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shriarulmozhivarman
shriarul5273
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updated a Space 2 months ago
shriarul5273/contactless-vitals-demo updated a Space 2 months ago
shriarul5273/Depth-Estimation-Compare-demo posted an update 2 months ago
🚀 I’m excited to release depth-estimation v0.1.2!
This release introduces a complete model-optimization workflow for monocular depth estimation:
✅ ONNX export with optional runtime verification
✅ PyTorch model pruning and sparsity reporting
✅ FP16, BF16, INT8, and ONNX quantization workflows
✅ A practical prune → export → quantize example
✅ Improved GPU and non-square image handling
✅ Safer checkpoint loading and archive extraction
✅ Expanded Python, PyTorch, CodeQL, and pretrained-model testing
Install it from PyPI:
pip install --upgrade depth-estimation
🔗 Explore the project on GitHub: https://github.com/shriarul5273/depth_estimation
📦 Install from PyPI: https://pypi.org/project/depth-estimation/
I’d love to hear how you’re using monocular depth estimation—and what you’d like to see in the next release.
#ComputerVision #DepthEstimation #PyTorch #ONNX #OpenCV #OpenSource #MachineLearning #DeepLearningOrganizations
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updated 2
Spaces 2 months ago
posted an update 2 months ago
Post
95
🚀 I’m excited to release depth-estimation v0.1.2!
This release introduces a complete model-optimization workflow for monocular depth estimation:
✅ ONNX export with optional runtime verification
✅ PyTorch model pruning and sparsity reporting
✅ FP16, BF16, INT8, and ONNX quantization workflows
✅ A practical prune → export → quantize example
✅ Improved GPU and non-square image handling
✅ Safer checkpoint loading and archive extraction
✅ Expanded Python, PyTorch, CodeQL, and pretrained-model testing
Install it from PyPI:
pip install --upgrade depth-estimation
🔗 Explore the project on GitHub: https://github.com/shriarul5273/depth_estimation
📦 Install from PyPI: https://pypi.org/project/depth-estimation/
I’d love to hear how you’re using monocular depth estimation—and what you’d like to see in the next release.
#ComputerVision #DepthEstimation #PyTorch #ONNX #OpenCV #OpenSource #MachineLearning #DeepLearning
This release introduces a complete model-optimization workflow for monocular depth estimation:
✅ ONNX export with optional runtime verification
✅ PyTorch model pruning and sparsity reporting
✅ FP16, BF16, INT8, and ONNX quantization workflows
✅ A practical prune → export → quantize example
✅ Improved GPU and non-square image handling
✅ Safer checkpoint loading and archive extraction
✅ Expanded Python, PyTorch, CodeQL, and pretrained-model testing
Install it from PyPI:
pip install --upgrade depth-estimation
🔗 Explore the project on GitHub: https://github.com/shriarul5273/depth_estimation
📦 Install from PyPI: https://pypi.org/project/depth-estimation/
I’d love to hear how you’re using monocular depth estimation—and what you’d like to see in the next release.
#ComputerVision #DepthEstimation #PyTorch #ONNX #OpenCV #OpenSource #MachineLearning #DeepLearning
updated a
Space 3 months ago
published a
Space 3 months ago
reacted to their post with 🤗 3 months ago
Post
3,039
🚀 Releasing gradio-sync3dcompare v0.0.22 — a Gradio custom component for synchronized 3D model comparison
🔁 One component. Side-by-side. Perfectly in sync.
✨ What's included
🗂️ Supports GLB and PLY files
🔵 Renders as point clouds or native meshes
🎥 Synchronized orbit, zoom, and pan across all viewports
📐 Auto point sizing with manual override
🔍 Configurable zoom range and reset controls
📦 pip install gradio-sync3dcompare
🛠️ Built on Gradio 6.10.0 — drops into any gr.Blocks app with a single import.
🤗 Try the live demo on Hugging Face Spaces: shriarul5273/gradio_sync3d_compare
⭐ GitHub: https://github.com/shriarul5273/Sync3DCompare
🎬 See it in action in the video below.
The video shows a real-world comparison of two 3D point clouds reconstructed from stereo depth estimation — one from FoundationStereo and one from RAFTStereo. Both models are exported as GLB files directly from the depth output and loaded side-by-side into the component. Every orbit, zoom, and pan is perfectly mirrored across both viewports, making it easy to spot structural differences between the two reconstructions at any angle.
💬 Feedback on supported formats, rendering features, or comparison workflows is very welcome!
🔁 One component. Side-by-side. Perfectly in sync.
