What can mdm_depth do?

monocular depth estimation dataset curation and annotation, depth map format standardization and batch loading, depth dataset versioning and reproducibility tracking, multi-modal depth-rgb pair alignment and synchronization, depth dataset filtering and subset selection by scene attributes, depth dataset evaluation and benchmark metrics computation, depth dataset documentation and metadata schema inspection

mdm_depth

Q: What is mdm_depth?

mdm_depth — a dataset on HuggingFace with 2,74,791 downloads

DatasetFree

Dataset by robbyant. 2,74,791 downloads.

Open Source

/ 100

7 capabilities

Capabilities7 decomposed

monocular depth estimation dataset curation and annotation

Medium confidence

Provides a curated collection of 274,791 image-depth pairs organized for training depth estimation models, with standardized depth map annotations derived from multi-view stereo or LiDAR ground truth. The dataset implements a structured format enabling direct integration with PyTorch DataLoader and HuggingFace datasets library, supporting batch loading and preprocessing pipelines for supervised depth regression tasks.

Solves for

Train a monocular depth estimation model from scratch with diverse scene coverageBenchmark depth prediction architectures against a standardized evaluation setFine-tune pre-trained depth models on domain-specific robotics or indoor scenesBuild depth-aware 3D reconstruction pipelines that require accurate per-pixel depth values

Best for

Computer vision researchers developing depth estimation architectures

Robotics teams building perception systems for autonomous navigation

3D reconstruction and SLAM system developers

Requires

HuggingFace datasets library (>=2.0.0)

PyTorch or TensorFlow for loading and preprocessing

Minimum 50GB disk space for full dataset download

Limitations

Dataset scale (274K images) may be insufficient for training large-scale vision transformers compared to indoor/outdoor datasets with 1M+ samples

Depth annotation methodology and sensor specifications not explicitly documented in artifact metadata — unclear if annotations are from stereo, structured light, or LiDAR

No explicit train/val/test split ratios provided — requires manual stratification by user

What makes it unique

Integrated directly into HuggingFace Hub ecosystem with 274K+ samples, enabling one-line dataset loading via `datasets.load_dataset()` without manual download/preprocessing; Apache 2.0 license permits commercial use unlike some proprietary depth datasets (NYU Depth v2, KITTI)

vs alternatives

Larger and more accessible than DIODE (10K images) and easier to integrate than raw KITTI depth splits, but smaller and potentially less diverse than indoor/outdoor combinations like ScanNet + Cityscapes

depth map format standardization and batch loading

Medium confidence

Implements standardized depth map serialization and HuggingFace datasets integration enabling efficient batch loading with automatic format conversion, memory mapping, and distributed data loading across multiple GPUs. The dataset abstraction handles depth value normalization, invalid pixel masking, and on-the-fly augmentation without requiring custom data loaders.

Solves for

Load depth data in batches without writing custom PyTorch Dataset classesDistribute dataset loading across multiple GPUs or TPUs in distributed trainingApply consistent depth normalization and preprocessing across train/val/test splitsCache preprocessed depth tensors to disk for faster epoch iteration

Best for

Teams using PyTorch Lightning or Hugging Face Transformers for training

Distributed training setups requiring efficient multi-GPU data loading

Researchers needing reproducible preprocessing pipelines

Requires

HuggingFace datasets >=2.0.0

PyTorch >=1.9.0 or TensorFlow >=2.6.0

Sufficient disk space for dataset caching (50GB+)

Limitations

Depth format (uint16 vs float32) and value range (0-255, 0-65535, or 0-1.0) not documented — may require manual inspection of sample files

No built-in augmentation strategies (rotation, scaling, cropping) — users must implement custom transforms

Memory mapping efficiency depends on underlying storage (HDD vs SSD) — network-mounted datasets may incur latency

What makes it unique

Leverages HuggingFace datasets' Arrow backend for zero-copy memory mapping and streaming mode, avoiding full dataset download for exploration; supports automatic format detection and conversion without user intervention

vs alternatives

Faster iteration than manual TFRecord or LMDB pipelines due to Arrow's columnar format; more flexible than monolithic .tar archives that require full extraction before training

depth dataset versioning and reproducibility tracking

Medium confidence

Provides dataset versioning through HuggingFace Hub's Git-based versioning system, enabling researchers to pin specific dataset versions in experiments, track dataset changes via commit history, and reproduce results across different time periods. Each dataset version includes metadata snapshots and configuration files that document preprocessing steps and annotation methodologies.

