lotus-depth-g-v2-1-disparity
jingheya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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DepthVLM-4B
JonnyYu828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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View details ↗depth-anything-base-hf
LiheYoungDepth 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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View details ↗depth-anything-large-hf
LiheYoungDepth 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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View details ↗depth-anything-small-hf
LiheYoungDepth 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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View details ↗depth_anything_vitb14
LiheYoung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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View details ↗depth_anything_vitl14
LiheYoung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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View details ↗depth_anything_vits14
LiheYoung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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View details ↗depth-anything-v2-small
litert-communityLiteRT/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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View details ↗MoGe-2-LiteRT
litert-community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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View details ↗depth-anything.cpp-gguf
mudlerGGUF 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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c-fast-foundationstereo
NVIDIA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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View details ↗c-foundationstereo-s
NVIDIA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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View details ↗depth-anything-v2-base
onnx-community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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View details ↗depth-anything-v2-large
onnx-community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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View details ↗depth-anything-v2-small
onnx-communityhttps://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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View details ↗depth-anything-v2-small-ONNX
onnx-communityhttps://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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View details ↗metric3d-vit-small
onnx-community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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View details ↗marigold-depth-lcm-v1-0
prs-eth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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View details ↗marigold-depth-v1-0
prs-ethThis 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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View details ↗marigold-depth-v1-1
prs-ethThis 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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View details ↗PaGeR
prs-eth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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View details ↗Depth-Anything
qualcomm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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View details ↗PromptMoGe
sergmister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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