unifyai/unifyai.github.io/blob/main/img/externally_linked/logo_dark.png?raw=true#gh-dark-mode-only
+ :width: 100%
+ :class: only-dark
+
+
+.. raw:: html
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+
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+ 
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+ 
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+ 
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+ 
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+
+AlexNet
+===========
+
+`AlexNet `_ competed in the ImageNet Large Scale Visual Recognition Challenge on September 30, 2012.
+
+The network achieved a top-5 error of 15.3%, more than 10.8 percentage points lower than that of the runner up.
+The original paper’s primary result was that the depth of the model was essential for its high performance, which was computationally expensive,
+but made feasible due to the utilization of graphics processing units (GPUs) during training.
+
+Getting started
+-----------------
+
+.. code-block:: python
+
+ import ivy
+ ivy.set_backend("torch")
+ from ivy_models.alexnet import alexnet
+
+ # Instantiate the AlexNet Model
+ ivy_alexnet = alexnet()
+
+ # Complile the model with the image preprocessed using torch
+ ivy_alexnet = ivy.compile(ivy_alexnet, args=(ivy.asarray(torch_img.cuda()),))
+
+ # Pass the processed image to the model
+ output = ivy.softmax(ivy_alexnet(ivy.asarray(img)))
+ classes = ivy.argsort(output[0], descending=True)[:3] # get the top 3 classes
+ logits = ivy.gather(output[0], classes) # get the logits
+
+ print("Indices of the top 3 classes are:", classes)
+ print("Logits of the top 3 classes are:", logits)
+ print("Categories of the top 3 classes are:", [categories[i] for i in classes.to_list()])
+
+
+ `Indices of the top 3 classes are: ivy.array([282, 281, 285], dev=gpu:0)`
+ `Logits of the top 3 classes are: ivy.array([0.64773697, 0.29496649, 0.04526037], dev=gpu:0)`
+ `Categories of the top 3 classes are: ['tiger cat', 'tabby', 'Egyptian cat']`
+
+
+The pretrained AlexNet model is now ready to be used, and is compatible with any Tensorflow, Jax and PyTorch code.
+See `this demo `_ for more usage example.
+
+Citation
+--------
+
+::
+
+ @article{
+ title={One weird trick for parallelizing convolutional neural networks},
+ author={Alex Krizhevsky},
+ journal={arXiv preprint arXiv:1404.5997},
+ year={2014}
+ }
+
+
+ @article{lenton2021ivy,
+ title={Ivy: Templated deep learning for inter-framework portability},
+ author={Lenton, Daniel and Pardo, Fabio and Falck, Fabian and James, Stephen and Clark, Ronald},
+ journal={arXiv preprint arXiv:2102.02886},
+ year={2021}
+ }