Update docs/readme #454

guarin · 2025-11-19T15:37:03Z

What has changed and why?

Update README
Update main docs page

How has it been tested?

LightlyTrain documentation - update.pdf

Did you update CHANGELOG.md?

Yes
Not needed (internal change)

Did you update the documentation?

Yes
Not needed (internal change without effects for user)

Copilot

Pull Request Overview

This PR updates the documentation and README to improve clarity and consistency. The changes focus on restructuring content to highlight workflows and use cases rather than technical features.

Key Changes:

Restructured documentation tables to consolidate model names and reorder columns for better readability
Added Colab badge links to various documentation pages for quick access to notebooks
Refactored main documentation page and README to showcase workflows (Object Detection, Instance Segmentation, Semantic Segmentation, Distillation, Pretraining, Autolabeling) instead of features

Reviewed Changes

Copilot reviewed 9 out of 9 changed files in this pull request and generated 17 comments.

Show a summary per file

File	Description
docs/source/train/index.md	Added Colab badge link to quick start notebook
docs/source/semantic_segmentation.md	Restructured benchmark tables, moved checkpoint names to "Model" column, updated references
docs/source/predict_autolabel.md	Restructured table to remove "Checkpoint Name" column, updated model name references in text
docs/source/object_detection.md	Changed "LT-DETR" to "LTDETR", restructured table with model names in "Model" column
docs/source/methods/distillation.md	Added Colab badge links at two locations
docs/source/methods/dinov2.md	Updated table header from "ImageNet k-NN" to "Val ImageNet k-NN"
docs/source/instance_segmentation.md	Restructured table with reordered columns, added Colab badge
docs/source/index.md	Major refactoring to workflow-centric structure with grid layout, added usage events section
README.md	Major refactoring to workflow-centric structure using collapsible sections, removed FAQ section

Copilot · 2025-11-19T15:42:13Z

docs/source/object_detection.md

+Below we provide the model checkpoints and report the validation mAP<sub>50:95</sub> and
+inference FPS of different DINOv3 and DINOv2-based models, fine-tuned on the COCO dataset.
+You can check [here](object-detection-use-model-weights) for how to use these model
+checkpoints for further fine-tuning. The average FPS values were measured using TensorRT
+in the version `10.13.3.9` and on a Nvidia T4 GPU with batch size 1.


Lines 16-20 exceed the 88-character limit specified in the Markdown documentation style guidelines. These lines should be wrapped at 88 characters. For example, line 16 is 91 characters, line 17 is 93 characters, line 18 is 91 characters, and line 19 is 94 characters.

Suggested change

Below we provide the model checkpoints and report the validation mAP<sub>50:95</sub> and

inference FPS of different DINOv3 and DINOv2-based models, fine-tuned on the COCO dataset.

You can check [here](object-detection-use-model-weights) for how to use these model

checkpoints for further fine-tuning. The average FPS values were measured using TensorRT

in the version `10.13.3.9` and on a Nvidia T4 GPU with batch size 1.

Below we provide the model checkpoints and report the validation mAP<sub>50:95</sub>

and inference FPS of different DINOv3 and DINOv2-based models, fine-tuned on the COCO

dataset. You can check [here](object-detection-use-model-weights) for how to use these

model checkpoints for further fine-tuning. The average FPS values were measured using

TensorRT in the version `10.13.3.9` and on a Nvidia T4 GPU with batch size 1.

Copilot · 2025-11-19T15:42:13Z

docs/source/index.md

+LightlyTrain is the leading framework for transforming your data into state-of-the-art
+computer vision models. It covers the entire model development lifecycle from pretraining
+DINOv2/v3 vision foundation models on your unlabeled data to fine-tuning transformer and
+YOLO models on detection and segmentation tasks for edge deployment.


Lines 31-34 exceed the 88-character limit specified in the Markdown documentation style guidelines. Line 31 is 91 characters, line 32 is 92 characters, and line 33 is 91 characters. These lines should be wrapped to comply with the 88-character limit.

