Semantic Segmentation

Note

🔥 New: LightlyTrain now supports training DINOv3 and DINOv2 models for semantic segmentation with the train_semantic_segmentation function! The method is based on the state-of-the-art segmentation model EoMT by Kerssies et al. and reaches 59.1% mIoU with DINOv3 weights and 58.4% mIoU with DINOv2 weights on the ADE20k dataset.

Benchmark Results

Below we provide the model checkpoints and report the validation mIoUs and inference latency of three different DINOv3 models fine-tuned on various datasets with LightlyTrain. We also made the comparison to the results obtained in the original EoMT paper, if available. You can check here for how to use these model checkpoints for further fine-tuning.

The experiments, unless stated otherwise, generally follow the protocol in the original EoMT paper, using a batch size of 16 and a learning rate of 1e-4. The average latency values were measured with model compilation using torch.compile on a single NVIDIA T4 GPU with FP16 precision.

You can also explore inferencing with these model weights using our Colab notebook:

COCO-Stuff

Implementation	Model	Val mIoU	Avg. Latency (ms)	Params (M)	Input Size
LightlyTrain	dinov3/vits16-eomt-coco	46.5	11.3	21.6	512×512
LightlyTrain	dinov3/vitb16-eomt-coco	52.0	23.1	85.7	512×512
LightlyTrain	dinov3/vitl16-eomt-coco	54.4	49.0	303.2	512×512

We trained with 12 epochs (~88k steps) on the COCO-Stuff dataset with num_queries=200 for EoMT.

Cityscapes

Implementation	Model	Val mIoU	Avg. Latency (ms)	Params (M)	Input Size
LightlyTrain	dinov3/vits16-eomt-cityscapes	78.6	53.8	21.6	1024×1024
LightlyTrain	dinov3/vitb16-eomt-cityscapes	81.0	114.9	85.7	1024×1024
LightlyTrain	dinov3/vitl16-eomt-cityscapes	84.4	256.4	303.2	1024×1024
Original EoMT	dinov2/vitl16-eomt	84.2	-	319	1024×1024

We trained with 107 epochs (~20k steps) on the Cityscapes dataset with num_queries=200 for EoMT.

Semantic Segmentation with EoMT

Training a semantic segmentation model with LightlyTrain is straightforward and only requires a few lines of code. See data for more details on how to prepare your dataset.

Train a Semantic Segmentation Model

import lightly_train

if __name__ == "__main__":
    lightly_train.train_semantic_segmentation(
        out="out/my_experiment",
        model="dinov2/vitl14-eomt", 
        data={
            "train": {
                "images": "my_data_dir/train/images",   # Path to training images
                "masks": "my_data_dir/train/masks",     # Path to training masks
            },
            "val": {
                "images": "my_data_dir/val/images",     # Path to validation images
                "masks": "my_data_dir/val/masks",       # Path to validation masks
            },
            "classes": {                                # Classes in the dataset                    
                0: "background",
                1: "car",
                2: "bicycle",
                # ...
            },
            # Optional, classes that are in the dataset but should be ignored during
            # training.
            "ignore_classes": [0], 
        },
    )

During training, both the

• best (with highest validation mIoU) and

• last (last validation round as determined by save_checkpoint_args.save_every_num_steps)

model weights are exported to out/my_experiment/exported_models/, unless disabled in save_checkpoint_args. You can use these weights to continue fine-tuning on another task by loading the weights via the checkpoint parameter:

import lightly_train

if __name__ == "__main__":
    lightly_train.train_semantic_segmentation(
        out="out/my_experiment",
        model="out/my_experiment/exported_models/exported_best.pt",  # Continue training from the best model
        data={...},
    )

Note

Check here for how to use the LightlyTrain model checkpoints for further fine-tuning.

Load the Trained Model from Checkpoint and Predict

After the training completes, you can load the best model checkpoints for inference like this:

import lightly_train

model = lightly_train.load_model("out/my_experiment/exported_models/exported_best.pt")
masks = model.predict("path/to/image.jpg")
# Masks is a tensor of shape (height, width) with class labels as values.
# It has the same height and width as the input image.

Visualize the Result

After making the predictions with the model weights, you can visualize the predicted masks like this:

import matplotlib.pyplot as plt
import torch
from torchvision.io import read_image
from torchvision.utils import draw_segmentation_masks

image = read_image("path/to/image.jpg")
masks = torch.stack([masks == class_id for class_id in masks.unique()])
image_with_masks = draw_segmentation_masks(image, masks, alpha=0.6)
plt.imshow(image_with_masks.permute(1, 2, 0))

The predicted masks have shape (height, width) and each value corresponds to a class ID as defined in the classes dictionary in the dataset.