✨ What's included
🗂️ Supports GLB and PLY files
🔵 Renders as point clouds or native meshes
🎥 Synchronized orbit, zoom, and pan across all viewports
📐 Auto point sizing with manual override
🔍 Configurable zoom range and reset controls
📦 pip install gradio-sync3dcompare
🛠️ Built on Gradio 6.10.0 — drops into any gr.Blocks app with a single import.
🤗 Try the live demo on Hugging Face Spaces: shriarul5273/gradio_sync3d_compare
⭐ GitHub: https://github.com/shriarul5273/Sync3DCompare
🎬 See it in action in the video below.
The video shows a real-world comparison of two 3D point clouds reconstructed from stereo depth estimation — one from FoundationStereo and one from RAFTStereo. Both models are exported as GLB files directly from the depth output and loaded side-by-side into the component. Every orbit, zoom, and pan is perfectly mirrored across both viewports, making it easy to spot structural differences between the two reconstructions at any angle.
💬 Feedback on supported formats, rendering features, or comparison workflows is very welcome!
reacted to their post with 😎 7 months ago
Post
194
Picking the wrong depth estimation model costs more time than most teams realize ⏳
I made a cheat sheet to help you choose between the 28 model variants in the depth_estimation package based on the constraint that actually matters for your use case 📊
Most teams do not need "the best" model ❌
They need the right model for their deployment target, latency budget, and output requirements ✅
Swipe through this before you build another custom preprocessing pipeline 👇
⚡ Fastest inference for edge and CPU deployments:
depth-anything-v2-vits📏 Real metric depth with absolute scale:
zoedepth or depth-pro🎥 Video and real-time streaming with temporal smoothing
🏆 Maximum quality metric predictions:
depth-anything-v3-metric-largeThat is why I open-sourced a library that unifies 12 model families and 28 variants behind one standardized API 🛠️ so you can compare models without rewriting your stack each time.
Save this if you work on depth estimation regularly 💾
Comment with your use case if you want help choosing a model 💬
I'll drop the GitHub repo in the comments 🔗
#DepthEstimation #MonocularDepthEstimation #DepthPrediction #ComputerVision #3DVision #DeepLearning #MachineLearning #AI #PyTorch #OpenSource #EdgeAI #RealTimeAI #MLOps #Robotics
- 1 reply
replied to their post 7 months ago
posted an update 7 months ago
Post
194
Picking the wrong depth estimation model costs more time than most teams realize ⏳
I made a cheat sheet to help you choose between the 28 model variants in the depth_estimation package based on the constraint that actually matters for your use case 📊
Most teams do not need "the best" model ❌
They need the right model for their deployment target, latency budget, and output requirements ✅
Swipe through this before you build another custom preprocessing pipeline 👇
⚡ Fastest inference for edge and CPU deployments:
depth-anything-v2-vits📏 Real metric depth with absolute scale:
zoedepth or depth-pro🎥 Video and real-time streaming with temporal smoothing
🏆 Maximum quality metric predictions:
depth-anything-v3-metric-largeThat is why I open-sourced a library that unifies 12 model families and 28 variants behind one standardized API 🛠️ so you can compare models without rewriting your stack each time.
Save this if you work on depth estimation regularly 💾
Comment with your use case if you want help choosing a model 💬
I'll drop the GitHub repo in the comments 🔗
#DepthEstimation #MonocularDepthEstimation #DepthPrediction #ComputerVision #3DVision #DeepLearning #MachineLearning #AI #PyTorch #OpenSource #EdgeAI #RealTimeAI #MLOps #Robotics
- 1 reply
reacted to their post with 🚀 7 months ago
Post
2,336
🔁 One API. 12 model families. 28 variants. Why depth_estimation makes depth research easier
Switching between depth models usually means rewriting preprocessing, adapting outputs, and dealing with different codebases.
depth_estimation removes that friction.
With the same interface, you can work with:
🌊 Depth Anything
🍎 DepthPro
🧭 MiDaS
📏 ZoeDepth
🧩 MoGe
🛰️ VGGT / OmniVGGT
and more
Change one model string, keep the rest of your workflow the same.
That makes it much easier to:
⚖️ compare models fairly
🧪 prototype quickly
📈 benchmark consistently
🛠️ build reusable depth pipelines
GitHub: https://github.com/shriarul5273/depth_estimation
#depthestimation #research #computervision #python #machinelearning #opensource #pytorch
Switching between depth models usually means rewriting preprocessing, adapting outputs, and dealing with different codebases.
depth_estimation removes that friction.
With the same interface, you can work with:
🌊 Depth Anything
🍎 DepthPro
🧭 MiDaS
📏 ZoeDepth
🧩 MoGe
🛰️ VGGT / OmniVGGT
and more
Change one model string, keep the rest of your workflow the same.
That makes it much easier to:
⚖️ compare models fairly
🧪 prototype quickly
📈 benchmark consistently
🛠️ build reusable depth pipelines
GitHub: https://github.com/shriarul5273/depth_estimation
#depthestimation #research #computervision #python #machinelearning #opensource #pytorch
posted an update 7 months ago
Post
2,336
🔁 One API. 12 model families. 28 variants. Why depth_estimation makes depth research easier
Switching between depth models usually means rewriting preprocessing, adapting outputs, and dealing with different codebases.
depth_estimation removes that friction.
With the same interface, you can work with:
🌊 Depth Anything
🍎 DepthPro
🧭 MiDaS
📏 ZoeDepth
🧩 MoGe
🛰️ VGGT / OmniVGGT
and more
Change one model string, keep the rest of your workflow the same.
That makes it much easier to:
⚖️ compare models fairly
🧪 prototype quickly
📈 benchmark consistently
🛠️ build reusable depth pipelines
GitHub: https://github.com/shriarul5273/depth_estimation
#depthestimation #research #computervision #python #machinelearning #opensource #pytorch
Switching between depth models usually means rewriting preprocessing, adapting outputs, and dealing with different codebases.
depth_estimation removes that friction.
With the same interface, you can work with:
🌊 Depth Anything
🍎 DepthPro
🧭 MiDaS
📏 ZoeDepth
🧩 MoGe
🛰️ VGGT / OmniVGGT
and more
Change one model string, keep the rest of your workflow the same.
That makes it much easier to:
⚖️ compare models fairly
🧪 prototype quickly
📈 benchmark consistently
🛠️ build reusable depth pipelines
GitHub: https://github.com/shriarul5273/depth_estimation
#depthestimation #research #computervision #python #machinelearning #opensource #pytorch
reacted to their post with 🚀 7 months ago
Post
3,039
🚀 Releasing gradio-sync3dcompare v0.0.22 — a Gradio custom component for synchronized 3D model comparison
🔁 One component. Side-by-side. Perfectly in sync.
✨ What's included
🗂️ Supports GLB and PLY files
🔵 Renders as point clouds or native meshes
🎥 Synchronized orbit, zoom, and pan across all viewports
📐 Auto point sizing with manual override
🔍 Configurable zoom range and reset controls
📦 pip install gradio-sync3dcompare
🛠️ Built on Gradio 6.10.0 — drops into any gr.Blocks app with a single import.
🤗 Try the live demo on Hugging Face Spaces: shriarul5273/gradio_sync3d_compare
⭐ GitHub: https://github.com/shriarul5273/Sync3DCompare
🎬 See it in action in the video below.
The video shows a real-world comparison of two 3D point clouds reconstructed from stereo depth estimation — one from FoundationStereo and one from RAFTStereo. Both models are exported as GLB files directly from the depth output and loaded side-by-side into the component. Every orbit, zoom, and pan is perfectly mirrored across both viewports, making it easy to spot structural differences between the two reconstructions at any angle.
💬 Feedback on supported formats, rendering features, or comparison workflows is very welcome!
🔁 One component. Side-by-side. Perfectly in sync.
✨ What's included
🗂️ Supports GLB and PLY files
🔵 Renders as point clouds or native meshes
🎥 Synchronized orbit, zoom, and pan across all viewports
📐 Auto point sizing with manual override
🔍 Configurable zoom range and reset controls
📦 pip install gradio-sync3dcompare
🛠️ Built on Gradio 6.10.0 — drops into any gr.Blocks app with a single import.
🤗 Try the live demo on Hugging Face Spaces: shriarul5273/gradio_sync3d_compare
⭐ GitHub: https://github.com/shriarul5273/Sync3DCompare
🎬 See it in action in the video below.
The video shows a real-world comparison of two 3D point clouds reconstructed from stereo depth estimation — one from FoundationStereo and one from RAFTStereo. Both models are exported as GLB files directly from the depth output and loaded side-by-side into the component. Every orbit, zoom, and pan is perfectly mirrored across both viewports, making it easy to spot structural differences between the two reconstructions at any angle.
💬 Feedback on supported formats, rendering features, or comparison workflows is very welcome!
reacted to their post with 👍 7 months ago
Post
148
🚀 Releasing depth-estimation v0.1.1 — a unified Python library for monocular depth estimation
One API. 12 model families. 28 variants. The full depth workflow in a single pip install.
Switch between Depth Anything, DepthPro, MoGe, MiDaS, ZoeDepth, VGGT, and more by changing a single string — same preprocessing, same output format, every time.
What's new in v0.1.1 🎨
Introducing depth_estimation.viz — a dedicated visualization toolkit:
🖼️ Display depth maps with show_depth()
🔲 Compare models side-by-side with compare_depths()
🎭 Blend depth over RGB with overlay_depth()
🔴🔵 Generate stereoscopic anaglyphs with create_anaglyph()
🌀 Export rotating 3D surface animations with animate_3d()
🗺️ Inspect per-pixel error heatmaps with plot_error_map()
And since v0.1.0 🎬
Stream depth from video files, webcams, or frame sequences — with EMA temporal smoothing and side-by-side MP4 export.
The full workflow in one place:
✅ Inference (image, batch, CLI)
✅ Video & streaming
✅ Visualization
✅ Fine-tuning with DepthTrainer
✅ Evaluation on NYU, KITTI, DIODE
✅ Dataset loading (auto-download or custom folders)
pip install depth-estimation
🤗 Try the live demo: shriarul5273/Depth-Estimation-Compare-demo
⭐ GitHub: https://github.com/shriarul5273/depth_estimation
Would love to hear what models or features you'd like to see next!
#depthestimation #computervision #deeplearning #python #opensource #pytorch #machinelearning #AI #3Dvision #robotics #autonomousdriving #computationalphotography #imageprocessing #SLAM #pointcloud #stereovisison #neuralnetworks #MLOps #research #huggingface
One API. 12 model families. 28 variants. The full depth workflow in a single pip install.
Switch between Depth Anything, DepthPro, MoGe, MiDaS, ZoeDepth, VGGT, and more by changing a single string — same preprocessing, same output format, every time.
What's new in v0.1.1 🎨
Introducing depth_estimation.viz — a dedicated visualization toolkit:
🖼️ Display depth maps with show_depth()
🔲 Compare models side-by-side with compare_depths()
🎭 Blend depth over RGB with overlay_depth()
🔴🔵 Generate stereoscopic anaglyphs with create_anaglyph()
🌀 Export rotating 3D surface animations with animate_3d()
🗺️ Inspect per-pixel error heatmaps with plot_error_map()
And since v0.1.0 🎬
Stream depth from video files, webcams, or frame sequences — with EMA temporal smoothing and side-by-side MP4 export.
The full workflow in one place:
✅ Inference (image, batch, CLI)
✅ Video & streaming
✅ Visualization
✅ Fine-tuning with DepthTrainer
✅ Evaluation on NYU, KITTI, DIODE
✅ Dataset loading (auto-download or custom folders)
pip install depth-estimation
🤗 Try the live demo: shriarul5273/Depth-Estimation-Compare-demo
⭐ GitHub: https://github.com/shriarul5273/depth_estimation
Would love to hear what models or features you'd like to see next!
#depthestimation #computervision #deeplearning #python #opensource #pytorch #machinelearning #AI #3Dvision #robotics #autonomousdriving #computationalphotography #imageprocessing #SLAM #pointcloud #stereovisison #neuralnetworks #MLOps #research #huggingface
posted an update 7 months ago
Post
3,039
🚀 Releasing gradio-sync3dcompare v0.0.22 — a Gradio custom component for synchronized 3D model comparison
🔁 One component. Side-by-side. Perfectly in sync.
✨ What's included
🗂️ Supports GLB and PLY files
🔵 Renders as point clouds or native meshes
🎥 Synchronized orbit, zoom, and pan across all viewports
📐 Auto point sizing with manual override
🔍 Configurable zoom range and reset controls
📦 pip install gradio-sync3dcompare
🛠️ Built on Gradio 6.10.0 — drops into any gr.Blocks app with a single import.
🤗 Try the live demo on Hugging Face Spaces: shriarul5273/gradio_sync3d_compare
⭐ GitHub: https://github.com/shriarul5273/Sync3DCompare
🎬 See it in action in the video below.
The video shows a real-world comparison of two 3D point clouds reconstructed from stereo depth estimation — one from FoundationStereo and one from RAFTStereo. Both models are exported as GLB files directly from the depth output and loaded side-by-side into the component. Every orbit, zoom, and pan is perfectly mirrored across both viewports, making it easy to spot structural differences between the two reconstructions at any angle.
💬 Feedback on supported formats, rendering features, or comparison workflows is very welcome!
🔁 One component. Side-by-side. Perfectly in sync.
✨ What's included
🗂️ Supports GLB and PLY files
🔵 Renders as point clouds or native meshes
🎥 Synchronized orbit, zoom, and pan across all viewports
📐 Auto point sizing with manual override
🔍 Configurable zoom range and reset controls
📦 pip install gradio-sync3dcompare
🛠️ Built on Gradio 6.10.0 — drops into any gr.Blocks app with a single import.
🤗 Try the live demo on Hugging Face Spaces: shriarul5273/gradio_sync3d_compare
⭐ GitHub: https://github.com/shriarul5273/Sync3DCompare
🎬 See it in action in the video below.
The video shows a real-world comparison of two 3D point clouds reconstructed from stereo depth estimation — one from FoundationStereo and one from RAFTStereo. Both models are exported as GLB files directly from the depth output and loaded side-by-side into the component. Every orbit, zoom, and pan is perfectly mirrored across both viewports, making it easy to spot structural differences between the two reconstructions at any angle.
💬 Feedback on supported formats, rendering features, or comparison workflows is very welcome!
updated a
Space 7 months ago
published a
Space 7 months ago
posted an update 7 months ago
Post
148
🚀 Releasing depth-estimation v0.1.1 — a unified Python library for monocular depth estimation
One API. 12 model families. 28 variants. The full depth workflow in a single pip install.
Switch between Depth Anything, DepthPro, MoGe, MiDaS, ZoeDepth, VGGT, and more by changing a single string — same preprocessing, same output format, every time.
What's new in v0.1.1 🎨
Introducing depth_estimation.viz — a dedicated visualization toolkit:
🖼️ Display depth maps with show_depth()
🔲 Compare models side-by-side with compare_depths()
🎭 Blend depth over RGB with overlay_depth()
🔴🔵 Generate stereoscopic anaglyphs with create_anaglyph()
🌀 Export rotating 3D surface animations with animate_3d()
🗺️ Inspect per-pixel error heatmaps with plot_error_map()
And since v0.1.0 🎬
Stream depth from video files, webcams, or frame sequences — with EMA temporal smoothing and side-by-side MP4 export.
The full workflow in one place:
✅ Inference (image, batch, CLI)
✅ Video & streaming
✅ Visualization
✅ Fine-tuning with DepthTrainer
✅ Evaluation on NYU, KITTI, DIODE
✅ Dataset loading (auto-download or custom folders)
pip install depth-estimation
🤗 Try the live demo: shriarul5273/Depth-Estimation-Compare-demo
⭐ GitHub: https://github.com/shriarul5273/depth_estimation
Would love to hear what models or features you'd like to see next!
#depthestimation #computervision #deeplearning #python #opensource #pytorch #machinelearning #AI #3Dvision #robotics #autonomousdriving #computationalphotography #imageprocessing #SLAM #pointcloud #stereovisison #neuralnetworks #MLOps #research #huggingface
One API. 12 model families. 28 variants. The full depth workflow in a single pip install.
Switch between Depth Anything, DepthPro, MoGe, MiDaS, ZoeDepth, VGGT, and more by changing a single string — same preprocessing, same output format, every time.
What's new in v0.1.1 🎨
Introducing depth_estimation.viz — a dedicated visualization toolkit:
🖼️ Display depth maps with show_depth()
🔲 Compare models side-by-side with compare_depths()
🎭 Blend depth over RGB with overlay_depth()
🔴🔵 Generate stereoscopic anaglyphs with create_anaglyph()
🌀 Export rotating 3D surface animations with animate_3d()
🗺️ Inspect per-pixel error heatmaps with plot_error_map()
And since v0.1.0 🎬
Stream depth from video files, webcams, or frame sequences — with EMA temporal smoothing and side-by-side MP4 export.
The full workflow in one place:
✅ Inference (image, batch, CLI)
✅ Video & streaming
✅ Visualization
✅ Fine-tuning with DepthTrainer
✅ Evaluation on NYU, KITTI, DIODE
✅ Dataset loading (auto-download or custom folders)
pip install depth-estimation
🤗 Try the live demo: shriarul5273/Depth-Estimation-Compare-demo
⭐ GitHub: https://github.com/shriarul5273/depth_estimation
Would love to hear what models or features you'd like to see next!
#depthestimation #computervision #deeplearning #python #opensource #pytorch #machinelearning #AI #3Dvision #robotics #autonomousdriving #computationalphotography #imageprocessing #SLAM #pointcloud #stereovisison #neuralnetworks #MLOps #research #huggingface
updated a
model 7 months ago
published a
model 7 months ago
updated a
model 7 months ago