Depth Anything Leverages Large-Scale Unlabeled Data for Improved Results

Depth anything is a powerful tool that can be used to create highly accurate models by leveraging large-scale unlabeled data. This approach has been shown to significantly outperform traditional methods in various applications.

One of the key benefits of using large-scale unlabeled data is that it allows models to learn from a vast amount of information, which can lead to improved accuracy and robustness. This is especially true in tasks such as image classification, where models can learn to recognize patterns and features from a massive dataset.

According to research, using large-scale unlabeled data can increase model accuracy by up to 20% in certain applications. This is a significant improvement, and it highlights the potential of this approach.

By leveraging large-scale unlabeled data, depth anything can be used to create models that are more accurate, more robust, and more efficient.

If this caught your attention, see: Scale Ai Data Labeling

Early works on monocular depth estimation relied heavily on handcrafted features and traditional computer vision techniques, but they struggled to handle complex scenes with occlusions and textureless regions.

These traditional methods were limited by their reliance on explicit depth cues, which made them less effective in real-world scenarios.

Deep learning-based methods have revolutionized monocular depth estimation, effectively learning depth representations from annotated datasets.

Some pioneering works explored the direction of zero-shot depth estimation by collecting more training images, but their supervision was very sparse and only enforced on limited pairs of points.

MiDaS is a milestone work that utilizes an affine-invariant loss to ignore the potentially different depth scales and shifts across varying datasets, providing relative depth information.

Recent works have taken a step further to estimate the metric depth, but they exhibit poorer generalization ability than MiDaS, especially its latest version.

Leveraging unlabeled data is a crucial aspect of semi-supervised learning, which has been popular in various applications, but existing works rarely consider the challenging scenario of large-scale unlabeled images.

We take this challenging direction for zero-shot MDE, demonstrating that unlabeled images can significantly enhance data coverage and improve model generalization and robustness.

Collecting a vast amount of data is crucial for training a reliable annotation tool, and the authors of Depth Anything gathered ~62M diverse unlabeled images from 8 public datasets.

This data was combined with ~1.5M labeled images from 6 public datasets to train a teacher MDE model.

The sheer scale of the unlabeled data used in Depth Anything is impressive, with over 62 million images.

The use of unlabeled data allows the model to learn patterns and relationships that may not be apparent in smaller, more curated datasets.

By leveraging the power of large-scale unlabeled data, Depth Anything achieves state-of-the-art performance in monocular depth estimation.

Here are some key statistics about the data used in Depth Anything:

Note that the specific datasets used are not listed in this table, but they are mentioned in the article section facts as 8 public datasets for unlabeled images and 6 public datasets for labeled images.

We adopt the DINOv2 encoder for feature extraction, which is a key component of our approach. This encoder is used in conjunction with the DPT decoder for depth regression, as seen in the implementation details.

The labeled datasets are combined together without re-sampling, allowing us to take advantage of the large-scale unlabeled data. In the first stage of training, we train a teacher model on labeled images for 20 epochs.

A ratio of 1:2 is set for labeled and unlabeled images in each batch, ensuring that the model is exposed to a diverse range of data. The base learning rate of the pre-trained encoder is set as 5e-6, while the randomly initialized decoder uses a 10× larger learning rate.

Additional reading: Model Drift vs Data Drift

Our Depth Anything model can be fine-tuned to perform metric depth estimation, a significant improvement over traditional methods.

By leveraging the MiDaS pre-trained encoder, we can fine-tune our model to achieve better results on a wide range of unseen datasets.

We follow ZoeDepth's approach, which fine-tunes the MiDaS pre-trained encoder with metric depth information from NYUv2 or KITTI.

The results are impressive, with our Depth Anything model surpassing the original ZoeDepth based on MiDaS across a wide range of unseen datasets.

Here's a comparison of our results with other state-of-the-art models on various datasets:

Our model achieves the best results on most datasets, demonstrating its superiority over other state-of-the-art models.

We adopt the DINOv2 encoder for feature extraction, which is a key component of our implementation.

The DINOv2 encoder is used in conjunction with the DPT decoder for depth regression, following the approach taken by MiDaS.

All labeled datasets are combined together without re-sampling, allowing us to work with a unified set of data.

In the first stage, we train a teacher model on labeled images for 20 epochs, which helps to establish a strong foundation for our implementation.

The base learning rate of the pre-trained encoder is set as 5e-6, which is a relatively low value that helps to prevent overfitting.

We use the AdamW optimizer and decay the learning rate with a linear schedule, which helps to adapt to the changing nature of the data.

The ratio of labeled and unlabeled images is set as 1:2 in each batch, which allows us to balance the influence of each type of data.

We only apply horizontal flipping as our data augmentation for labeled images, which is a simple yet effective technique for improving robustness.

The tolerance margin α for feature alignment loss is set as 0.15, which is a key hyperparameter that requires careful tuning.

In the second stage of joint training, we train a student model to sweep across all unlabeled images for one time, which helps to leverage the benefits of self-supervised learning.

To start implementing Depth Anything for monocular depth estimation, you'll need to set up your development environment. First, make sure you have Poetry installed on your computer.

You'll then need to clone the project repository using the command in the terminal. This will bring the entire project onto your local machine.

Next, access the project directory where you cloned the repository. This is where you'll find all the necessary files to get started.

If you're new to Poetry, you'll need to initialize the environment. This will help Poetry understand your project's dependencies.

DeepLab V3 is a powerful tool for image segmentation, capable of segmenting the pixels of a camera frame or image into a predefined set of classes. This can be incredibly useful for applications like object detection and image analysis.

The source code for DeepLab V3 is available on GitHub, which is a great resource for developers looking to work with this model. The source code is likely to be well-maintained and up-to-date, making it easier to integrate into your own projects.

One of the key components of DeepLab V3 is the MobileNetV2 architecture, which is a popular choice for mobile and embedded applications. This architecture is known for its efficiency and speed, making it well-suited for real-time applications.

DeepLab V3 is available in three different sizes: DeepLabV3.mlmodel (8.6MB), DeepLabV3FP16.mlmodel (4.3MB), and DeepLabV3Int8LUT.mlmodel (2.3MB). These different sizes can be useful for applications with varying memory constraints.

Here are the available DeepLab V3 models, along with their sizes and download options: