Fullstack computer vision engineer specializing in deploying models on edge devices for real-time inference.
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Accelerate inference speed for PyTorch image models using ONNX Runtime and TensorRT optimizations. Achieve up to 123x speedup over the original PyTorch model on CPU. 📅 September 30, 2024 |
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PyTorch at the Edge: Deploying Over 964 TIMM Models on Android with TorchScript and Flutter. Deploy PyTorch models on Android using TIMM, Fastai, TorchScript, and Flutter. Select a model from TIMM's 900+ models, train with Fastai, export to TorchScript, and create an Android app with Flutter for inference. 📅 February 7, 2023 |
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Supercharging YOLOv5: How I Got 182.4 FPS Inference Without a GPU. Optimize YOLOv5 model for CPU inference using Neural Magic's SparseML and DeepSparse. Train on custom data, apply sparsification techniques like pruning and quantization, and achieve up to 180+ FPS on a CPU with only 4 cores. 📅 June 7, 2022 |
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Faster than GPU: How to 10x your Object Detection Model and Deploy on CPU at 50+ FPS. Optimize a YOLOX object detection model deploy on a CPU. Train with custom data, convert to ONNX and OpenVINO IR formats, and apply post-training quantization. This results in a 10x speed improvement, making real-time inference possible on CPU, even outperforming GPU performance. 📅 April 30, 2022 |
Deep Learning Frameworks:
Hyperparameter Optimization:
Experiment Management:
Model Deployment:
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Software Engineering:
Data:
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