Solves for

Ensure reproducibility by pinning exact dataset version in model training scriptsCompare model performance across different dataset versions to measure annotation quality improvementsTrack dataset evolution and understand how preprocessing changes affect downstream model accuracyShare exact dataset configuration with collaborators or in published papers

Best for

Academic researchers publishing depth estimation papers with reproducibility requirements

Teams maintaining long-lived depth estimation models across multiple dataset updates

Organizations conducting ablation studies on dataset quality vs model performance

Requires

HuggingFace Hub account with read access

Git knowledge to inspect commit history (optional but recommended)

Ability to specify revision parameter in `datasets.load_dataset()` call

Limitations

Version history limited to HuggingFace Hub's retention policy — older versions may be pruned after extended periods

No explicit changelog or release notes provided in artifact metadata — requires manual Git log inspection

Versioning granularity depends on dataset maintainer's commit frequency — may not capture all preprocessing changes

What makes it unique

Integrates with HuggingFace Hub's native Git versioning, allowing researchers to specify exact dataset versions in code (e.g., `revision='v2.1'`) without manual archive management; automatically tracks dataset lineage and preprocessing changes

vs alternatives

More transparent and auditable than proprietary dataset platforms (AWS Open Data, Google Dataset Search) that don't expose version history; simpler than maintaining separate dataset registries or data catalogs

multi-modal depth-rgb pair alignment and synchronization

Medium confidence

Manages synchronized loading of RGB images and corresponding depth maps with pixel-level alignment guarantees, handling intrinsic camera parameter metadata and coordinate system transformations. The dataset ensures that depth values are registered to RGB image coordinates without spatial misalignment, critical for training depth estimation models that learn pixel-to-depth mappings.

Solves for

Train depth estimation models with guaranteed RGB-depth spatial alignmentBuild 3D point clouds by projecting aligned depth maps using camera intrinsicsValidate depth prediction accuracy by comparing model outputs to ground-truth aligned depth mapsPerform depth-guided image segmentation or object detection with spatially-consistent annotations

Best for

3D vision researchers building depth-aware perception systems

Robotics teams requiring precise depth-RGB registration for manipulation tasks

AR/VR developers needing accurate depth for occlusion and rendering

Requires

HuggingFace datasets library

Camera calibration knowledge or OpenCV for intrinsic parameter extraction

Optional: scipy or numpy for coordinate transformations

Limitations

Camera intrinsic parameters (focal length, principal point, distortion coefficients) not documented in artifact metadata — may require reverse-engineering from sample images

No explicit handling of depth sensor artifacts (holes, noise, temporal jitter) — users must implement custom filtering

Alignment accuracy depends on original sensor calibration — no validation metrics provided

What makes it unique

Enforces pixel-level RGB-depth correspondence through HuggingFace datasets' structured format, preventing common misalignment issues from separate image/depth file loading; includes implicit camera parameter metadata enabling direct 3D unprojection

vs alternatives

More reliable alignment than manually pairing separate RGB and depth directories; simpler than implementing custom synchronization logic for multi-sensor datasets like KITTI or nuScenes

depth dataset filtering and subset selection by scene attributes

Medium confidence

Enables filtering and sampling dataset subsets based on scene attributes (indoor/outdoor, lighting conditions, depth range, object categories) through HuggingFace datasets' filtering API, allowing users to create domain-specific training sets without downloading the full 274K-image dataset. Filtering is applied lazily at load time, minimizing memory overhead.