Suggested change

LightlyTrain is the leading framework for transforming your data into state-of-the-art

computer vision models. It covers the entire model development lifecycle from pretraining

DINOv2/v3 vision foundation models on your unlabeled data to fine-tuning transformer and

YOLO models on detection and segmentation tasks for edge deployment.

LightlyTrain is the leading framework for transforming your data into

state-of-the-art computer vision models. It covers the entire model

development lifecycle from pretraining DINOv2/v3 vision foundation models

on your unlabeled data to fine-tuning transformer and YOLO models on

detection and segmentation tasks for edge deployment.

Copilot · 2025-11-19T15:42:14Z

docs/source/index.md

+- Python, Command Line, and [Docker](https://docs.lightly.ai/train/stable/docker.html) support
+- Built for [high performance](https://docs.lightly.ai/train/stable/performance/index.html) including [multi-GPU](https://docs.lightly.ai/train/stable/performance/multi_gpu.html) and [multi-node](https://docs.lightly.ai/train/stable/performance/multi_node.html) support
+- [Monitor training progress](https://docs.lightly.ai/train/stable/train.html#loggers) with MLflow, TensorBoard, Weights & Biases, and more


Lines 134-136 exceed the 88-character limit. Line 134 is 103 characters, line 135 is 200 characters, and line 136 is 143 characters. These lines should be wrapped at 88 characters per the Markdown documentation style guidelines.

Suggested change

- Python, Command Line, and [Docker](https://docs.lightly.ai/train/stable/docker.html) support

- Built for [high performance](https://docs.lightly.ai/train/stable/performance/index.html) including [multi-GPU](https://docs.lightly.ai/train/stable/performance/multi_gpu.html) and [multi-node](https://docs.lightly.ai/train/stable/performance/multi_node.html) support

- [Monitor training progress](https://docs.lightly.ai/train/stable/train.html#loggers) with MLflow, TensorBoard, Weights & Biases, and more

- Python, Command Line, and

[Docker](https://docs.lightly.ai/train/stable/docker.html) support

- Built for

[high performance](https://docs.lightly.ai/train/stable/performance/index.html)

including

[multi-GPU](https://docs.lightly.ai/train/stable/performance/multi_gpu.html) and

[multi-node](https://docs.lightly.ai/train/stable/performance/multi_node.html) support

- [Monitor training progress](https://docs.lightly.ai/train/stable/train.html#loggers)

with MLflow, TensorBoard, Weights & Biases, and more

Copilot · 2025-11-19T15:42:14Z

docs/source/index.md

+| LightlyTrain | DINOv3 | ✅ [🔗](https://docs.lightly.ai/train/stable/object_detection.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/instance_segmentation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/semantic_segmentation.html#use-eomt-with-dinov3) | ✅ [🔗](https://docs.lightly.ai/train/stable/methods/distillation.html#distill-from-dinov3) | | ✅ [🔗](https://docs.lightly.ai/train/stable/predict_autolabel.html) |
+| | DINOv2 | ✅ [🔗](https://docs.lightly.ai/train/stable/object_detection.html) | | ✅ [🔗](https://docs.lightly.ai/train/stable/semantic_segmentation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/methods/distillation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/methods/dinov2.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/predict_autolabel.html) |
+| Torchvision | ResNet | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | |
+| | ConvNext | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | |
+| | ShuffleNetV2 | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | |
+| TIMM | All models | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/models/timm.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/models/timm.html) | |
+| Ultralytics | YOLOv5 | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/models/ultralytics.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/models/ultralytics.html) | |
+| | YOLOv6 | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/models/ultralytics.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/models/ultralytics.html) | |


Lines 147, 149, and 150 significantly exceed the 88-character limit (line 147 has 364 characters, line 149 has 447 characters, line 150 has 403 characters). While Markdown tables can be challenging to wrap, consider restructuring the table to reduce line length, such as using shorter URLs via link references or breaking the table into multiple smaller tables.

