dpt-large
Maintainer: Intel
158
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The dpt-large model, also known as MiDaS 3.0, is a Dense Prediction Transformer (DPT) model trained by Intel on 1.4 million images for monocular depth estimation. The DPT model uses the Vision Transformer (ViT) as its backbone and adds a neck and head on top for the depth estimation task. This model was introduced in the paper Vision Transformers for Dense Prediction by Ranftl et al. (2021). The model card was written in collaboration between the Hugging Face team and Intel.
The dpt-large model is similar to other object detection and depth estimation models like the detr-resnet-50 model from Facebook, which also uses a transformer-based architecture for object detection. However, the dpt-large model is specifically focused on the task of monocular depth estimation.
Model inputs and outputs
Inputs
- RGB image
Outputs
- Depth estimation map for the input image
Capabilities
The dpt-large model is capable of performing zero-shot monocular depth estimation on input images. This means you can use the raw pre-trained model to predict depth maps without any fine-tuning. The model has been trained on a large dataset of 1.4 million images, giving it the ability to generalize to a wide variety of scenes and objects.
What can I use it for?
You can use the dpt-large model for various applications that require monocular depth estimation, such as:
- 3D scene reconstruction
- Augmented reality and virtual reality
- Autonomous driving and robotics
- Computational photography
The model can be fine-tuned on specific datasets or tasks to further improve its performance for your particular use case. You can find fine-tuned versions of the dpt-large model on the Hugging Face model hub.
Things to try
One interesting thing to try with the dpt-large model is to compare its performance on different types of scenes and objects. For example, you could try depth estimation on indoor scenes, outdoor landscapes, and images with a variety of objects and textures. This can help you understand the model's strengths and limitations, and identify areas where further fine-tuning or model improvements may be beneficial.
Another interesting experiment would be to combine the dpt-large model with other computer vision models, such as object detection or semantic segmentation, to create more comprehensive scene understanding pipelines. The depth information provided by the dpt-large model could be a valuable input for these downstream tasks.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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