---
comments: true
description: Explore the pseudo-labeled ImageNet depth dataset used for knowledge distillation in YOLO26-Depth. Learn how pseudo depth labels are generated, its role, usage, and pretrained models.
keywords: ImageNet, pseudo-labeled depth, knowledge distillation, monocular depth estimation, Depth Anything, YOLO26-Depth, depth pretraining, Ultralytics
---

# ImageNet (Pseudo-Labeled) Depth Dataset

The [ImageNet](https://www.image-net.org/) (pseudo-labeled depth) dataset reuses the 1,281,167 ImageNet-1K training images, which have no ground-truth depth, and pairs each image with a pseudo depth map produced by a Depth Anything V3 teacher model (monocular and metric). It is used purely for knowledge [distillation](https://www.ultralytics.com/glossary/knowledge-distillation) and for the scene and object diversity that ImageNet provides. As the single largest source in the roughly 2.19M-image YOLO26-Depth pretraining mix (about 58%), adding it was decisive for indoor cross-domain generalization.

## Key Features

- 1,281,167 ImageNet-1K training images covering a very broad range of objects, scenes, and contexts.
- No ground-truth depth: every depth target is a pseudo label generated by a Depth Anything V3 teacher (monocular, metric).
- Used purely for knowledge distillation and scene/object diversity, not as a benchmark.
- The single largest source in the ~2.19M-image pretraining mix (~58%), decisive for indoor cross-domain generalization.
- No validation split — validation is performed only on real ground-truth datasets.

## Dataset Structure

The pseudo-labeled ImageNet source consists of the ImageNet-1K training images with one machine-generated depth map per image:

1. **Training images**: 1,281,167 ImageNet-1K training images, the largest single component (~58%) of the ~2.19M-image YOLO26-Depth pretraining mix.
2. **Validation**: none. This source has no validation split; YOLO26-Depth validation uses only real ground-truth datasets such as NYU Depth V2 and KITTI Eigen.

### How the pseudo-labels are generated

Because ImageNet ships without depth annotations, depth targets are produced offline rather than measured:

1. Each ImageNet training image is run through a pretrained Depth Anything V3 teacher model to predict a per-pixel metric depth map.
2. The teacher prediction is saved as a `.npy` float32 array in meters, following the [Ultralytics depth dataset format](index.md), and paired with its source image by file stem.
3. The pseudo-labeled pairs are then added as one component of a larger training mix, where a student YOLO26-Depth model learns to match the teacher while also training on real ground-truth sources.

This pseudo-labeled source is a component of the internal mixed-training configuration used to pretrain the released YOLO26-Depth weights and is not distributed as a standalone download.

## Role in YOLO26-Depth

This source contributes the bulk of the YOLO26-Depth pretraining data and the widest diversity of scenes and objects. By distilling a strong Depth Anything V3 teacher across more than a million images, it transfers broad scene priors into the student. In practice, adding it was decisive for indoor cross-domain generalization, helping the model perform well on indoor scenes beyond the real ground-truth training distributions.

## Usage

The pseudo-labeled ImageNet data is not trained on in isolation; it is consumed as one component of a combined depth mix, defined by an internal/experiment YAML rather than a public dataset download. Conceptually, training on such a mix looks like the following:

!!! example "Train Example"

    === "Python"

        ```python
        from ultralytics import YOLO

        # Load a pretrained depth model
        model = YOLO("yolo26n-depth.pt")

        # Train on a combined depth mix (your own multi-source dataset YAML)
        results = model.train(data="depth-mix.yaml", epochs=100, imgsz=640)
        ```

    === "CLI"

        ```bash
        # Train on a combined depth mix (your own multi-source dataset YAML)
        yolo depth train data=depth-mix.yaml model=yolo26n-depth.pt epochs=100 imgsz=640
        ```

## Pretrained Models

Pretrained YOLO26-Depth models auto-download from the Ultralytics [v8.4.0 assets release](https://github.com/ultralytics/assets/releases/tag/v8.4.0) when first referenced by name:

- [YOLO26n-Depth](https://platform.ultralytics.com/ultralytics/yolo26/yolo26n-depth)
- [YOLO26s-Depth](https://platform.ultralytics.com/ultralytics/yolo26/yolo26s-depth)
- [YOLO26m-Depth](https://platform.ultralytics.com/ultralytics/yolo26/yolo26m-depth)
- [YOLO26l-Depth](https://platform.ultralytics.com/ultralytics/yolo26/yolo26l-depth)
- [YOLO26x-Depth](https://platform.ultralytics.com/ultralytics/yolo26/yolo26x-depth)

## Citations and Acknowledgments

If you use the ImageNet dataset in your research or development work, please cite the following paper:

!!! quote ""

    === "BibTeX"

        ```bibtex
        @inproceedings{deng2009imagenet,
              title={ImageNet: A Large-Scale Hierarchical Image Database},
              author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
              booktitle={2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
              year={2009},
              organization={IEEE}
        }

        @inproceedings{yang2024depthanything,
              title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data},
              author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
              booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
              year={2024}
        }
        ```

We would like to acknowledge the ImageNet team for creating and maintaining the dataset, and the authors of Depth Anything for the teacher model used to generate the pseudo depth labels.
