Crate yscv 

YSCV is a pure-Rust computer vision and deep learning framework with SIMD-accelerated tensor operations, image processing, model training, and ONNX inference.

Quick start

use yscv::prelude::*;

// Create a tensor and run a simple operation.
let a = Tensor::from_vec(vec![2, 3], vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).unwrap();
let b = a.scale(2.0);
assert_eq!(b.sum(), 42.0);

// Load an ONNX model and run inference.
// let runner = yscv::onnx::OnnxRunner::load("model.onnx").unwrap();
// let output = runner.run(&[("input", &input_tensor)]).unwrap();

Module overview

Module	Description
tensor	N-dimensional tensor with SIMD-accelerated math
autograd	Automatic differentiation compute graph
optim	Optimizers (SGD, Adam, AdamW, LAMB, RAdam)
kernels	Low-level SIMD/GPU compute kernels
model	Model building, training, data loading, losses
imgproc	Image I/O, filtering, color, morphology, features
video	Video capture and frame processing
detect	Object detection utilities
recognize	Recognition / embedding search
track	Multi-object tracking (Hungarian, etc.)
eval	Evaluation metrics (accuracy, F1, confusion matrix)
onnx	ONNX model loading and inference
cli	Command-line interface helpers

Re-exports

pub use yscv_autograd as autograd;

pub use yscv_cli as cli;

pub use yscv_detect as detect;

pub use yscv_eval as eval;

pub use yscv_imgproc as imgproc;

pub use yscv_kernels as kernels;

pub use yscv_model as model;

pub use yscv_onnx as onnx;

pub use yscv_optim as optim;

pub use yscv_recognize as recognize;

pub use yscv_tensor as tensor;

pub use yscv_track as track;

pub use yscv_video as video;

Modules

prelude

Commonly used types from across the yscv ecosystem.