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Rust-native framework for Edge AI CV inference.

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> **Status: work in progress.** Usable today for small inputs and specific
> models. The self-contained crates — `yscv-tensor`, `yscv-kernels`,
> `yscv-imgproc` — stand on their own already. Dropping the WIP label is the
> 0.2.0 milestone. Found a problem? Open an issue, or reach me via Telegram
> from my GitHub page.

# yscv
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A complete computer vision and deep learning framework in pure Rust. One `cargo add yscv` gives you image processing (175 ops), neural network training (39 layer types, 8 optimizers), ONNX inference (122 operators, INT4/INT8 quantization), LLM generation (KV-cache, RoPE, GQA), real-time detection + tracking + recognition, H.264/HEVC/AV1 video decoding, hardware decode (VideoToolbox/VAAPI/NVDEC/MediaFoundation), and GPU compute via Vulkan/Metal/DX12 — all in a single statically-linked binary with zero Python or C++ dependencies.

> Project focus. YSCV is built for CPU inference on edge devices — Raspberry Pi, Rockchip / Allwinner SBCs, drone boards, factory PCs, anything ARM Cortex-A or low-power x86. The north star is a **drop-in replacement for ONNX Runtime's CPU execution provider**: load an ONNX model, call run, and a single crate auto-detects the best path for the host — no execution-provider wiring, no backend selection, no build-time target pinning. Hot paths are hand-tuned SIMD (NEON / AVX / SSE / scalar) with rayon multi-thread fork-join, selected at runtime by detected ISA, and — increasingly — by detected **microarchitecture** (see [`docs/microarch-dispatch.md`](docs/microarch-dispatch.md) for the vision and the dispatch roadmap). On a public Siamese tracker we are within ~7% of ORT-CPU single-thread on x86 and faster than ORT on ARM SBCs and Apple M1; **the CPU benchmarks (x86 / ARM / Apple M1) and the M1 GPU path (MPSGraph vs CoreML) are freshly measured on current hardware** (the YOLO / video Metal sections are still pending re-measurement) — see [`docs/performance-benchmarks.md`](docs/performance-benchmarks.md). Other backends — wgpu cross-platform GPU, Apple MPSGraph, Rockchip RKNN NPU, optional BLAS — exist as opt-in features and keep getting wider, but they're not the headline target. PRs are welcome; see [`CONTRIBUTING.md`](CONTRIBUTING.md).
>
> Agent-friendly documentation. YSCV is structured so that an AI coding agent can wire it into a downstream project end-to-end without prior context: every crate has a focused `README.md` describing its surface, [`docs/cookbook.md`](docs/cookbook.md) has recipes per task, [`docs/feature-flags.md`](docs/feature-flags.md) is exhaustive on Cargo features and runtime env knobs, [`AGENTS.md`](AGENTS.md) has the workflow + style rules verbatim, and per-op profile labels (`YSCV_RUNNER_PROFILE=path` dumps fused-path JSON) make hot-path issues self-diagnosing. The benefit is downstream: agents can build working code on top of yscv quickly, not the other way around. Responsibility for any PR — including patches drafted by an agent — rests with the human author submitting it.

> **First time here?** → **[QUICKSTART](QUICKSTART.md)** (5 minutes to a running program) · **[Tutorial](docs/getting-started.md)** (full walkthrough) · **[Cookbook](docs/cookbook.md)** (recipes by task) · **[Feature flags](docs/feature-flags.md)** (what to enable for your target) · **[Edge / Rockchip](docs/edge-deployment.md)** (NPU deployment) · **[Examples](examples/README.md)** (worked code) · **[Troubleshooting](docs/troubleshooting.md)** (when things break) · **[Docs hub](docs/README.md)** (everything else)

We built this because deploying ML in production shouldn't require Docker containers with PyTorch, CUDA drivers, and a prayer. YSCV compiles to one binary that runs on a Raspberry Pi, a cloud VM, or a factory floor computer. Every hot path has hand-tuned SIMD for ARM and x86 — 570 `#[target_feature]`-gated functions selected by runtime CPU detection, so one binary picks the best path for the host it lands on.

