diff --git a/ivy_models/efficientnet/README.rst b/ivy_models/efficientnet/README.rst
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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
+
+
+
+ 
+
+
+ 
+
+
+ 
+
+
+ 
+
+
+
+EfficientNet
+===========
+
+`EfficientNet `_ represents a convolutional neural network structure and scaling approach that consistently adjusts depth,
+width, and resolution using a combined coefficient. Diverging from traditional methods that unevenly modify these aspects,
+the EfficientNet approach maintains uniform adjustments to network dimensions by utilizing a predefined set of scaling coefficients.
+
+This method employs a compound coefficient to ensure consistent modifications in network attributes, resulting in a more systematic approach.
+The rationale behind the compound scaling method is intuitive: as the input image size increases,
+the network requires additional layers to enhance its receptive field, along with increased channels to capture intricate patterns specific to larger images.
+
+Getting started
+-----------------
+
+.. code-block:: python
+
+ import ivy
+ from ivy_models.efficientnet import efficientnet_b0
+ ivy.set_backend("torch")
+
+ # Instantiate efficientnet_b0 model
+ ivy_efficientnet_b0 = efficientnet_b0(pretrained=True)
+
+ # Convert the Torch image tensor to an Ivy tensor and adjust dimensions
+ img = ivy.asarray(torch_img.permute((0, 2, 3, 1)), dtype="float32", device="gpu:0")
+
+ # Compile the Ivy efficientnet_b0 model with the Ivy image tensor
+ ivy_efficientnet_b0.compile(args=(img,))
+
+ # Pass the Ivy image tensor through the Ivy efficientnet_b0 model and apply softmax
+ output = ivy.softmax(ivy_efficientnet_b0(img))
+
+ # Get the indices of the top 3 classes from the output probabilities
+ classes = ivy.argsort(output[0], descending=True)[:3]
+
+ # Retrieve the logits corresponding to the top 3 classes
+ logits = ivy.gather(output[0], classes)
+
+ 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([0.60317987, 0.18620452, 0.07535177], dev=gpu:0)`
+ `Categories of the top 3 classes are: ['tiger cat', 'tabby', 'Egyptian cat']`
+
+Citation
+--------
+
+::
+
+ @article{
+ title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
+ author={Mingxing Tan and Quoc V. Le},
+ journal={arXiv preprint arXiv:1905.11946},
+ year={2020}
+ }
+
+
+ @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}
+ }
diff --git a/ivy_models/resnet/README.rst b/ivy_models/resnet/README.rst
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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
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ResNet
+===========
+
+`ResNet `_ also known as Residual Network is a deep learning model utilized in computer vision tasks.
+It's a type of Convolutional Neural Network (CNN) architecture designed to accommodate a large number of convolutional layers,
+possibly ranging from hundreds to thousands. ResNet addresses the challenge of diminishing gradients, a problem encountered during training,
+by introducing a concept called "skip connections." These connections enable the network to skip over several initial layers,
+which consist of identity mappings (convolutional layers with no immediate impact).
+This results in faster initial training by condensing the network into fewer layers.
+
+During subsequent retraining, the network expands to its full depth, and the sections of the network that weren't initially utilized,
+called "residual parts," are given the opportunity to explore the features present in the input image more comprehensively.
+
+Getting started
+-----------------
+
+.. code-block:: python
+
+ import ivy
+ from ivy_models.resnet import resnet_34
+ ivy.set_backend("torch")
+
+ # Instantiate Resnet_34 model
+ ivy_resnet_34 = resnet_34(pretrained=True)
+
+ # Convert the Torch image tensor to an Ivy tensor and adjust dimensions
+ img = ivy.asarray(torch_img.permute((0, 2, 3, 1)), dtype="float32", device="gpu:0")
+
+ # Compile the Ivy Resnet_34 model with the Ivy image tensor
+ ivy_resnet_34.compile(args=(img,))
+
+ # Pass the Ivy image tensor through the Ivy Resnet_34 model and apply softmax
+ output = ivy.softmax(ivy_resnet_34(img))
+
+ # Get the indices of the top 3 classes from the output probabilities
+ classes = ivy.argsort(output[0], descending=True)[:3]
+
+ # Retrieve the logits corresponding to the top 3 classes
+ logits = ivy.gather(output[0], classes)
+
+
+ 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([0.85072654, 0.13506058, 0.00688287], dev=gpu:0)`
+ `Categories of the top 3 classes are: ['tiger cat', 'tabby', 'Egyptian cat']`
+
+See `this demo `_ for more usage example.
+
+Citation
+--------
+
+::
+
+ @article{
+ title={Deep Residual Learning for Image Recognition},
+ author={Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun},
+ journal={arXiv preprint arXiv:1512.03385},
+ year={2015}
+ }
+
+
+ @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}
+ }
diff --git a/ivy_models/unet/README.rst b/ivy_models/unet/README.rst
index 9cf4d558..27e42843 100644
--- a/ivy_models/unet/README.rst
+++ b/ivy_models/unet/README.rst
@@ -27,7 +27,7 @@
U-Net
===========
-`Unet `_ The UNET architecture and training approach effectively leverage data augmentation to make the most of
+The `U-Net `_ architecture and training approach effectively leverage data augmentation to make the most of
available annotated samples, even with limited data. The design features a contracting path for context capture and a symmetric expanding path
for precise localization. Notably, this UNET network achieves superior performance with minimal images during end-to-end training.
It surpasses previous methods, including a sliding-window convolutional network, in the ISBI challenge for segmenting neuronal structures in