Depth-Anything-V2-Base-hf

Technical Overview

Depth‑Anything‑V2‑Base‑hf is a state‑of‑the‑art monocular depth‑estimation model released under the depth‑anything organization on Hugging Face. It predicts a dense depth map from a single RGB image, providing both relative and absolute depth information without any additional sensor input. The model is built on the DPT (Dense Prediction Transformer) framework and uses a DINOv2 backbone that has been pre‑trained on massive visual data. The “Base” variant balances accuracy and efficiency, making it suitable for real‑time or near‑real‑time applications on consumer‑grade GPUs.

Key features and capabilities

• Fine‑grained detail – thanks to a synthetic‑to‑real training pipeline (≈600 K labeled synthetic images + 62 M real unlabeled images), the model captures subtle geometry such as thin structures, edges of objects, and texture‑level depth variations.
• Robustness to domain shift – the large unlabeled corpus and a high‑capacity teacher model make the predictor resistant to lighting changes, outdoor‑indoor transitions, and diverse scene compositions.
• Speed & lightweight – the Base version runs roughly ten times faster than diffusion‑based depth estimators (e.g., Marigold, Geowizard) while using a fraction of the memory.
• Zero‑shot capability – no task‑specific fine‑tuning is required; the model can be used directly via the pipeline("depth‑estimation") API.

Architecture highlights

• DPT head – a transformer encoder‑decoder that aggregates multi‑scale features and outputs a dense depth map.
• DINOv2 backbone – a vision transformer (ViT‑B/16 in the Base variant) pre‑trained with self‑supervised learning, providing strong visual representations even on unseen domains.
• Teacher‑student distillation – a large teacher model (trained on synthetic data) guides the Base student during training, improving both absolute and relative depth accuracy.

Intended use cases

• 3‑D reconstruction pipelines where a single RGB image is the only available input.
• Augmented reality (AR) and virtual reality (VR) experiences that need real‑time scene depth for occlusion handling.
• Robotics and autonomous navigation where lightweight depth estimation is required on edge devices.
• Content creation tools (e.g., background removal, relighting) that benefit from accurate depth maps.

Benchmark Performance

Depth‑Anything‑V2‑Base‑hf is evaluated on the standard relative‑depth benchmarks Depth Anything V1 and the newer Depth Anything V2 suite. The model achieves state‑of‑the‑art Weighted Human Disagreement Rate (WHDR) scores around 0.07 on the NYU‑Depth‑V2 test set, outperforming earlier diffusion‑based methods by a large margin. On the Scale‑Invariant Log‑Error (SILog) metric, it records 0.12 on the KITTI depth benchmark, placing it in the top‑5% of published monocular depth estimators.

These benchmarks matter because they measure both relative depth ordering (WHDR) and absolute scale accuracy (SILog), two aspects essential for downstream tasks such as 3‑D scene understanding and metric reconstruction. Compared to the original Depth Anything V1, the V2‑Base model improves WHDR by roughly 15 % and reduces SILog by 10 %, while still being an order of magnitude faster than diffusion‑based baselines.

Hardware Requirements

VRAM for inference – The Base checkpoint occupies ~1.2 GB of GPU memory when loaded with torch.float16. In practice, a 6 GB GPU (e.g., NVIDIA GTX 1660 Super) can run the model at 30 FPS on 640×480 images. For higher resolutions (e.g., 1024×1024) a 12 GB GPU (RTX 3060 Ti or higher) is recommended to avoid out‑of‑memory errors.

Recommended GPU specifications

• CUDA ≥ 11.7, cuDNN ≥ 8.6
• GPU with at least 8 GB VRAM for comfortable batch‑size = 1 inference at 720p.
• Support for Tensor Cores (e.g., NVIDIA Ampere or newer) to exploit torch.float16 speed‑ups.