## Quick Start

```toml
[dependencies]
yscv = "0.1.11"
```

Load an image, process it, save the result — three lines:

```rust,ignore
use yscv::prelude::*;

let img = imread("photo.jpg")?;
let gray = rgb_to_grayscale(&img)?;
imwrite("gray.png", &gray)?;
```

## Training a model

```rust,ignore
use yscv::prelude::*;

let mut graph = Graph::new();
let mut model = SequentialModel::new(&graph);
model.add_conv2d_zero(3, 16, 3, 3, 1, 1, true)?;
model.add_relu();
model.add_flatten();
model.add_linear_zero(&mut graph, 16 * 30 * 30, 10)?;

let config = TrainerConfig {
    optimizer: OptimizerKind::Adam { lr: 0.001 },
    loss: LossKind::CrossEntropy,
    epochs: 50,
    batch_size: 32,
    validation_split: Some(0.2),
};

let result = Trainer::new(config).fit(&mut model, &mut graph, &inputs, &targets)?;
println!("Final loss: {:.4}", result.final_loss);
```

17 pretrained architectures available out of the box — ResNet, VGG, MobileNet, EfficientNet, ViT, DeiT. Weights download automatically:

```rust,ignore
let hub = ModelHub::new(); // caches in ~/.yscv/models/
let weights = hub.load_weights("resnet50")?;
```

## Object detection

YOLOv8 detection with tracking and recognition:

```rust,ignore
use yscv::detect::{detect_yolov8_from_rgb, yolov8_coco_config, non_max_suppression};

let img = imread("scene.jpg")?;
let detections = detect_yolov8_from_rgb(&img, &model, &yolov8_coco_config())?;
let filtered = non_max_suppression(&detections, 0.5);
```

The detect → track → recognize pipeline's tracking and matching stages cost tens of microseconds per frame on a single CPU core (the model inference is the real budget). DeepSORT and ByteTrack are built in. VP-tree ANN for recognition.

## Performance

YSCV is profiled and tuned on real edge hardware — the hot paths (Conv, MatMul,
depthwise, pointwise, the fused streaming kernels) are hand-written SIMD with
runtime dispatch, benchmarked per thread count against ONNX Runtime / XNNPACK on
the target board, and against NumPy / PyTorch / OpenCV / ffmpeg for the
standalone ops. The kernel work is mapped in
[docs/onnx-cpu-kernels.md](docs/onnx-cpu-kernels.md) (per-op hot-path map,
asm-vs-intrinsics coverage, A/B env toggles); the direction for broadening
per-hardware performance is in
[docs/microarch-dispatch.md](docs/microarch-dispatch.md).

> **Benchmarks (current CPU suite).** The CPU sections of
> [docs/performance-benchmarks.md](docs/performance-benchmarks.md) are freshly
> measured on fixed hardware with pinned competitor versions and a regenerable
> script. On a public Siamese tracker, AMD Ryzen 5 7500F (Zen 4):
> **8.63 ms / 1T (116 FPS), 2.52 ms / 6T (396 FPS)** — roughly 7% behind ONNX
> Runtime 1.24.4 single-thread, with ORT scaling better across cores (the gap
> widens to ~1.45× at 6T). **On the Orange Pi Zero 3 (Cortex-A53) — the actual
> deployment target — the picture inverts: yscv is 1.5–1.6× *faster* than ORT**
> on the same tracker (321 ms vs 496 ms / 1T). **On the Apple M1, yscv is
> 1.9× / 4.3× faster than ORT-CPU at 1 / 4 threads** (15.4 / 5.46 ms), and its
> MPSGraph GPU path runs the tracker at 1.26 ms sync (792 FPS, 1.3× over ORT
> CoreML) up to 2688 FPS pipelined. On single ops yscv is at parity
> with NumPy/PyTorch on memory-bound elementwise and faster on transcendentals
> and activations; it beats ORT-CPU across the board (on the A53, up to ~11× on
> activations like sigmoid/gelu). See
> [docs/performance-benchmarks.md](docs/performance-benchmarks.md) for the
> tables, methodology, and exact reproduction commands. The older
> YOLO / Metal / video numbers in that doc were measured on different
> hardware and dates and are marked *pending re-measurement* — treat those as
> provisional.

2,249 tests across 19 crates.