🔥 Use EoMT with DINOv3 🔥

To fine-tune EoMT from DINOv3, you have to set model to one of the DINOv3 models.

Note

DINOv3 models are released under the DINOv3 license.

import lightly_train

if __name__ == "__main__":
    lightly_train.train_semantic_segmentation(
        out="out/my_experiment",
        model="dinov3/vits16-eomt",
        data={
            "train": {
                "images": "my_data_dir/train/images",   # Path to training images
                "masks": "my_data_dir/train/masks",     # Path to training masks
            },
            "val": {
                "images": "my_data_dir/val/images",     # Path to validation images
                "masks": "my_data_dir/val/masks",       # Path to validation masks
            },
            "classes": {                                # Classes in the dataset                    
                0: "background",
                1: "car",
                2: "bicycle",
                # ...
            },
            # Optional, classes that are in the dataset but should be ignored during
            # training.
            "ignore_classes": [0], 
        },
    )

Use the LightlyTrain Model Checkpoints

Now you can also start with the DINOv3 model checkpoints that LightlyTrain provides. The models are listed here in the “Model” column of the tables.

import lightly_train

if __name__ == "__main__":
    lightly_train.train_semantic_segmentation(
        out="out/my_experiment",
        model="dinov3/vits16-eomt-coco", # Use the COCO-Stuff model checkpoint for further fine-tuning
        data={...},
    )

Out

The out argument specifies the output directory where all training logs, model exports, and checkpoints are saved. It looks like this after training:

out/my_experiment
├── checkpoints
│   └── last.ckpt                                       # Last checkpoint
├── exported_models
|   └── exported_last.pt                                # Last model exported (unless disabled)
|   └── exported_best.pt                                # Best model exported (unless disabled)
├── events.out.tfevents.1721899772.host.1839736.0       # TensorBoard logs
└── train.log                                           # Training logs

The final model checkpoint is saved to out/my_experiment/checkpoints/last.ckpt. The last and best model weights are exported to out/my_experiment/exported_models/ unless disabled in save_checkpoint_args.

Tip

Create a new output directory for each experiment to keep training logs, model exports, and checkpoints organized.

Data

LightlyTrain supports training semantic segmentation models with images and masks. Every image must have a corresponding mask whose filename either matches that of the image (under a different directory) or follows a specific template pattern. The masks must be PNG images in either grayscale integer format, where each pixel value corresponds to a class ID, or multi-channel (e.g., RGB) format.

The following image formats are supported:

• jpg

• jpeg

• png

• ppm

• bmp

• pgm

• tif

• tiff

• webp

• dcm (DICOM)

For more details on LightlyTrain’s support for data input, please check the Data Input page.

The following mask formats are supported:

• png

Specify Mask Filepaths

We support two ways of specifying the mask filepaths in relation to the image filepaths:

1. Using the same filename as the image but under a different directory.

2. Using a template against the image filepath.

Using the Same Filename as the Image

We support loading masks that share the same filenames as their corresponding images under different directories. Here is an example of such a directory structure with training and validation images and masks:

my_data_dir
├── train
│   ├── images
│   │   ├── image0.jpg
│   │   └── image1.jpg
│   └── masks
│       ├── image0.png
│       └── image1.png
└── val
    ├── images
    |  ├── image2.jpg
    |  └── image3.jpg
    └── masks
       ├── image2.png
       └── image3.png

To train with this directory structure, set the data argument like this:

import lightly_train

if __name__ == "__main__":
    lightly_train.train_semantic_segmentation(
        out="out/my_experiment",
        model="dinov2/vitl14-eomt",
        data={
            "train": {
                "images": "my_data_dir/train/images",   # Path to training images
                "masks": "my_data_dir/train/masks",     # Path to training masks
            },
            "val": {
                "images": "my_data_dir/val/images",     # Path to validation images
                "masks": "my_data_dir/val/masks",       # Path to validation masks
            },
            "classes": {                                # Classes in the dataset                    
                0: "background",
                1: "car",
                2: "bicycle",
                # ...
            },
            # Optional, classes that are in the dataset but should be ignored during
            # training.
            "ignore_classes": [0], 
        },
    )

The classes in the dataset must be specified in the classes dictionary. The keys are the class IDs and the values are the class names. The class IDs must be identical to the values in the mask images. All possible class IDs must be specified, otherwise LightlyTrain will raise an error if an unknown class ID is encountered. If you would like to ignore some classes during training, you specify their class IDs in the ignore_classes argument. The trained model will then not predict these classes.

