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README.md

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+---
+license: apache-2.0
+base_model: depth-anything/Depth-Anything-V2-Large
+tags:
+  - robotics
+  - edge-deployment
+  - anima
+  - forge
+  - depth-estimation
+  - monocular-depth
+  - safetensors
+  - vision
+  - ros2
+  - jetson
+  - real-time
+library_name: transformers
+pipeline_tag: depth-estimation
+model-index:
+  - name: depth-anything-v2-large
+    results:
+      - task:
+          type: depth-estimation
+        metrics:
+          - name: Model Size (MB)
+            type: model_size
+            value: 1279
+---
+
+# Depth Anything V2 Large — SafeTensors
+
+> Depth Anything V2 (Large, ViT-L backbone) converted to SafeTensors format for safe, fast loading in robotic depth estimation pipelines. 335M parameters for high-quality monocular depth maps.
+
+This model is part of the **[RobotFlowLabs](https://huggingface.co/robotflowlabs)** model library, built for the **ANIMA** agentic robotics platform — a modular ROS2-native AI system that brings foundation model intelligence to real robots operating in the real world.
+
+## Why This Model Exists
+
+Monocular depth estimation is fundamental to robotic navigation and manipulation — robots need to know how far away things are from a single camera. Depth Anything V2 produces the highest-quality relative depth maps from a single image. The original weights are distributed as raw `.pth` files. We converted them to SafeTensors format for safe, zero-copy memory-mapped loading.
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| **Architecture** | DPT head + ViT-Large encoder |
+| **Parameters** | 335M |
+| **Encoder** | ViT-L/14 (DINOv2-based) |
+| **Input Resolution** | Flexible (recommended 518×518) |
+| **Output** | Dense relative depth map |
+| **Training** | Synthetic + real depth labels (multi-stage) |
+| **Original Model** | [`depth-anything/Depth-Anything-V2-Large`](https://huggingface.co/depth-anything/Depth-Anything-V2-Large) |
+| **License** | Apache-2.0 |
+
+## Included Files
+
+```
+depth-anything-v2-large/
+├── model.safetensors          # 1.3 GB — Full model weights
+└── README.md                  # This file
+```
+
+## Quick Start
+
+```python
+from safetensors.torch import load_file
+import torch
+
+# Load SafeTensors weights
+state_dict = load_file("model.safetensors")
+
+# Load into Depth Anything V2 architecture
+from depth_anything_v2.dpt import DepthAnythingV2
+
+model = DepthAnythingV2(encoder='vitl', features=256, out_channels=[256, 512, 1024, 1024])
+model.load_state_dict(state_dict)
+model.to("cuda").eval()
+
+# Predict depth
+depth = model.infer_image(image)  # Returns relative depth map
+```
+
+### With Transformers
+
+```python
+from transformers import AutoModelForDepthEstimation, AutoImageProcessor
+import torch
+
+processor = AutoImageProcessor.from_pretrained("depth-anything/Depth-Anything-V2-Large")
+model = AutoModelForDepthEstimation.from_pretrained("depth-anything/Depth-Anything-V2-Large")
+model.to("cuda").eval()
+
+inputs = processor(images=image, return_tensors="pt").to("cuda")
+with torch.no_grad():
+    depth = model(**inputs).predicted_depth
+```
+
+### With FORGE (ANIMA Integration)
+
+```python
+from forge.vision import VisionEncoderRegistry
+
+depth_estimator = VisionEncoderRegistry.load("depth-anything-v2-large")
+depth_map = depth_estimator(image_tensor)  # Relative depth map
+```
+
+## Use Cases in ANIMA
+
+Depth estimation is critical across ANIMA modules:
+
+- **Obstacle Avoidance** — Real-time depth maps for safe navigation
+- **Grasp Planning** — Estimate object distance for manipulation reach calculations
+- **3D Reconstruction** — Dense depth for point cloud generation from single camera
+- **Safety Zones** — Distance-based safety boundaries for human-robot collaboration
+- **Path Planning** — Identify traversable spaces and obstacle heights
+
+## Depth Anything V2 Family
+
+| Model | Params | Size | Best For |
+|-------|--------|------|----------|
+| **[depth-anything-v2-large](https://huggingface.co/robotflowlabs/depth-anything-v2-large)** | **335M** | **1.3 GB** | **Highest quality depth** |
+| [depth-anything-v2-small](https://huggingface.co/robotflowlabs/depth-anything-v2-small) | 24.8M | 95 MB | Real-time edge deployment |
+
+## Intended Use
+
+### Designed For
+- Monocular depth estimation for robotic navigation
+- Dense depth maps for manipulation planning
+- Point cloud generation from RGB cameras
+- Obstacle detection and distance estimation
+
+### Limitations
+- Produces relative (not metric) depth — requires calibration for absolute distances
+- Performance degrades on reflective, transparent, or textureless surfaces
+- Single-frame estimation — no temporal consistency for video
+- Inherits biases from training data distribution
+
+### Out of Scope
+- Safety-critical autonomous driving without additional validation
+- Medical depth estimation
+- Surveillance applications
+
+## Attribution
+
+- **Original Model**: [`depth-anything/Depth-Anything-V2-Large`](https://huggingface.co/depth-anything/Depth-Anything-V2-Large) by TUM & HKU
+- **License**: [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)
+- **Paper**: [Depth Anything V2](https://arxiv.org/abs/2406.09414) — Yang et al., 2024
+- **Converted by**: [RobotFlowLabs](https://huggingface.co/robotflowlabs) using [FORGE](https://github.com/robotflowlabs/forge)
+
+## Citation
+
+```bibtex
+@article{yang2024depth_anything_v2,
+  title={Depth Anything V2},
+  author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
+  journal={arXiv preprint arXiv:2406.09414},
+  year={2024}
+}
+```
+
+---
+
+<p align="center">
+  <b>Built with FORGE by <a href="https://huggingface.co/robotflowlabs">RobotFlowLabs</a></b><br>
+  Optimizing foundation models for real robots.
+</p>

model.safetensors

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