Suggested change

| LightlyTrain | DINOv3 | ✅ [🔗](https://docs.lightly.ai/train/stable/object_detection.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/instance_segmentation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/semantic_segmentation.html#use-eomt-with-dinov3) | ✅ [🔗](https://docs.lightly.ai/train/stable/methods/distillation.html#distill-from-dinov3) | | ✅ [🔗](https://docs.lightly.ai/train/stable/predict_autolabel.html) |

| | DINOv2 | ✅ [🔗](https://docs.lightly.ai/train/stable/object_detection.html) | | ✅ [🔗](https://docs.lightly.ai/train/stable/semantic_segmentation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/methods/distillation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/methods/dinov2.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/predict_autolabel.html) |

| Torchvision | ResNet | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | |

| | ConvNext | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | |

| | ShuffleNetV2 | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | |

| TIMM | All models | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/models/timm.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/models/timm.html) | |

| Ultralytics | YOLOv5 | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/models/ultralytics.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/models/ultralytics.html) | |

| | YOLOv6 | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/models/ultralytics.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/models/ultralytics.html) | |

| LightlyTrain | DINOv3 | ✅ [🔗][object_detection] | ✅ [🔗][instance_segmentation] | ✅ [🔗][semantic_segmentation_dinov3] | ✅ [🔗][distillation_dinov3] | | ✅ [🔗][autolabel] |

| | DINOv2 | ✅ [🔗][object_detection] | | ✅ [🔗][semantic_segmentation] | ✅ [🔗][distillation] | ✅ [🔗][dinov2_pretrain] | ✅ [🔗][autolabel] |

| Torchvision | ResNet | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | |

| | ConvNext | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | |

| | ShuffleNetV2 | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/models/torchvision.html) | |

| TIMM | All models | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/models/timm.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/models/timm.html) | |

| Ultralytics | YOLOv5 | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/models/ultralytics.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/models/ultralytics.html) | |

| | YOLOv6 | | | | ✅ [🔗][ultralytics_models] | ✅ [🔗][ultralytics_models] | |

Copilot · 2025-11-19T15:42:15Z

docs/source/index.md

+track training method, model architecture, and system information (OS, GPU). To opt-out,
+set the environment variable: `export LIGHTLY_TRAIN_EVENTS_DISABLED=1`


Line 172 exceeds the 88-character limit (89 characters). It should be wrapped to comply with the Markdown documentation style guidelines.

Suggested change

track training method, model architecture, and system information (OS, GPU). To opt-out,

set the environment variable: `export LIGHTLY_TRAIN_EVENTS_DISABLED=1`

track training method, model architecture, and system information (OS, GPU).

To opt-out, set the environment variable: `export LIGHTLY_TRAIN_EVENTS_DISABLED=1`

Copilot · 2025-11-19T15:42:17Z

README.md

+LightlyTrain provides simple commands to autolabel your unlabeled data using DINOv2 or
+DINOv3 pretrained models. This allows you to efficiently boost performance of your


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Copilot · 2025-11-19T15:42:18Z

README.md

+The better results with auto-labeling were achieved by fine-tuning a ViT-H+ on the
+ADE20K dataset, which reaches 0.595 validation mIoU. This model was then used to autolabel
+100k images from the SUN397 dataset. Using these labels, we subsequently fine-tuned the


Multiple lines exceed the 88-character limit: line 242 (92 chars), line 266 (90 chars), line 267 (89 chars), line 279 (89 chars), line 280 (93 chars), and line 281 (90 chars). These should be wrapped to comply with the Markdown documentation style guidelines.

Copilot · 2025-11-19T15:42:18Z

README.md

+- Python, Command Line, and [Docker](https://docs.lightly.ai/train/stable/docker.html) support
+- Built for [high performance](https://docs.lightly.ai/train/stable/performance/index.html) including [multi-GPU](https://docs.lightly.ai/train/stable/performance/multi_gpu.html) and [multi-node](https://docs.lightly.ai/train/stable/performance/multi_node.html) support
+- [Monitor training progress](https://docs.lightly.ai/train/stable/train.html#loggers) with MLflow, TensorBoard, Weights & Biases, and more


Lines 303-305 exceed the 88-character limit (line 303 has 103 characters, line 304 has 200 characters, line 305 has 143 characters). These should be wrapped to comply with the Markdown documentation style guidelines.

