diff --git a/README.md b/README.md
index 2b77fae..5d019dd 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,83 @@
# Lightly Studio Plugins
-A collection of plugins for [Lightly Studio](https://github.com/lightly-ai/lightly-studio). Each plugin is independently pip-installable and auto-discovered via Python entry points.
+A collection of installable plugins that extend the base functionality of [Lightly Studio](https://github.com/lightly-ai/lightly-studio).
-| Plugin | Description | Maintainer | Install |
-|---|---|---|---|
-| [BBox auto propagation nano tracker](plugins/bbox_auto_propagation_nano_tracker/)|Auto bbox propagation using nano tracker|Lightly| `pip install git+https://github.com/lightly-ai/lightly-studio-plugins.git#subdirectory=plugins/bbox_auto_propagation_nano_tracker/`|
-| [SAM3 Segmentation](plugins/sam3_segmentation/)|Automatic instance segmentation using SAM3 with a text prompt. Requires HuggingFace access to `facebook/sam3`.|Lightly| `pip install git+https://github.com/lightly-ai/lightly-studio-plugins.git#subdirectory=plugins/sam3_segmentation/`|
-| [LightlyTrain object detection inference](plugins/lightly_train_object_detection_inference/)|LightlyTrain inference operator for object detection auto-labeling|Lightly| `pip install git+https://github.com/lightly-ai/lightly-studio-plugins.git#subdirectory=plugins/lightly_train_object_detection_inference/`|
+Each plugin in this repository is packaged independently, installs in a single command, and is auto-discovered by Lightly Studio via Python entry points.
-## Adding a New Plugin
+
+
+SAM3 Segmentation Plugin
+
+Each plugin entry below includes the exact copy-paste install command. After installation, the plugin is available in Lightly Studio automatically.
+
+## Available Plugins
+
+- [BBox auto propagation nano tracker](plugins/bbox_auto_propagation_nano_tracker/)
+ Propagates boxes from one annotated video frame to other frames in the same video.
+
+
+ Details
+
+ If triggered from a frame, all bounding box annotations on that frame are
+ propagated. If triggered from an annotation, only the selected annotation is
+ propagated.
+
+ - Scope: video only, within a single video
+ - Entry points: frame or annotation
+ - Controls: forward and backward propagation windows in seconds
+ - Tradeoff: uses OpenCV NanoTracker, which is lightweight and fast on many
+ machines but less robust on difficult motion, occlusion, or scale changes
+ - Maintainer: Lightly
+ - Install:
+ `pip install git+https://github.com/lightly-ai/lightly-studio-plugins.git#subdirectory=plugins/bbox_auto_propagation_nano_tracker/`
+
+
+
+- [SAM3 Segmentation](plugins/sam3_segmentation/)
+ Segments all instances matching a text prompt in a single image or across the current view.
+
+
+ Details
+
+ This is designed for dataset-wide prompt-based labeling workflows with
+ class-like prompts such as `person`, `car`, or `dog`.
+
+ - Scope: single image or images in the current view
+ - Input: text prompt
+ - Output: segmentation masks
+ - Labels: the prompt text is used as the annotation class name
+ - Requirement: Hugging Face access to `facebook/sam3`
+ - Maintainer: Lightly
+ - Install:
+ `pip install git+https://github.com/lightly-ai/lightly-studio-plugins.git#subdirectory=plugins/sam3_segmentation/`
+
+
+
+- [LightlyTrain object detection inference](plugins/lightly_train_object_detection_inference/)
+ Runs LightlyTrain object detection inference on one image or the current view for auto-labeling.
+
+
+ Details
+
+ You can use built-in LightlyTrain models for quick bootstrapping or provide a
+ path to your own LightlyTrain checkpoint.
+
+ - Scope: single image or images in the current view
+ - Input: LightlyTrain model name or local path to a LightlyTrain checkpoint
+ - Output: object detection annotations
+ - Labels: class labels are read from the loaded model and created in the
+ dataset if they do not exist yet
+ - Recommended models:
+ `dinov3/convnext-large-ltdetr-coco` for best performance,
+ `dinov3/vits16-ltdetr-coco` for a speed/quality balance,
+ `picodet-l-coco` for resource-constrained environments
+ - Maintainer: Lightly
+ - Install:
+ `pip install git+https://github.com/lightly-ai/lightly-studio-plugins.git#subdirectory=plugins/lightly_train_object_detection_inference/`
+
+
+
+## Contributing Plugins
1. Create a new directory under `plugins/`:
```
diff --git a/plugins.toml b/plugins.toml
index 5d9f5df..a773371 100644
--- a/plugins.toml
+++ b/plugins.toml
@@ -1,7 +1,7 @@
[[plugins]]
id = "lightly_plugins_bbox_auto_propagation_nano_tracker"
name = "BBox auto propagation nano tracker"
-description = "Auto bbox propagation using nano tracker"
+description = "Propagates bounding boxes across frames in the same video using OpenCV NanoTracker"
source = "local:plugins/bbox_auto_propagation_nano_tracker"
maintainer = "lightly"
tags = ["auto-labeling", "tracking"]
@@ -9,7 +9,7 @@ tags = ["auto-labeling", "tracking"]
[[plugins]]
id = "lightly_plugins_sam3_segmentation"
name = "SAM3 Segmentation"
-description = "Automatic instance segmentation using SAM3 with a text prompt"
+description = "Segments all instances matching a text prompt in a single image or the current image view"
source = "local:plugins/sam3_segmentation"
maintainer = "lightly"
tags = ["auto-labeling", "segmentation"]
@@ -17,7 +17,7 @@ tags = ["auto-labeling", "segmentation"]
[[plugins]]
id = "lightly_plugins_lightly_train_object_detection_inference"
name = "LightlyTrain object detection inference"
-description = "LightlyTrain inference operator for object detection auto-labeling"
+description = "Runs LightlyTrain object detection inference on a single image or the current filtered image view"
source = "local:plugins/lightly_train_object_detection_inference"
maintainer = "lightly"
tags = ["auto-labeling", "object-detection", "inference"]
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