Using a Template against the Image Filepath

We also support loading masks that follow a certain template against the corresponding image filepath. For example, if you have the following directory structure:

my_data_dir
├── train
│   ├── images
│   │   ├── image0.jpg
│   │   └── image1.jpg
│   └── masks
│       ├── image0_mask.png
│       └── image1_mask.png
└── val
    ├── images
    |  ├── image2.jpg
    |  └── image3.jpg
    └── masks
       ├── image2_mask.png
       └── image3_mask.png

you could set the data argument like this:

import lightly_train

if __name__ == "__main__":
    mask_file = "{image_path.parent.parent}/masks/{image_path.stem}_mask.png"
    lightly_train.train_semantic_segmentation(
        out="out/my_experiment",
        model="dinov2/vitl14-eomt",
        data={
            "train": {
                "images": "my_data_dir/train/images",
                "masks": mask_file,  # This will match masks with the same filename stem as the training image but with a `_mask` suffix in "my_data_dir/train/masks" in PNG format
            },
            "val": {
                "images": "my_data_dir/val/images", 
                "masks": mask_file,  # This will match masks with the same filename stem as the training image but with a `_mask` suffix in "my_data_dir/val/masks" in PNG format
            },
            "classes": {             # Classes in the dataset                    
                0: "background",
                1: "car",
                2: "bicycle",
                # ...
            },
            # Optional, classes that are in the dataset but should be ignored during
            # training.
            "ignore_classes": [0], 
        },
    )

The template string always uses image_path (of type pathlib.Path) to refer to the filepath of the corresponding image. Only this parameter is allowed in the template string, which is used to calculate the mask filepath.

Specify Training Classes

We support two mask formats:

• Single-channel integer masks, where each integer value determines a label

• Multi-channel masks (e.g., RGB masks), where each pixel value determines a label

Use the classes dict in the data dict to map class IDs to labels. In this document, a class ID is a key in the classes dictionary and a label is its value.

Using Integer Masks

For single-channel integer masks (each pixel value is a label), the default is a direct mapping from class IDs to label names:

    "classes": {                   
        0: "background",
        1: "car",
        2: "bicycle",
        # ...
    },

Alternatively, to merge multiple labels into one class during training, use a dictionary like the following:

    "classes": {
        0: "background",
        1: {"name": "vehicle", "labels": [1, 2]}, # Merge label 1 and 2 into "vehicle" with class ID 1
        # ...
    },

Or:

    "classes": {
        0: {"name": "background", "labels": [0]},
        1: {"name": "vehicle", "labels": [1, 2]},
        # ...
    },

It is fine if original labels coincide with class IDs, as in the example. Only the class IDs are used for the internal classes for training and prediction masks. Note that each label can map to only one class ID.

Using Multi-channel Masks

For multi-channel masks, specify pixel values as lists of integer tuples (type list[tuple[int, ...]]) in the "labels" field:

    "classes": {
        0: {"name": "unlabeled", "labels": [(0, 0, 0), (255, 255, 255)]}, # (0,0,0) and (255,255,255) will be mapped to class "unlabeled" with class ID 0
        1: {"name": "road", "labels": [(128, 128, 128)]},
    },

These pixel values are converted to class IDs internally during training. Predictions are single-channel masks with those class IDs. Again, each label can map to only one class ID, and you cannot mix integer and tuple-valued labels in a single classes dictionary.

Model

The model argument defines the model used for semantic segmentation training. The following models are available:

DINOv3 Models

• dinov3/vits16-eomt

• dinov3/vits16plus-eomt

• dinov3/vitb16-eomt

• dinov3/vitl16-eomt

• dinov3/vitl16plus-eomt

• dinov3/vith16plus-eomt

• dinov3/vit7b16-eomt

All DINOv3 models are pretrained by Meta.

DINOv2 Models

• dinov2/vits14-eomt

• dinov2/vitb14-eomt

• dinov2/vitl14-eomt

• dinov2/vitg14-eomt

All DINOv2 models are pretrained by Meta.

Logging

Logging is configured with the logger_args argument. The following loggers are supported:

• mlflow: Logs training metrics to MLflow (disabled by default, requires MLflow to be installed)

• tensorboard: Logs training metrics to TensorBoard (enabled by default, requires TensorBoard to be installed)

• wandb: Logs training metrics to Weights & Biases (disabled by default, requires wandb to be installed)

MLflow

Important

MLflow must be installed with pip install "lightly-train[mlflow]".