Suggested change

- Python, Command Line, and [Docker](https://docs.lightly.ai/train/stable/docker.html) support

- Built for [high performance](https://docs.lightly.ai/train/stable/performance/index.html) including [multi-GPU](https://docs.lightly.ai/train/stable/performance/multi_gpu.html) and [multi-node](https://docs.lightly.ai/train/stable/performance/multi_node.html) support

- [Monitor training progress](https://docs.lightly.ai/train/stable/train.html#loggers) with MLflow, TensorBoard, Weights & Biases, and more

- Python, Command Line, and

[Docker](https://docs.lightly.ai/train/stable/docker.html) support

- Built for

[high performance](https://docs.lightly.ai/train/stable/performance/index.html)

including

[multi-GPU](https://docs.lightly.ai/train/stable/performance/multi_gpu.html) and

[multi-node](https://docs.lightly.ai/train/stable/performance/multi_node.html) support

- [Monitor training progress](https://docs.lightly.ai/train/stable/train.html#loggers)

with MLflow, TensorBoard, Weights & Biases, and more

Copilot · 2025-11-19T15:42:18Z

README.md

+| LightlyTrain | DINOv3 | ✅ [🔗](https://docs.lightly.ai/train/stable/object_detection.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/instance_segmentation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/semantic_segmentation.html#use-eomt-with-dinov3) | ✅ [🔗](https://docs.lightly.ai/train/stable/methods/distillation.html#distill-from-dinov3) | | ✅ [🔗](https://docs.lightly.ai/train/stable/predict_autolabel.html) |
+| | DINOv2 | ✅ [🔗](https://docs.lightly.ai/train/stable/object_detection.html) | | ✅ [🔗](https://docs.lightly.ai/train/stable/semantic_segmentation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/methods/distillation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/methods/dinov2.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/predict_autolabel.html) |


Lines 316, 318, and 319 significantly exceed the 88-character limit (line 316 has 364 characters, line 318 has 447 characters, line 319 has 403 characters). While Markdown tables can be challenging to wrap, consider restructuring the table to reduce line length, such as using shorter URLs via link references or breaking the table into multiple smaller tables.

Suggested change

| LightlyTrain | DINOv3 | ✅ [🔗](https://docs.lightly.ai/train/stable/object_detection.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/instance_segmentation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/semantic_segmentation.html#use-eomt-with-dinov3) | ✅ [🔗](https://docs.lightly.ai/train/stable/methods/distillation.html#distill-from-dinov3) | | ✅ [🔗](https://docs.lightly.ai/train/stable/predict_autolabel.html) |

| | DINOv2 | ✅ [🔗](https://docs.lightly.ai/train/stable/object_detection.html) | | ✅ [🔗](https://docs.lightly.ai/train/stable/semantic_segmentation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/methods/distillation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/methods/dinov2.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/predict_autolabel.html) |

| LightlyTrain | DINOv3 | ✅ [🔗][object_detection] | ✅ [🔗][instance_segmentation] | ✅ [🔗][semantic_segmentation_eomt] | ✅ [🔗][distill_dinov3] | | ✅ [🔗][autolabel] |

| | DINOv2 | ✅ [🔗][object_detection] | | ✅ [🔗][semantic_segmentation] | ✅ [🔗][distill_dinov2] | ✅ [🔗][pretrain_dinov2] | ✅ [🔗][autolabel] |

Copilot · 2025-11-19T15:42:19Z

README.md

+track training method, model architecture, and system information (OS, GPU). To opt-out,
+set the environment variable: `export LIGHTLY_TRAIN_EVENTS_DISABLED=1`


Line 343 exceeds the 88-character limit (89 characters). It should be wrapped to comply with the Markdown documentation style guidelines.

Suggested change

track training method, model architecture, and system information (OS, GPU). To opt-out,

set the environment variable: `export LIGHTLY_TRAIN_EVENTS_DISABLED=1`

track training method, model architecture, and system information (OS, GPU).

To opt-out, set the environment variable: `export LIGHTLY_TRAIN_EVENTS_DISABLED=1`

IgorSusmelj

I'd change a few minor things (to make the sample code more useful and end2end we should also show how to use the trained models. And I'd leave away the huge table of models.

