[Docs] Model_doc structure/clarity improvements (#26876)

* first batch of structure improvements for model_docs

* second batch of structure improvements for model_docs

* more structure improvements for model_docs

* more structure improvements for model_docs

* structure improvements for cv model_docs

* more structural refactoring

* addressed feedback about image processors
This commit is contained in:
Maria Khalusova
2023-11-03 10:57:03 -04:00
committed by GitHub
parent ad8ff96224
commit 5964f820db
223 changed files with 1796 additions and 1116 deletions

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@@ -27,10 +27,6 @@ The abstract from the paper is the following:
*Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.
The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.*
Tips:
- One can use [`AutoImageProcessor`] to prepare images for the model.
The figure below illustrates the architecture of ResNet. Taken from the [original paper](https://arxiv.org/abs/1512.03385).
<img width="600" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/resnet_architecture.png"/>
@@ -52,30 +48,35 @@ If you're interested in submitting a resource to be included here, please feel f
[[autodoc]] ResNetConfig
<frameworkcontent>
<pt>
## ResNetModel
[[autodoc]] ResNetModel
- forward
## ResNetForImageClassification
[[autodoc]] ResNetForImageClassification
- forward
</pt>
<tf>
## TFResNetModel
[[autodoc]] TFResNetModel
- call
## TFResNetForImageClassification
[[autodoc]] TFResNetForImageClassification
- call
</tf>
<jax>
## FlaxResNetModel
[[autodoc]] FlaxResNetModel
@@ -85,3 +86,6 @@ If you're interested in submitting a resource to be included here, please feel f
[[autodoc]] FlaxResNetForImageClassification
- __call__
</jax>
</frameworkcontent>