The mlflow logger can be configured with the following arguments:

import lightly_train

if __name__ == "__main__":
    lightly_train.train_semantic_segmentation(
        out="out/my_experiment",
        model="dinov2/vitl14-eomt",
        data={
            # ...
        },
        logger_args={
            "mlflow": {
                "experiment_name": "my_experiment",
                "run_name": "my_run",
                "tracking_uri": "tracking_uri",
            },
        },
    )

TensorBoard

TensorBoard logs are automatically saved to the output directory. Run TensorBoard in a new terminal to visualize the training progress:

tensorboard --logdir out/my_experiment

Disable the TensorBoard logger with:

logger_args={"tensorboard": None}

Weights & Biases

Important

Weights & Biases must be installed with pip install "lightly-train[wandb]".

The Weights & Biases logger can be configured with the following arguments:

import lightly_train

if __name__ == "__main__":
    lightly_train.train_semantic_segmentation(
        out="out/my_experiment",
        model="dinov3/vitl16-eomt-coco",
        data={
            # ...
        },
        logger_args={
            "wandb": {
                "project": "my_project",
                "name": "my_experiment",
                "log_model": False,        # Set to True to upload model checkpoints
            },
        },
    )

Resume Training

Like in pretraining, there are two distinct ways to continue training, depending on your intention. Therefore, the resume_interrupted=True parameter cannot be combined with passing a checkpoint path to the model parameter.

Resume Interrupted Training

Use resume_interrupted=True to resume a previously interrupted or crashed training run. This will pick up exactly where the training left off.

• You must use the same out directory as the original run.

• You must not change any training parameters (e.g., learning rate, batch size, data, etc.).

• This is intended for continuing the same run without modification.

This will utilize the .ckpt checkpoint file out/my_experiment/checkpoints/last.ckpt to restore the entire training state, including model weights, optimizer state, and epoch count.

Load Weights for a New Run

As stated above, you can specify model="<checkpoint path"> to further fine-tune a model from a previous run.

• You are free to change training parameters.

• This is useful for continuing training with a different setup.

We recommend using the exported best model weights from out/my_experiment/exported_models/exported_best.pt for this purpose, though a .ckpt file can also be loaded.

Pretrain and Fine-tune a Semantic Segmentation Model

To further improve the performance of your semantic segmentation model, you can first pretrain a DINOv2 model on unlabeled data using self-supervised learning and then fine-tune it on your segmentation dataset. This is especially useful if your dataset is only partially labeled or if you have access to a large amount of unlabeled data.

The following example shows how to pretrain and fine-tune the model. Check out the page on DINOv2 to learn more about pretraining DINOv2 models on unlabeled data.

import lightly_train

if __name__ == "__main__":
    # Pretrain a DINOv2 model.
    lightly_train.pretrain(
        out="out/my_pretrain_experiment",
        data="my_pretrain_data_dir",
        model="dinov2/vitl14",
        method="dinov2",
    )

    # Fine-tune the DINOv2 model for semantic segmentation.
    lightly_train.train_semantic_segmentation(
        out="out/my_experiment",
        model="dinov2/vitl14-eomt",
        model_args={
            # Path to your pretrained DINOv2 model.
            "backbone_weights": "out/my_pretrain_experiment/exported_models/exported_best.pt",
        },
        data={
            "train": {
                "images": "my_data_dir/train/images",   # Path to training images
                "masks": "my_data_dir/train/masks",     # Path to training masks
            },
            "val": {
                "images": "my_data_dir/val/images",     # Path to validation images
                "masks": "my_data_dir/val/masks",       # Path to validation masks
            },
            "classes": {                                # Classes in the dataset                    
                0: "background",
                1: "car",
                2: "bicycle",
                # ...
            },
            # Optional, classes that are in the dataset but should be ignored during
            # training.
            "ignore_classes": [0], 
        },
    )

Default Image Transform Arguments

The following are the default train transform arguments for EoMT. The validation arguments are automatically inferred from the train arguments. Specifically the image size and normalization are shared between train and validation.

