The Pipeline API in Hugging Face Transformers is a convenient tool that simplifies the process of performing inference with pre-trained models. It abstracts away the underlying complexities of model loading, input preprocessing, and output postprocessing, allowing users to focus on their specific task.
Note: the pipeline API doesn't leverage a GPU by default, one needs to pass the device argument for that. See the collection of Hugging Face-compatible checkpoints.
Here we load the Depth Anything model which leverages a DINOv2-small backbone. There were also checkpoints released which leverages a base and large backbone, resulting in better performance. We also load the corresponding image processor.
Let's prepare the image for the model using the image processor.
Next we perform a forward pass. As we're at inference time, we use the "torch.no_grad()" operator to save memory (we don't need to compute any gradients).
Finally, let's visualize the results! The opencv-python package has a handy applyColorMap() function which we can leverage.
The "get_depth_any_dino_v2_backbone" function, decorated with @cache, is our key player. This function uses "snapshot_download" to fetch the specific backbone.
The use of @cache is a clever optimization; it ensures that once the model and processor are loaded, they are stored in memory. This significantly speeds up future calls to this function, as it avoids reloading the model and processor from scratch each time, making it ideal for deployments.
modelbitYou can test your REST Endpoint by sending single or batch production images to it for inference.
Use the requests package to POST a request to the API and use json to format the response to print nicely:
⚠️ Replace the "ENTER_WORKSPACE_NAME" placeholder with your Modelbit workspace name.
You can also test your endpoint from the command line using:
⚠️ Replace the "ENTER_WORKSPACE_NAME" placeholder with your Modelbit workspace name.
Depth Anything is a state-of-the-art model in the field of monocular depth estimation, developed to address the challenges associated with understanding 3D structures from single 2D images. This model stands out due to its unique approach to utilizing unlabeled data, significantly enhancing its depth perception capabilities. Unlike traditional models, Depth Anything does not rely on complex new technical modules; instead, it focuses on scaling up datasets and improving data coverage, which in turn reduces generalization errors and enhances model robustness.
Depth Anything has significant applications in fields like autonomous driving, 3D modeling, and augmented reality. Its superior depth estimation capabilities make it particularly useful in scenarios where understanding the spatial layout from a single viewpoint is crucial. Moreover, the model's versatility is highlighted through its improved depth-conditioned ControlNet, making it beneficial for dynamic scene understanding and video editing.
The primary strength of Depth Anything lies in its exceptional ability to perform monocular depth estimation leveraging large-scale unlabeled datasets. This enables the model to achieve state-of-the-art performance in both relative and absolute depth estimations. The model’s training approach and architecture allow it to outperform predecessors significantly in zero-shot evaluations and establish new benchmarks when fine-tuned on specific datasets like NYUv2 and KITTI.
While Depth Anything marks a significant improvement in depth estimation, its reliance on large-scale data might pose challenges in scenarios with limited computational resources or specific privacy constraints. Additionally, while it advances monocular depth estimation, there might be limitations in extremely diverse or novel environments not represented in the training data.
Depth Anything utilizes a semi-supervised learning approach, capitalizing on both labeled and unlabeled data. The model employs a novel training strategy that includes pseudo-labeling of unlabeled images and strong data augmentation techniques. This approach helps in overcoming the limitations of traditional supervised learning methods and enables the model to adapt to a wide variety of visual domains.
