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Robust Associations Multi-Pedestrian Tracking using motion and appearance information with camera-motion compensation.

Minimalist ML framework for Rust with a focus on performance and ease of use.
Candle is a minimalist machine learning framework written in Rust, designed for performance and ease of use. It provides GPU support through CUDA and cuDNN, enabling accelerated computations. The framework focuses on simplifying the deployment of machine learning models, particularly large language models (LLMs). Candle's architecture is designed to minimize dependencies and provide a lightweight inference solution. It supports various state-of-the-art models like LLaMA, T5, and Whisper, with examples demonstrating their implementation. Its integration with the Rust ecosystem allows for efficient memory management and low-latency execution, making it suitable for real-time applications and edge deployments. Candle also supports ONNX and WASM, facilitating cross-platform deployment and interoperability. This architecture makes it ideal for applications where speed, efficiency, and control over the runtime environment are critical.
Candle is a minimalist machine learning framework written in Rust, designed for performance and ease of use.
Explore all tools that specialize in text generation. This domain focus ensures Candle delivers optimized results for this specific requirement.
Explore all tools that specialize in speech recognition. This domain focus ensures Candle delivers optimized results for this specific requirement.
Explore all tools that specialize in object detection. This domain focus ensures Candle delivers optimized results for this specific requirement.
Explore all tools that specialize in image segmentation. This domain focus ensures Candle delivers optimized results for this specific requirement.
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Robust Associations Multi-Pedestrian Tracking using motion and appearance information with camera-motion compensation.
Pluggable SOTA multi-object tracking modules for segmentation, object detection, and pose estimation models.

A simple, fast, and strong multi-object tracker that associates every detection box.

AI Inference platform offering developer-friendly APIs for performance and cost-efficiency.

A large-scale street fashion dataset with polygon annotations for computer vision research.

A pure ConvNet model constructed entirely from standard ConvNet modules, designed for the 2020s.