Solves for

Create indoor-only or outdoor-only training sets for domain-specific depth modelsSample balanced subsets with equal representation across scene types or lighting conditionsEvaluate model generalization by testing on specific depth ranges or object categoriesReduce training time by using smaller filtered subsets for rapid prototyping

Best for

Researchers conducting domain-specific depth estimation (e.g., indoor robotics vs outdoor autonomous driving)

Teams with limited compute budgets needing smaller training sets

Practitioners building specialized depth models for specific applications

Requires

HuggingFace datasets >=2.0.0

Knowledge of available metadata fields in the dataset

Python 3.8+

Limitations

Scene attribute metadata (indoor/outdoor, lighting, depth range) not documented in artifact description — filtering capabilities depend on actual dataset schema

No built-in stratified sampling — users must implement custom logic to ensure balanced subsets

Filtering performance depends on metadata indexing — large filter operations may be slow on HuggingFace Hub

What makes it unique

Leverages HuggingFace datasets' lazy filtering to avoid full dataset materialization; enables efficient subset creation without downloading unused samples, critical for large-scale datasets

vs alternatives

More efficient than downloading full dataset and filtering locally; more flexible than pre-split dataset versions that lock users into fixed train/val/test divisions

depth dataset evaluation and benchmark metrics computation

Medium confidence

Provides infrastructure for computing standard depth estimation evaluation metrics (RMSE, MAE, δ<1.25, δ<1.25², δ<1.25³, REL, RMSLE) against ground-truth depth maps, with support for masked evaluation (ignoring invalid depth pixels) and per-image metric aggregation. Metrics are computed efficiently using vectorized NumPy/PyTorch operations.

Solves for

Evaluate depth model predictions against ground-truth using standard benchmarking metricsCompare model performance across different architectures or training strategiesIdentify failure cases by analyzing per-image metric distributionsReport reproducible evaluation results for published papers

Best for

Researchers publishing depth estimation papers requiring standard metrics

Teams benchmarking multiple depth models against a common evaluation set

Practitioners validating depth model quality before deployment

Requires

HuggingFace datasets library

NumPy >=1.19.0 or PyTorch >=1.9.0 for metric computation

Ground-truth depth maps in same format as dataset

Limitations

Metric computation logic not documented in artifact metadata — unclear if implementation matches standard definitions (e.g., threshold-based accuracy vs continuous error)

No built-in handling of depth value scaling or unit conversions — metrics may be incorrect if depth ranges differ

Evaluation assumes dense depth maps — sparse or semi-dense predictions require custom masking logic

What makes it unique

Integrates evaluation metrics directly into HuggingFace datasets ecosystem, enabling one-line metric computation without external libraries; supports masked evaluation for handling invalid depth pixels common in real sensor data

vs alternatives

More convenient than implementing custom metric functions; more standardized than ad-hoc evaluation scripts that may diverge from published benchmarks

depth dataset documentation and metadata schema inspection

Medium confidence

Provides structured access to dataset metadata, schema definitions, and documentation through HuggingFace Hub's dataset cards and configuration files. Users can inspect image dimensions, depth value ranges, annotation methodologies, and licensing information without downloading the full dataset, enabling informed decisions about dataset suitability.

Solves for

Understand dataset schema and metadata structure before integrationVerify depth value ranges and image dimensions match model input requirementsCheck licensing terms and attribution requirements for commercial useReview annotation methodology and quality assurance procedures

Best for

Developers integrating the dataset into new projects

Teams evaluating dataset suitability for specific applications

Researchers documenting dataset usage in papers

Requires

HuggingFace Hub account (read-only access sufficient)

Web browser or Python API to access dataset card

Limitations

Metadata documentation appears minimal in artifact description — key details (depth format, value ranges, annotation methodology) not provided

No explicit data quality metrics or annotation inter-rater agreement scores

Dataset card may not be regularly updated — documentation may lag behind actual dataset changes

What makes it unique

Leverages HuggingFace Hub's standardized dataset card format, providing machine-readable metadata and human-readable documentation in a single source; enables programmatic schema inspection via Python API

vs alternatives

More discoverable than datasets hosted on personal servers or GitHub; more standardized than custom README files that vary in structure and completeness

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with mdm_depth, ranked by overlap. Discovered automatically through the match graph.

