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Depth Anything model. It was introduced in the paper Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lihe Yang et al. and first released in this repository. Disclaimer: The team releasing Depth Anything did not write a model card for this model so this model card has been written by the Hugging Face team. Depth Anything leverages the DPT architecture with a DINOv2 backbone.
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Depth Anything model. It was introduced in the paper Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lihe Yang et al. and first released in this repository. Disclaimer: The team releasing Depth Anything did not write a model card for this model so this model card has been written by the Hugging Face team. Depth Anything leverages the DPT architecture with a DINOv2 backbone.
번역 대기 중 · 원문 표시
Depth Anything model. It was introduced in the paper Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lihe Yang et al. and first released in this repository. Disclaimer: The team releasing Depth Anything did not write a model card for this model so this model card has been written by the Hugging Face team. Depth Anything leverages the DPT architecture with a DINOv2 backbone.
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LiteRT/TFLite exports of Depth Anything V2 Small, including the complete depth decoder. This model estimates a relative-depth map from a single RGB image. Its outputs are not calibrated distances in meters. The source has a DINOv2 Small backbone and a DPT decoder, with approximately 24.8 million parameters. These files are based on the official Transformers checkpoint at revision `5426e4f0f36572d16453bbda7a8389317b1bef99`. The INT8 variant uses AI Edge Quantizer's `dynamic_wi8_afp32()` recipe. Supported weight tensors are quantized; the model is not fully integer, and floating-point tensors remain. No calibration images are required. Both files have the same input and output contract.
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GGUF conversions of ByteDance Depth Anything 3, for use with **depth-anything.cpp** — a from-scratch C++17 / ggml port. No Python, no PyTorch, no CUDA toolkit at inference: one self-contained GGUF file plus a small native library and CLI, **faster than PyTorch on CPU** and **bit-exact** against the original (correlation 1.0, verified component by component). Given an image, the engine recovers a dense **depth** map, per-pixel **confidence**, camera **extrinsics (3×4)** and **intrinsics (3×3)**, an optional **sky** mask, a back-projected **3D point cloud**, and exports to **glb / COLMAP / PLY**. Each GGUF is fully self-contained — every dimension, hyperparameter and preprocessing constant is baked into the file; the loader reads them, nothing is hardcoded.
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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https://huggingface.co/depth-anything/Depth-Anything-V2-Small with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
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https://huggingface.co/depth-anything/Depth-Anything-V2-Small with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
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This is a model card for the `marigold-depth-v1-0` model for monocular depth estimation from a single image. The model is fine-tuned from the `stable-diffusion-2` model as described in our papers: - CVPR'2024 paper titled "Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation" - Journal extension titled "Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis"
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This is a model card for the `marigold-depth-v1-1` model for monocular depth estimation from a single image. The model is fine-tuned from the `stable-diffusion-2` model as described in our papers: - CVPR'2024 paper titled "Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation" - Journal extension titled "Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis"
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Global-Local Path Networks (GLPN) model trained on KITTI for monocular depth estimation. It was introduced in the paper Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth by Kim et al. and first released in this repository. Disclaimer: The team releasing GLPN did not write a model card for this model so this model card has been written by the Hugging Face team. You can use the raw model for monocular depth estimation. See the model hub to look for fine-tuned versions on a task that interests you.
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