Panoptic Segmentation¶

Note

🔥 LightlyTrain now supports training DINOv3-based panoptic segmentation models with the EoMT architecture by Kerssies et al.!

Benchmark Results¶

Below we provide the models and report the validation panoptic quality (PQ) and inference latency of different DINOv3 models fine-tuned on COCO with LightlyTrain. You can check here how to use these models for further fine-tuning.

You can also explore running inference and training these models using our Colab notebook:

COCO¶

Implementation	Model	Val PQ	Avg. Latency (ms)	Params (M)	Input Size
LightlyTrain	dinov3/vitt16-eomt-panoptic-coco	38.0	13.5	6.0	640×640
LightlyTrain	dinov3/vittplus16-eomt-panoptic-coco	41.4	14.1	7.7	640×640
LightlyTrain	dinov3/vits16-eomt-panoptic-coco	46.8	21.2	23.4	640×640
LightlyTrain	dinov3/vitb16-eomt-panoptic-coco	53.2	39.4	92.5	640×640
LightlyTrain	dinov3/vitl16-eomt-panoptic-coco	57.0	80.1	315.1	640×640
LightlyTrain	dinov3/vitl16-eomt-panoptic-coco-1280	59.0	500.1	315.1	1280×1280
EoMT (CVPR 2025 paper, current SOTA)	dinov3/vitl16-eomt-panoptic-coco-1280	58.9	-	315.1	1280×1280

Training follows the protocol in the original EoMT paper. Tiny models are trained for 360K steps (48 epochs), small and base models for 180K steps (24 epochs) and large models for 90K steps (12 epochs) on the COCO dataset with batch size 16 and learning rate 2e-4. The average latency values were measured with model compilation using torch.compile on a single NVIDIA T4 GPU with FP16 precision.

Train a Panoptic Segmentation Model¶

Training a panoptic 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.

import lightly_train

if __name__ == "__main__":
    lightly_train.train_panoptic_segmentation(
        out="out/my_experiment",
        model="dinov3/vitl16-eomt-panoptic-coco", 
        data={
            "train": {
                "images": "images/train",   # Path to train images
                "masks": "annotations/train", # Path to train mask images
                "annotations": "annotations/train.json", # Path to train COCO-style annotations
            },
            "val": {
                "images": "images/val", # Path to val images
                "masks": "annotations/val", # Path to val mask images
                "annotations": "annotations/val.json", # Path to val COCO-style annotations
            },
        },
    )

During training, the best and last model weights are exported to out/my_experiment/exported_models/, unless disabled in save_checkpoint_args:

best (highest validation PQ): exported_best.pt
last: exported_last.pt

You can use these weights to continue fine-tuning on another dataset by loading the weights with model="<checkpoint path>":

import lightly_train

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

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")
results = model.predict("image.jpg")
results["masks"]    # Masks with (class_label, segment_id) for each pixel, tensor of
                    # shape (height, width, 2). Height and width correspond to the
                    # original image size.
results["segment_ids"]    # Segment ids, tensor of shape (num_segments,).
results["scores"]   # Confidence scores, tensor of shape (num_segments,)

Or use one of the pretrained models directly from LightlyTrain:

import lightly_train

model = lightly_train.load_model("dinov3/vitl16-eomt-panoptic-coco")
results = model.predict("image.jpg")

Visualize the Predictions¶

You can visualize the predicted masks like this:

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

image = read_image("image.jpg")
masks = results["masks"]
segment_ids = results["segment_ids"]
masks = torch.stack([masks[..., 1] == -1] + [masks[..., 1] == segment_id for segment_id in segment_ids])
colors = [(0, 0, 0)] + [[int(color * 255) for color in plt.cm.tab20c(i / len(segment_ids))[:3]] for i in range(len(segment_ids))]
image_with_masks = draw_segmentation_masks(image, masks, colors=colors, alpha=1.0)
plt.imshow(image_with_masks.permute(1, 2, 0))

Data¶

LightlyTrain supports panoptic segmentation datasets in COCO format. Every image must have a corresponding mask image that encodes the segmentation class and segment ID for each pixel. The dataset must also include COCO-style JSON annotation files that define the thing and stuff classes and list the individual segments for each image. See the COCO Panoptic Segmentation format for more details.