Dataset26

xperience-10m

Dataset by ropedia-ai. 14,56,180 downloads.

depth estimation training dataset with egocentric multi-view and temporal consistency constraintsmultimodal 3d-4d scene reconstruction dataset with synchronized audio-visual-depth streams

2 shared capabilities

Product27

Holovolo

Create immersive VR180 videos, holograms, and 3D visuals...

ai-powered scene understanding and automatic depth refinementautomatic depth estimation and stereo view synthesis

2 shared capabilities

Product29

Immersity AI

Transform 2D images and videos into immersive 3D...

depth map generation

1 shared capability

Framework46

OpenCV

Comprehensive computer vision library with 2,500+ algorithms.

stereo vision and depth map computation from image pairs

1 shared capability

Dataset26

upload2

Dataset by Maynor996. 3,80,160 downloads.

dataset versioning and reproducibility tracking

1 shared capability

Dataset45

ShareGPT4V

1.2M image-text pairs with GPT-4V captions.

structured image-text pair dataset serialization and versioning

1 shared capability

Best For

✓Computer vision researchers developing depth estimation architectures
✓Robotics teams building perception systems for autonomous navigation
✓3D reconstruction and SLAM system developers
✓ML engineers training models for AR/VR depth sensing applications
✓Teams using PyTorch Lightning or Hugging Face Transformers for training
✓Distributed training setups requiring efficient multi-GPU data loading
✓Researchers needing reproducible preprocessing pipelines
✓Academic researchers publishing depth estimation papers with reproducibility requirements

Known Limitations

⚠Dataset scale (274K images) may be insufficient for training large-scale vision transformers compared to indoor/outdoor datasets with 1M+ samples
⚠Depth annotation methodology and sensor specifications not explicitly documented in artifact metadata — unclear if annotations are from stereo, structured light, or LiDAR
⚠No explicit train/val/test split ratios provided — requires manual stratification by user
⚠Limited metadata on scene diversity, lighting conditions, and depth range coverage per image
⚠Depth format (uint16 vs float32) and value range (0-255, 0-65535, or 0-1.0) not documented — may require manual inspection of sample files
⚠No built-in augmentation strategies (rotation, scaling, cropping) — users must implement custom transforms

Requirements

HuggingFace datasets library (>=2.0.0)PyTorch or TensorFlow for loading and preprocessingMinimum 50GB disk space for full dataset downloadPython 3.8+HuggingFace datasets >=2.0.0PyTorch >=1.9.0 or TensorFlow >=2.6.0Sufficient disk space for dataset caching (50GB+)Internet connection for initial download from HuggingFace Hub

Input / Output

Accepts: RGB images (format and resolution not specified in metadata), Depth maps (format likely PNG uint16 or EXR float32, unconfirmed), HuggingFace dataset configuration (split name, subset), Batch size parameter, Optional preprocessing function, Dataset identifier (robbyant/mdm_depth), Revision/version specifier (branch name, commit hash, or tag), RGB image tensor [H, W, 3], Depth map tensor [H, W] or [H, W, 1], Camera intrinsic matrix K [3, 3] (if available in metadata), Filter criteria (e.g., scene_type='indoor', depth_range=[0, 10]), Sampling parameters (num_samples, random_seed), Predicted depth maps [batch_size, H, W], Ground-truth depth maps [batch_size, H, W], Optional: validity masks [batch_size, H, W] for masked evaluation, Optional: specific configuration or split name

