#Model Conversion

Bolt, a library for efficient neural network deployment, offers over 15% speed improvement compared to other open-source libraries. It facilitates model conversion from Caffe, ONNX, TFLite, and TensorFlow, supporting multiple inference precisions such as FP32 and 1-BIT. Bolt's compatibility spans various platforms, including ARM and X86 CPUs and GPUs from diverse vendors. It pioneers support for NLP alongside CV applications, featuring advanced graph optimization, efficient thread settings, and algorithm tuning. Extensively used in HUAWEI departments, Bolt is a reliable tool enhancing performance in CV, NLP, and recommendation applications.

The project offers a detailed guide for leveraging ONNX and YOLOv8 in object detection, covering essential requirements and installation procedures, with a focus on GPU compatibility using onnxruntime-gpu for NVIDIA devices. It facilitates model conversion via Google Colab using clear Python scripts. The repository features examples for image, webcam, and video inference, demonstrating the model's versatility and efficiency. This project serves as a valuable resource for developers interested in implementing YOLOv8 in ONNX format, providing practical application insights.

Discover a repository that facilitates effortless inter-conversion of AI models among TensorFlow, PyTorch, ONNX, and other significant frameworks. With support for diverse quantization methods and optimization processes, this project enhances model performance across platforms like EdgeTPU and CoreML. It encourages community contributions for sample codes while keeping you informed on the progress in model conversion techniques for streamlined deployment.

The onnx2tflite project simplifies the conversion of ONNX models to TensorFlow Lite, maintaining high precision and efficiency. It minimizes errors per element to less than 1e-5 compared to ONNX outputs and accelerates model output, achieving speeds 30% faster than other methods. This tool automatically aligns channel formats and supports the deployment of quantized models, including fp16 and uint8. Users can modify input and output layers, add custom operators, and benefit from a straightforward code structure, making it a versatile solution for AI model deployments.