Depth Anything V2 Metric Outdoor Large Hf

--- library_name: transformers tags: - depth - absolute depth license: apache-2.0 pipeline_tag: depth-estimation --- # Depth Anything V2 (Fine-tuned for Metric Depth Estimation) - Transformers Version This model represents a fine-tuned version of [Depth Anything V2](https://huggingface.co/depth-anything/Depth-Anything-V2-Large-hf) for outdoor metric depth estimation using the synthetic Virtual KITTI datasets. The model checkpoint is compatible with the transformers library. Depth Anything V2 was introduced in [the paper of the same name](https://arxiv.org/abs/2406.09414) by Lihe Yang et al. It uses the same architecture as the original Depth Anything release but employs synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions. This fine-tuned version for metric depth estimation was first released in [this repository](https://github.com/DepthAnything/Depth-Anything-V2). **Six metric depth models** of three scales for indoor and outdoor scenes, respectively, were released and are available: | Base Model | Params | Indoor (Hypersim) | Outdoor (Virtual KITTI 2) | |:-|-:|:-:|:-:| | Depth-Anything-V2-Small | 24.8M | [Model Card](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-Indoor-Small-hf) | [Model Card](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-Outdoor-Small-hf) | | Depth-Anything-V2-Base | 97.5M | [Model Card](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-Indoor-Base-hf) | [Model Card](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-Outdoor-Base-hf) | | Depth-Anything-V2-Large | 335.3M | [Model Card](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-Indoor-Large-hf) | [Model Card](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-Outdoor-Large-hf) | ## Model description Depth Anything V2 leverages the [DPT](https://huggingface.co/docs/transformers/model_doc/dpt) architecture with a [DINOv2](https://huggingface.co/docs/transformers/model_doc/dinov2) backbone. The model is trained on ~600K synthetic labeled images and ~62 million real unlabeled images, obtaining state-of-the-art results for both relative and absolute depth estimation. <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/depth_anything_overview.jpg" alt="drawing" width="600"/> <small> Dep...