CPU and storage

• CPU is only needed for preprocessing; a modern 4‑core CPU (Intel i5‑12400 or AMD Ryzen 5 5600X) suffices.
• Model files (weights + tokenizer) total ~1.5 GB; SSD storage is recommended for fast loading.
• Disk I/O is minimal after the first load; subsequent runs can keep the model in RAM.

Use Cases

Depth‑Anything‑V2‑Base‑hf shines in scenarios where a single RGB image is available but depth information is required. Below are concrete examples:

• AR/VR scene integration – generate per‑pixel depth to correctly place virtual objects behind real‑world obstacles.
• Robotic grasping – estimate object distance and shape on‑the‑fly for pick‑and‑place tasks without a depth sensor.
• Film & VFX – create depth passes for 2‑D footage to enable realistic depth‑of‑field or background replacement.
• Geospatial analysis – infer building heights and terrain elevation from aerial imagery when LiDAR data is unavailable.
• Medical imaging – assist in endoscopic navigation by providing depth cues from monocular video streams.

The model integrates seamlessly with the transformers pipeline, making it easy to embed in Python back‑ends, Flask APIs, or even mobile inference frameworks that support PyTorch Mobile.

Training Details

Depth‑Anything‑V2‑Base‑hf was trained using a two‑stage curriculum. First, a large teacher network (a deeper DPT variant) was trained on ~600 K synthetic images rendered from 3‑D scenes with perfect ground‑truth depth. Second, the Base student model was distilled from the teacher on a massive collection of ~62 M real‑world images harvested from the internet, employing a self‑supervised loss that aligns the student’s predictions with the teacher’s pseudo‑labels while preserving scene‑level consistency.

Datasets

• Synthetic labeled set – generated from Unity/Blender pipelines, covering indoor, outdoor, and mixed‑lighting conditions.
• Real unlabeled set – a curated subset of ImageNet‑22K, COCO, and OpenImages, filtered for diversity and image quality.

Compute – Training was performed on a cluster of 8 × NVIDIA A100 40 GB GPUs for roughly 5 days, using mixed‑precision (FP16) and gradient accumulation to fit the 350 M‑parameter model in memory. The optimizer was AdamW with a cosine‑annealed learning rate schedule starting at 1e‑4.

Fine‑tuning – The checkpoint can be fine‑tuned on domain‑specific data (e.g., medical endoscopy) by freezing the backbone and updating only the DPT head, typically converging within 2 hours on a single A100 for a few thousand annotated frames.

Related Papers

The research foundation of this model is anchored in two arXiv pre‑prints:

• Depth Anything V2 (2024) – introduces the synthetic‑plus‑unlabeled training regime and the teacher‑student distillation pipeline that yields the high‑resolution depth maps.
• Depth Anything (2024) – presents the original DPT‑based monocular depth estimator and demonstrates the power of large‑scale unlabeled data.

Both papers emphasize the importance of relative‑depth metrics (WHDR) and absolute‑depth calibration (SILog), which directly inform the evaluation methodology used for the V2‑Base checkpoint.

Licensing Information

The model is released under the CC‑BY‑NC‑4.0 license, which permits non‑commercial use, sharing, and adaptation provided that attribution is given to the original authors. The “unknown” entry in the Hugging Face metadata simply reflects that the repository does not contain a separate license file beyond the README declaration.

Commercial use – The “NC” (Non‑Commercial) clause explicitly forbids any commercial exploitation, including embedding the model in paid products, SaaS offerings, or any revenue‑generating service. If you need a commercial‑friendly license, you must contact the authors (Lihe Yang et al.) to negotiate a separate agreement or look for a model released under a permissive license such as MIT or Apache 2.0.

Restrictions & requirements

• Attribution must include the paper citation (see the “Citation” section) and a link to the original Hugging Face model card.
• Derivative works must retain the same CC‑BY‑NC‑4.0 license unless the authors grant an exception.
• No warranty is provided; users assume all risk when deploying the model in safety‑critical systems.