The following image formats are supported:

jpg
jpeg
png
ppm
bmp
pgm
tif
tiff
webp

Your dataset directory must be organized like this:

my_data_dir/
├── images
│   ├── train
│   │   ├── image1.jpg
│   │   ├── image2.jpg
│   │   └── ...
│   └── val
│       ├── image1.jpg
│       ├── image2.jpg
│       └── ...
└── annotations
    ├── train
    │   ├── image1.png
    │   ├── image2.png
    │   └── ...
    ├── train.json
    ├── val
    │   ├── image1.png
    │   ├── image2.png
    │   └── ...
    └── val.json

The directories can have any name, as long as the paths are correctly specified in the data argument.

See the Colab notebook for an example dataset and how to set up the data for training.

Model¶

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

DINOv3 Models¶

dinov3/vits16-eomt-panoptic-coco (fine-tuned on COCO)
dinov3/vitb16-eomt-panoptic-coco (fine-tuned on COCO)
dinov3/vitl16-eomt-panoptic-coco (fine-tuned on COCO)
dinov3/vitl16-eomt-panoptic-coco-1280 (fine-tuned on COCO with 1280x1280 input size)
dinov3/vitt16-eomt
dinov3/vitt16plus-eomt
dinov3/vits16-eomt
dinov3/vits16plus-eomt
dinov3/vitb16-eomt
dinov3/vitl16-eomt
dinov3/vitl16plus-eomt
dinov3/vith16plus-eomt
dinov3/vit7b16-eomt

All models are pretrained by Meta and fine-tuned by Lightly, except the vitt models which are pretrained by Lightly.

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

Resume Training¶

There are two distinct ways to continue training, depending on your intention.

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.

Default Image Transform Arguments¶

The following are the default train transform arguments. The validation arguments are automatically inferred from the train arguments.

You can configure the image size and normalization like this:

import lightly_train

if __name__ == "__main__":
    lightly_train.train_panoptic_segmentation(
        out="out/my_experiment",
        model="dinov3/vitl16-eomt-panoptic-coco",
        data={
            # ...
        }
        transform_args={
            "image_size": (1280, 1280),     # (height, width)
            "normalize": {
                "mean": [0.485, 0.456, 0.406],
                "std": [0.229, 0.224, 0.225],
            },
        },
    )

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": (640, 640), # (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.

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.

You can install them with:

pip install "lightly-train[onnx,onnxruntime,onnxslim]"

The following example shows how to export a previously trained model to ONNX.

import lightly_train

# Instantiate the model from a checkpoint.
model = lightly_train.load_model("out/my_experiment/exported_models/exported_best.pt")

# Export to ONNX.
model.export_onnx(
    out="out/my_experiment/exported_models/model.onnx",
    # precision="fp16", # Export model with FP16 weights for smaller size and faster inference.
)

See export_onnx() for all available options when exporting to ONNX.

The following notebook shows how to export a model to ONNX in Colab:

Exporting a Checkpoint to TensorRT¶

TensorRT engines are built from an ONNX representation of the model. The export_tensorrt method internally exports the model to ONNX (see the ONNX export section above) before building a TensorRT engine for fast GPU inference.

Requirements¶

TensorRT is not part of LightlyTrain’s dependencies and must be installed separately. Installation depends on your OS, Python version, GPU, and NVIDIA driver/CUDA setup. See the TensorRT documentation for more details.

On CUDA 12.x systems you can often install the Python package via:

pip install tensorrt-cu12

import lightly_train

# Instantiate the model from a checkpoint.
model = lightly_train.load_model("out/my_experiment/exported_models/exported_best.pt")

# Export to TensorRT from an ONNX file.
model.export_tensorrt(
    out="out/my_experiment/exported_models/model.trt", # TensorRT engine destination.
    # precision="fp16", # Export model with FP16 weights for smaller size and faster inference.
)

See export_tensorrt() for all available options when exporting to TensorRT.

You can also learn more about exporting EoMT to TensorRT using our Colab notebook: