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🚀 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