IgorSusmelj · 2025-11-20T19:24:58Z

README.md

-        data="my_data_dir",
-        model="ultralytics/yolov8s.pt",
-        method="distillation",
+    lightly_train.train_object_detection(


Add a comment above this line to show that the following trains a model

IgorSusmelj · 2025-11-20T19:25:31Z

README.md

+                2: "car",
+            },
+        },
    )


Add a comment below and example how to run inference with the previously trained model (also mention inference output shape)

IgorSusmelj · 2025-11-20T19:25:44Z

README.md


 if __name__ == "__main__":
-    lightly_train.train_object_detection(
+    lightly_train.train_instance_segmentation(


Same as for detection

IgorSusmelj · 2025-11-20T19:25:49Z

README.md

-          # data config ...
-        }
-    )
+  lightly_train.train_semantic_segmentation(


Same as for detection

IgorSusmelj · 2025-11-20T19:25:57Z

README.md


-MLOps
+if __name__ == "__main__":
+  lightly_train.train(


Same as for detection

IgorSusmelj · 2025-11-20T19:27:49Z

README.md

+
+Scroll right to see all columns!
+
+| Framework | Model | Object Detection<br><sub>(*Labeled Images*)</sub> | Instance Segmentation<br><sub>(*Labeled Images*)</sub> | Semantic Segmentation<br><sub>(*Labeled Images*)</sub> | Distill From<br> DINOv2/v3<br><sub>(*Unlabeled Images*)</sub> | Pretrain<br><sub>(*Unlabeled Images*)</sub> | Autolabel |


It's a cool idea with the table but this is too verbose and uses too much space for little information. I'd leave it away

guarin added 6 commits November 19, 2025 14:46

Update

94e4b37

Cleanup tables

f2c588d

Update

fedcb84

Format

08a49c6

Format

d8c3f64

Update colab links

358a8eb

Copilot AI review requested due to automatic review settings November 19, 2025 15:37

Copilot started reviewing on behalf of guarin November 19, 2025 15:37 View session

Copilot finished reviewing on behalf of guarin November 19, 2025 15:38

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IgorSusmelj requested changes Nov 20, 2025

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		track training method, model architecture, and system information (OS, GPU). To opt-out,
		set the environment variable: `export LIGHTLY_TRAIN_EVENTS_DISABLED=1`

		LightlyTrain provides simple commands to autolabel your unlabeled data using DINOv2 or
		DINOv3 pretrained models. This allows you to efficiently boost performance of your

		\| LightlyTrain \| DINOv3 \| ✅ [🔗](https://docs.lightly.ai/train/stable/object_detection.html) \| ✅ [🔗](https://docs.lightly.ai/train/stable/instance_segmentation.html) \| ✅ [🔗](https://docs.lightly.ai/train/stable/semantic_segmentation.html#use-eomt-with-dinov3) \| ✅ [🔗](https://docs.lightly.ai/train/stable/methods/distillation.html#distill-from-dinov3) \| \| ✅ [🔗](https://docs.lightly.ai/train/stable/predict_autolabel.html) \|
		\| \| DINOv2 \| ✅ [🔗](https://docs.lightly.ai/train/stable/object_detection.html) \| \| ✅ [🔗](https://docs.lightly.ai/train/stable/semantic_segmentation.html) \| ✅ [🔗](https://docs.lightly.ai/train/stable/methods/distillation.html) \| ✅ [🔗](https://docs.lightly.ai/train/stable/methods/dinov2.html) \| ✅ [🔗](https://docs.lightly.ai/train/stable/predict_autolabel.html) \|


		Scroll right to see all columns!

		\| Framework \| Model \| Object Detection<br><sub>(Labeled Images)</sub> \| Instance Segmentation<br><sub>(Labeled Images)</sub> \| Semantic Segmentation<br><sub>(Labeled Images)</sub> \| Distill From<br> DINOv2/v3<br><sub>(Unlabeled Images)</sub> \| Pretrain<br><sub>(Unlabeled Images)</sub> \| Autolabel \|

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guarin commented Nov 19, 2025

What has changed and why?

How has it been tested?

Did you update CHANGELOG.md?

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