ultralytics 8.1.39 add YOLO-World training (#9268)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/models/fast-sam.md

@@ -147,7 +147,7 @@ FastSAM is also available directly from the [https://github.com/CASIA-IVA-Lab/Fa
 
 4. Install the CLIP model:
    ```shell
-   pip install git+https://github.com/openai/CLIP.git
+   pip install git+https://github.com/ultralytics/CLIP.git
    ```
 
 ### Example Usage

docs/en/models/yolo-world.md

@@ -64,6 +64,39 @@ This section details the models available with their specific pre-trained weight
 
 The YOLO-World models are easy to integrate into your Python applications. Ultralytics provides user-friendly Python API and CLI commands to streamline development.
 
+### Train Usage
+
+!!! Tip "Tip"
+
+    We strongly recommend to use `yolov8-worldv2` model for custom training, because it supports deterministic training and also easy to export other formats i.e onnx/tensorrt.
+
+Object detection is straightforward with the `train` method, as illustrated below:
+
+!!! Example
+
+    === "Python"
+        PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLOWorld()` class to create a model instance in python:
+
+        ```python
+        from ultralytics import YOLOWorld
+
+        # Load a pretrained YOLOv8s-worldv2 model
+        model = YOLOWorld('yolov8s-worldv2.pt')
+
+        # Train the model on the COCO8 example dataset for 100 epochs
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
+
+        # Run inference with the YOLOv8n model on the 'bus.jpg' image
+        results = model('path/to/bus.jpg')
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Load a pretrained YOLOv8s-worldv2 model and train it on the COCO8 example dataset for 100 epochs
+        yolo train model=yolov8s-worldv2.yaml data=coco8.yaml epochs=100 imgsz=640
+        ```
+
 ### Predict Usage
 
 Object detection is straightforward with the `predict` method, as illustrated below:
@@ -196,6 +229,59 @@ You can also save a model after setting custom classes. By doing this you create
 
 This approach provides a powerful means of customizing state-of-the-art object detection models for specific tasks, making advanced AI more accessible and applicable to a broader range of practical applications.
 
+## Reproduce official results from scratch(Experimental)
+
+### Prepare datasets
+
+- Train data
+
+| Dataset                                                           | Type      | Samples | Boxes | Annotation Files                                                                                                                           |
+|-------------------------------------------------------------------|-----------|---------|-------|--------------------------------------------------------------------------------------------------------------------------------------------|
+| [Objects365v1](https://opendatalab.com/OpenDataLab/Objects365_v1) | Detection | 609k    | 9621k | [objects365_train.json](https://opendatalab.com/OpenDataLab/Objects365_v1)                                                                 |
+| [GQA](https://nlp.stanford.edu/data/gqa/images.zip)               | Grounding | 621k    | 3681k | [final_mixed_train_no_coco.json](https://huggingface.co/GLIPModel/GLIP/blob/main/mdetr_annotations/final_mixed_train_no_coco.json)         |
+| [Flickr30k](https://shannon.cs.illinois.edu/DenotationGraph/)     | Grounding | 149k    | 641k  | [final_flickr_separateGT_train.json](https://huggingface.co/GLIPModel/GLIP/blob/main/mdetr_annotations/final_flickr_separateGT_train.json) |
+
+- Val data
+
+| Dataset                                                                                                 | Type      | Annotation Files                                                                                       |
+|---------------------------------------------------------------------------------------------------------|-----------|--------------------------------------------------------------------------------------------------------|
+| [LVIS minival](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/lvis.yaml) | Detection | [minival.txt](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/lvis.yaml) |
+
+### Launch training from scratch
+
+!!! Note
+
+    `WorldTrainerFromScratch` is highly customized to allow training yolo-world models on both detection datasets and grounding datasets simultaneously. More details please checkout [ultralytics.model.yolo.world.train_world.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/world/train_world.py).
+
+!!! Example
+
+    === "Python"
+
+        ```python
+        from ultralytics.models.yolo.world.train_world import WorldTrainerFromScratch
+        from ultralytics import YOLOWorld
+
+        data = dict(
+            train=dict(
+                yolo_data=["Objects365.yaml"],
+                grounding_data=[
+                    dict(
+                        img_path="../datasets/flickr30k/images",
+                        json_file="../datasets/flickr30k/final_flickr_separateGT_train.json",
+                    ),
+                    dict(
+                        img_path="../datasets/GQA/images",
+                        json_file="../datasets/GQA/final_mixed_train_no_coco.json",
+                    ),
+                ],
+            ),
+            val=dict(yolo_data=["lvis.yaml"]),
+        )
+        model = YOLOWorld("yolov8s-worldv2.yaml")
+        model.train(data=data, batch=128, epochs=100, trainer=WorldTrainerFromScratch)
+
+        ```
+
 ## Citations and Acknowledgements
 
 We extend our gratitude to the [Tencent AILab Computer Vision Center](https://ai.tencent.com/) for their pioneering work in real-time open-vocabulary object detection with YOLO-World: