Merge pull request #29220 from omrope79:doc_optimizations_v4

[FOLLOW UP] : Documentation optimizations for the new Sphinx structure #29220

### Pull Request Readiness Checklist

This PR serves as a follow-up to the new documentation system introduced in [#29206](https://github.com/opencv/opencv/pull/29206)
Co-authored by: @abhishek-gola @kirtijindal14 @Akansha-977 @Prasadayus @varun-jaiswal17

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake

modules/dnn/include/opencv2/dnn/all_layers.hpp

@@ -57,8 +57,8 @@ CV__DNN_INLINE_NS_BEGIN
   In addition to this way of layers instantiation, there is a more common factory API (see @ref dnnLayerFactory), it allows to create layers dynamically (by name) and register new ones.
   You can use both API, but factory API is less convenient for native C++ programming and basically designed for use inside importers (see @ref readNetFromTensorflow()).
 
-  Built-in layers partially reproduce functionality of corresponding ONNX, TensorFlow and Caffe layers.
-  In particular, the following layers and Caffe importer were tested to reproduce <a href="http://caffe.berkeleyvision.org/tutorial/layers.html">Caffe</a> functionality:
+  Built-in layers reproduce the functionality of the corresponding ONNX and TensorFlow operators.
+  The following layers are among the core building blocks used to assemble imported networks:
   - Convolution
   - Deconvolution
   - Pooling

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -126,7 +126,6 @@ CV__DNN_INLINE_NS_BEGIN
         DNN_MODEL_ONNX = 1, //!< ONNX model
         DNN_MODEL_TF = 2, //!< TF model
         DNN_MODEL_TFLITE = 3, //!< TFLite model
-        DNN_MODEL_CAFFE = 4, //!< Caffe model
     };
 
     CV_EXPORTS std::string modelFormatToString(ModelFormat modelFormat);