You can configure the image size and normalization like this:

import lightly_train

if __name__ == "__main__":
    lightly_train.train_semantic_segmentation(
        out="out/my_experiment",
        model="dinov2/vitl14-eomt",
        data={
            "train": {
                "images": "my_data_dir/train/images",   # Path to training images
                "masks": "my_data_dir/train/masks",     # Path to training masks
            },
            "val": {
                "images": "my_data_dir/val/images",     # Path to validation images
                "masks": "my_data_dir/val/masks",       # Path to validation masks
            },
            "classes": {                                # Classes in the dataset                    
                0: "background",
                1: "car",
                2: "bicycle",
                # ...
            },
            # Optional, classes that are in the dataset but should be ignored during
            # training.
            "ignore_classes": [0], 
        },
        transform_args={
            "image_size": (518, 518), # (height, width)
            "normalize": {
                "mean": [0.485, 0.456, 0.406],
                "std": [0.229, 0.224, 0.225],
            },
        },
    )

EoMT DINOv2 Default Transform Arguments

Train

{
    "channel_drop": null,
    "color_jitter": {
        "brightness": 0.12549019607843137,
        "contrast": 0.5,
        "hue": 0.05,
        "prob": 0.5,
        "saturation": 0.5,
        "strength": 1.0
    },
    "ignore_index": -100,
    "image_size": "auto",
    "normalize": "auto",
    "num_channels": "auto",
    "random_crop": {
        "fill": 0,
        "height": "auto",
        "pad_if_needed": true,
        "pad_position": "center",
        "prob": 1.0,
        "width": "auto"
    },
    "random_flip": {
        "horizontal_prob": 0.5,
        "vertical_prob": 0.0
    },
    "scale_jitter": {
        "divisible_by": null,
        "max_scale": 2.0,
        "min_scale": 0.5,
        "num_scales": 20,
        "prob": 1.0,
        "seed_offset": 0,
        "sizes": null,
        "step_seeding": false
    },
    "smallest_max_size": null
}

Val

{
    "channel_drop": null,
    "color_jitter": null,
    "ignore_index": -100,
    "image_size": "auto",
    "normalize": "auto",
    "num_channels": "auto",
    "random_crop": null,
    "random_flip": null,
    "scale_jitter": null,
    "smallest_max_size": {
        "max_size": "auto",
        "prob": 1.0
    }
}

EoMT DINOv3 Default Transform Arguments

Train

{
    "channel_drop": null,
    "color_jitter": {
        "brightness": 0.12549019607843137,
        "contrast": 0.5,
        "hue": 0.05,
        "prob": 0.5,
        "saturation": 0.5,
        "strength": 1.0
    },
    "ignore_index": -100,
    "image_size": "auto",
    "normalize": "auto",
    "num_channels": "auto",
    "random_crop": {
        "fill": 0,
        "height": "auto",
        "pad_if_needed": true,
        "pad_position": "center",
        "prob": 1.0,
        "width": "auto"
    },
    "random_flip": {
        "horizontal_prob": 0.5,
        "vertical_prob": 0.0
    },
    "scale_jitter": {
        "divisible_by": null,
        "max_scale": 2.0,
        "min_scale": 0.5,
        "num_scales": 20,
        "prob": 1.0,
        "seed_offset": 0,
        "sizes": null,
        "step_seeding": false
    },
    "smallest_max_size": null
}

Val

{
    "channel_drop": null,
    "color_jitter": null,
    "ignore_index": -100,
    "image_size": "auto",
    "normalize": "auto",
    "num_channels": "auto",
    "random_crop": null,
    "random_flip": null,
    "scale_jitter": null,
    "smallest_max_size": {
        "max_size": "auto",
        "prob": 1.0
    }
}

In case you need different parameters for training and validation, you can pass an optional val dictionary to transform_args to override the validation parameters:

transform_args={
    "image_size": (518, 518), # (height, width)
    "normalize": {
        "mean": [0.485, 0.456, 0.406],
        "std": [0.229, 0.224, 0.225],
    },
    "val": {    # Override validation parameters
        "image_size": (512, 512), # (height, width)
    }
}

Exporting a Checkpoint to ONNX

Open Neural Network Exchange (ONNX) is a standard format for representing machine learning models in a framework independent manner. In particular, it is useful for deploying our models on edge devices where PyTorch is not available.

Currently, we support exporting as ONNX for DINOv2 EoMT segmentation models. The support for DINOv3 EoMT will be released in the short term.

The following example shows how to export a previously trained checkpoint to ONNX using the export_onnx function.

import lightly_train

lightly_train.export_onnx(
    out="model.onnx",
    checkpoint="out/my_experiment/exported_models/exported_best.pt",
    height=518,
    width=518
)

Requirements

Exporting to ONNX requires some additional packages to be installed. Namely

• onnx

• onnxruntime if verify is set to True.

• onnxslim if simplify is set to True.