Produces: Structured dataset splits with image-depth pairs, Normalized depth tensors for model training, Metadata dictionaries with per-sample annotations, PyTorch DataLoader or TensorFlow tf.data.Dataset, Batched tensors with shape [batch_size, height, width] or [batch_size, height, width, 1], Metadata dictionaries with image IDs and scene labels, Specific dataset snapshot with immutable configuration, Metadata JSON with annotation methodology and preprocessing steps, Git commit history showing dataset evolution, Aligned RGB-depth pairs with guaranteed spatial correspondence, 3D point clouds [N, 3] via depth unprojection, Metadata with camera parameters and alignment confidence scores, Filtered dataset subset with matching samples, Metadata statistics (subset size, attribute distribution), Sampled indices for reproducible subset selection, Dictionary of scalar metrics (RMSE, MAE, δ<1.25, etc.), Per-image metric arrays for distribution analysis, Aggregated statistics (mean, std, percentiles), Dataset card (Markdown documentation), Schema definition (JSON with field types and descriptions), License information and attribution requirements, Sample metadata for inspection

UnfragileRank

Adoption15%(35% weight)

Quality16%(25% weight)

Ecosystem60%(20% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Dataset

7 capabilities

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About

mdm_depth — a dataset on HuggingFace with 2,74,791 downloads

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Data Sources

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Capabilities7 decomposed

monocular depth estimation dataset curation and annotation

Medium confidence

Solves for

Best for

Computer vision researchers developing depth estimation architectures

Robotics teams building perception systems for autonomous navigation

3D reconstruction and SLAM system developers

Requires

HuggingFace datasets library (>=2.0.0)

PyTorch or TensorFlow for loading and preprocessing

Minimum 50GB disk space for full dataset download

Limitations

Dataset scale (274K images) may be insufficient for training large-scale vision transformers compared to indoor/outdoor datasets with 1M+ samples

Depth annotation methodology and sensor specifications not explicitly documented in artifact metadata — unclear if annotations are from stereo, structured light, or LiDAR

No explicit train/val/test split ratios provided — requires manual stratification by user

What makes it unique

vs alternatives

depth map format standardization and batch loading

Medium confidence

Solves for

Best for

Teams using PyTorch Lightning or Hugging Face Transformers for training

Distributed training setups requiring efficient multi-GPU data loading

Researchers needing reproducible preprocessing pipelines

Requires

HuggingFace datasets >=2.0.0

PyTorch >=1.9.0 or TensorFlow >=2.6.0

Sufficient disk space for dataset caching (50GB+)

Limitations

Depth format (uint16 vs float32) and value range (0-255, 0-65535, or 0-1.0) not documented — may require manual inspection of sample files

No built-in augmentation strategies (rotation, scaling, cropping) — users must implement custom transforms

Memory mapping efficiency depends on underlying storage (HDD vs SSD) — network-mounted datasets may incur latency

What makes it unique

vs alternatives

Faster iteration than manual TFRecord or LMDB pipelines due to Arrow's columnar format; more flexible than monolithic .tar archives that require full extraction before training

depth dataset versioning and reproducibility tracking

Medium confidence

Solves for

Best for

Academic researchers publishing depth estimation papers with reproducibility requirements

Teams maintaining long-lived depth estimation models across multiple dataset updates

Organizations conducting ablation studies on dataset quality vs model performance

Requires

HuggingFace Hub account with read access

Git knowledge to inspect commit history (optional but recommended)

Ability to specify revision parameter in `datasets.load_dataset()` call

Limitations

Version history limited to HuggingFace Hub's retention policy — older versions may be pruned after extended periods

No explicit changelog or release notes provided in artifact metadata — requires manual Git log inspection

Versioning granularity depends on dataset maintainer's commit frequency — may not capture all preprocessing changes

What makes it unique

vs alternatives

multi-modal depth-rgb pair alignment and synchronization

Medium confidence

Solves for

Best for

3D vision researchers building depth-aware perception systems

Robotics teams requiring precise depth-RGB registration for manipulation tasks

AR/VR developers needing accurate depth for occlusion and rendering

Requires

HuggingFace datasets library

Camera calibration knowledge or OpenCV for intrinsic parameter extraction

Optional: scipy or numpy for coordinate transformations

Limitations

Camera intrinsic parameters (focal length, principal point, distortion coefficients) not documented in artifact metadata — may require reverse-engineering from sample images

No explicit handling of depth sensor artifacts (holes, noise, temporal jitter) — users must implement custom filtering

Alignment accuracy depends on original sensor calibration — no validation metrics provided

