[Docs]: Link buttons, add autobackend, BaseModel and ops (#130)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/quickstart.md

@@ -33,7 +33,7 @@ CLI requires no customization or code. You can simply run all tasks from the ter
         ```bash
         yolo task=detect mode=train model=s.yaml device=\'0,1,2,3\'
         ```
-[CLI Guide](#){ .md-button .md-button--primary}
+[CLI Guide](cli.md){ .md-button .md-button--primary}
 
 ## Python API
 Ultralytics YOLO comes with pythonic Model and Trainer interface. 
@@ -42,10 +42,9 @@ Ultralytics YOLO comes with pythonic Model and Trainer interface.
     import ultralytics
     from ultralytics import YOLO
 
-    model = YOLO()
-    model.new("s-seg.yaml") # automatically detects task type
-    model.load("s-seg.pt") # load checkpoint
+    model = YOLO("s-seg.yaml") # automatically detects task type
+    model = YOLO("s-seg.pt") # load checkpoint
     model.train(data="coco128-segments", epochs=1, lr0=0.01, ...)
     model.train(data="coco128-segments", epochs=1, lr0=0.01, device="0,1,2,3") # DDP mode
     ```
-[API Guide](#){ .md-button .md-button--primary}
+[API Guide](sdk.md){ .md-button .md-button--primary}

docs/reference/nn.md

@@ -0,0 +1,15 @@
+# nn Module
+Ultralytics nn module contains 3 main components:
+
+1. **AutoBackend**: A module that can run inference on all popular model formats
+2. **BaseModel**: `BaseModel` class defines the operations supported by tasks like Detection and Segmentation
+3. **modules**: Optimized and reusable neural network blocks built on PyTorch.
+
+## AutoBackend
+:::ultralytics.nn.autobackend.AutoBackend
+
+## BaseModel
+:::ultralytics.nn.tasks.BaseModel
+
+## Modules
+TODO

docs/reference/ops.md

@@ -0,0 +1,162 @@
+This module contains optimized deep learning related operations used in the Ultralytics YOLO framework
+## Non-max suppression
+:::ultralytics.ops.non_max_suppression
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## Scale boxes
+:::ultralytics.ops.scale_boxes
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## Scale image
+:::ultralytics.ops.scale_image
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## clip boxes
+:::ultralytics.ops.clip_boxes
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+# Box Format Conversion
+## xyxy2xywh
+:::ultralytics.ops.xyxy2xywh
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## xywh2xyxy
+:::ultralytics.ops.xywh2xyxy
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## xywhn2xyxy
+:::ultralytics.ops.xywhn2xyxy
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## xyxy2xywhn
+:::ultralytics.ops.xyxy2xywhn
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## xyn2xy
+:::ultralytics.ops.xyn2xy
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## xywh2ltwh
+:::ultralytics.ops.xywh2ltwh
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## xyxy2ltwh
+:::ultralytics.ops.xyxy2ltwh
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## ltwh2xywh
+:::ultralytics.ops.ltwh2xywh
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## ltwh2xyxy
+:::ultralytics.ops.ltwh2xyxy
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## segment2box
+:::ultralytics.ops.segment2box
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+# Mask Operations
+## resample_segments
+:::ultralytics.ops.resample_segments
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## crop_mask
+:::ultralytics.ops.crop_mask
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## process_mask_upsample
+:::ultralytics.ops.process_mask_upsample
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## process_mask
+:::ultralytics.ops.process_mask
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## process_mask_native
+:::ultralytics.ops.process_mask_native
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## scale_segments
+:::ultralytics.ops.scale_segments
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## masks2segments
+:::ultralytics.ops.masks2segments
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+## clip_segments
+:::ultralytics.ops.clip_segments
+    handler: python
+    options:
+        show_source: false
+        show_root_toc_entry: false
+---
+
+
+
+
+

docs/sdk.md

@@ -6,8 +6,7 @@ This is the simplest way of simply using yolo models in a python environment. It
         ```python
         from ultralytics import YOLO
 
-        model = YOLO()
-        model.new("n.yaml") # pass any model type
+        model = YOLO("yolov8n.yaml")
         model(img_tensor) # Or model.forward(). inference.
         model.train(data="coco128.yaml", epochs=5)
         ```
@@ -16,10 +15,9 @@ This is the simplest way of simply using yolo models in a python environment. It
         ```python
         from ultralytics import YOLO
 
-        model = YOLO()
-        model.load("n.pt") # pass any model type
+        model = YOLO("yolov8n.pt") # pass any model type
         model(...) # inference
-        model.train(data="coco128.yaml", epochs=5)
+        model.train(epochs=5)
         ```
 
     === "Resume Training"
@@ -35,8 +33,7 @@ This is the simplest way of simply using yolo models in a python environment. It
     ```python
     from ultralytics import YOLO
 
-    model = YOLO()
-    model.load("model.pt")
+    model = YOLO("model.pt")
     model.predict(source="0") # accepts all formats - img/folder/vid.*(mp4/format). 0 for webcam
     model.predict(source="folder", view_img=True) # Display preds. Accepts all yolo predict arguments
 
@@ -48,7 +45,7 @@ This is the simplest way of simply using yolo models in a python environment. It
         ```python
         from ultralytics import YOLO
 
-        model = YOLO()
+        model = YOLO("model.pt")
         model.fuse()  
         model.info(verbose=True)  # Print model information
         model.export(format=)  # TODO: 
@@ -61,7 +58,7 @@ This is the simplest way of simply using yolo models in a python environment. It
 
 To know more about using `YOLO` models, refer Model class refernce
 
-[Model reference](#){ .md-button .md-button--primary}
+[Model reference](reference/model.md){ .md-button .md-button--primary}
 
 ---
 ### Customizing Tasks with Trainers