ultralytics 8.0.105 classification hyp fix and new onplot callbacks (#2684)

Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Ivan Shcheklein <shcheklein@gmail.com>

docs/models/rtdetr.md

@@ -9,6 +9,12 @@ description: Explore RT-DETR, a high-performance real-time object detector. Lear
 
 Real-Time Detection Transformer (RT-DETR) is an end-to-end object detector that provides real-time performance while maintaining high accuracy. It efficiently processes multi-scale features by decoupling intra-scale interaction and cross-scale fusion, and supports flexible adjustment of inference speed using different decoder layers without retraining. RT-DETR outperforms many real-time object detectors on accelerated backends like CUDA with TensorRT.
 
+![Model example image](https://user-images.githubusercontent.com/26833433/238963168-90e8483f-90aa-4eb6-a5e1-0d408b23dd33.png)
+**Overview of RT-DETR.** Model architecture diagram showing the last three stages of the backbone {S3, S4, S5} as the input
+to the encoder. The efficient hybrid encoder transforms multiscale features into a sequence of image features through intrascale feature interaction (AIFI) and cross-scale feature-fusion module (CCFM). The IoU-aware query selection is employed
+to select a fixed number of image features to serve as initial object queries for the decoder. Finally, the decoder with auxiliary
+prediction heads iteratively optimizes object queries to generate boxes and confidence scores ([source](https://arxiv.org/pdf/2304.08069.pdf)).
+
 ### Key Features
 
 - **Efficient Hybrid Encoder:** RT-DETR uses an efficient hybrid encoder that processes multi-scale features by decoupling intra-scale interaction and cross-scale fusion. This design reduces computational costs and allows for real-time object detection.

docs/models/sam.md

@@ -57,7 +57,7 @@ Auto-annotation is an essential feature that allows you to generate a [segmentat
 To auto-annotate your dataset using the Ultralytics framework, you can use the `auto_annotate` function as shown below:
 
 ```python
-from ultralytics.yolo.data import auto_annotate
+from ultralytics.yolo.data.annotator import auto_annotate
 
 auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
 ```