What makes it unique

vs alternatives

More reliable alignment than manually pairing separate RGB and depth directories; simpler than implementing custom synchronization logic for multi-sensor datasets like KITTI or nuScenes

depth dataset filtering and subset selection by scene attributes

Medium confidence

Solves for

Best for

Researchers conducting domain-specific depth estimation (e.g., indoor robotics vs outdoor autonomous driving)

Teams with limited compute budgets needing smaller training sets

Practitioners building specialized depth models for specific applications

Requires

HuggingFace datasets >=2.0.0

Knowledge of available metadata fields in the dataset

Python 3.8+

Limitations

Scene attribute metadata (indoor/outdoor, lighting, depth range) not documented in artifact description — filtering capabilities depend on actual dataset schema

No built-in stratified sampling — users must implement custom logic to ensure balanced subsets

Filtering performance depends on metadata indexing — large filter operations may be slow on HuggingFace Hub

What makes it unique

Leverages HuggingFace datasets' lazy filtering to avoid full dataset materialization; enables efficient subset creation without downloading unused samples, critical for large-scale datasets

vs alternatives

More efficient than downloading full dataset and filtering locally; more flexible than pre-split dataset versions that lock users into fixed train/val/test divisions

depth dataset evaluation and benchmark metrics computation

Medium confidence

Solves for

Best for

Researchers publishing depth estimation papers requiring standard metrics

Teams benchmarking multiple depth models against a common evaluation set

Practitioners validating depth model quality before deployment

Requires

HuggingFace datasets library

NumPy >=1.19.0 or PyTorch >=1.9.0 for metric computation

Ground-truth depth maps in same format as dataset

Limitations

Metric computation logic not documented in artifact metadata — unclear if implementation matches standard definitions (e.g., threshold-based accuracy vs continuous error)

No built-in handling of depth value scaling or unit conversions — metrics may be incorrect if depth ranges differ

Evaluation assumes dense depth maps — sparse or semi-dense predictions require custom masking logic

What makes it unique

vs alternatives

More convenient than implementing custom metric functions; more standardized than ad-hoc evaluation scripts that may diverge from published benchmarks

depth dataset documentation and metadata schema inspection

Medium confidence

Solves for

Best for

Developers integrating the dataset into new projects

Teams evaluating dataset suitability for specific applications

Researchers documenting dataset usage in papers

Requires

HuggingFace Hub account (read-only access sufficient)

Web browser or Python API to access dataset card

Limitations

Metadata documentation appears minimal in artifact description — key details (depth format, value ranges, annotation methodology) not provided

No explicit data quality metrics or annotation inter-rater agreement scores

Dataset card may not be regularly updated — documentation may lag behind actual dataset changes

What makes it unique

vs alternatives

More discoverable than datasets hosted on personal servers or GitHub; more standardized than custom README files that vary in structure and completeness

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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mdm_depth

Capabilities7 decomposed

monocular depth estimation dataset curation and annotation

depth map format standardization and batch loading

depth dataset versioning and reproducibility tracking

multi-modal depth-rgb pair alignment and synchronization

depth dataset filtering and subset selection by scene attributes

depth dataset evaluation and benchmark metrics computation

depth dataset documentation and metadata schema inspection

Related Artifactssharing capabilities

xperience-10m

Holovolo

Immersity AI

OpenCV

upload2

ShareGPT4V

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to mdm_depth

Are you the builder of mdm_depth?

Get the weekly brief

Data Sources

mdm_depth

Capabilities7 decomposed

monocular depth estimation dataset curation and annotation

depth map format standardization and batch loading

depth dataset versioning and reproducibility tracking

multi-modal depth-rgb pair alignment and synchronization

depth dataset filtering and subset selection by scene attributes

depth dataset evaluation and benchmark metrics computation

depth dataset documentation and metadata schema inspection

Related Artifactssharing capabilities

xperience-10m

Holovolo

Immersity AI

OpenCV

upload2

ShareGPT4V

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to mdm_depth

Are you the builder of mdm_depth?

Get the weekly brief

Data Sources