## What's inside

The framework is split into 19 crates:

| Crate | Purpose |
|-------|---------|
| `yscv-cpu` | Cached host CPU identity (`Microarch`, `CpuFeatures`, `host_cpu`) shared by runtime dispatch |
| `yscv-tensor` | N-dimensional tensor with 159 ops, f32/f16/bf16, SIMD-accelerated |
| `yscv-kernels` | CPU + GPU compute backends, 264 SIMD functions, 61 WGSL + 4 Metal shaders |
| `yscv-autograd` | Reverse-mode autodiff with 61 backward op variants |
| `yscv-optim` | SGD, Adam, AdamW, Adagrad, RAdam, RmsProp, Lamb, Lars + Lookahead, 11 LR schedulers |
| `yscv-model` | 39 layer types, 17 loss functions, Trainer API, model zoo (17 architectures), LoRA |
| `yscv-imgproc` | 175 image processing ops (blur, edges, morphology, features, color) |
| `yscv-video` | H.264/HEVC/AV1 decoder (parallel tile/WPP, weighted prediction, AV1 inter MC), hardware decode, camera I/O, MP4 / MKV parsing, audio metadata |
| `yscv-detect` | YOLOv8/v11 pipeline, NMS, heatmap decoding |
| `yscv-track` | DeepSORT, ByteTrack, Kalman filter, Hungarian assignment, Re-ID |
| `yscv-recognize` | Cosine matching, VP-Tree ANN indexing, Recognizer with enroll/match |
| `yscv-eval` | Classification/detection/tracking/regression/image-quality metrics, 8 dataset adapters |
| `yscv-onnx` | 122 op ONNX CPU runtime, INT4/INT8 quantization, LLM generation (KV-cache, RoPE, GQA), graph optimizer, Metal/MPSGraph GPU |
| `yscv-pipeline` | TOML-driven multi-accelerator dispatch (CPU / RKNN / MPSGraph / GPU), RT wiring, recovery, hot-reload |
| `yscv-video-mpp` | Rockchip MPP hardware encoder integration (H.264, H.265) |
| `yscv-cli` | Inference + evaluation CLI: camera diagnostics, dataset eval, pipeline runner |
| `yscv` | Umbrella crate re-exporting the prelude and per-crate APIs |

## Building

```bash
cargo build --workspace --release
cargo test --workspace --release      # 2,249 tests
cargo run --example train_cnn         # train a CNN on synthetic data
cargo run --example train_linear      # linear regression
cargo run --example image_processing  # image pipeline demo
cargo run --example yolo_detect -- model.onnx photo.jpg  # YOLOv8/v11 detection
cargo run --example yolo_finetune     # fine-tune a detection head
```

### GPU / Metal (Apple Silicon)

```bash
# wgpu backend (Vulkan / Metal / DX12 — cross-platform)
cargo run --release --example bench_gpu --features gpu -- model.onnx

# Metal-native backend (macOS only)
cargo run --release --example bench_mpsgraph --features metal-backend  # MPSGraph + per-op comparison
cargo run --release --example bench_metal_yolo --features metal-backend
cargo run --release --example bench_mps_gemm  --features metal-backend

# Both features can be combined
cargo test --workspace --features metal-backend
cargo clippy --workspace --features metal-backend
```

The `gpu` feature uses wgpu and works on any platform with Vulkan, Metal, or DX12. The `metal-backend` feature talks to Metal directly via `metal-rs` and provides two backends: **MPSGraph** (whole-model graph compilation, the fastest of yscv's own backends on Apple Silicon — ~4.8 ms YOLOv8n *(measured on Apple M1, pending re-measurement)*; also exposes a triple-buffered `submit_mpsgraph_plan` / `wait_mpsgraph_plan` pipelined API that overlaps CPU marshaling with GPU compute for higher throughput on sustained inference, multi-input models supported) and **Metal per-op** (individual op dispatch with Winograd + MPS GEMM, fallback for unsupported models). On macOS, `metal-backend` is what you want.

### System dependencies

YSCV builds with zero required system dependencies. Optional:

- **OpenBLAS** (Linux/Windows) — faster matmul. `apt install libopenblas-dev` or `brew install openblas`. macOS uses Accelerate automatically.
- **protoc** — for ONNX proto generation. Without it, a built-in fallback is used. `apt install protobuf-compiler` or `brew install protobuf`.

### Feature flags

> **Full reference**: [`docs/feature-flags.md`](docs/feature-flags.md) —
> every flag, what it does, setup steps per platform, combination
> recipes, troubleshooting. Start there if unsure what to enable.

Quick summary of the big ones:

| Flag | What it does | Platforms |
|------|-------------|-----------|
| `gpu` | GPU acceleration via wgpu (Vulkan / Metal / DX12) | All |
| `metal-backend` | Metal-native GPU pipeline (yscv's fastest backend on Apple Silicon) | macOS only |
| `rknn` | Rockchip NPU via `librknnrt.so` (RK3588 / RK3576 / RV1106) — `dlopen` at runtime, full SDK 2.4.3a0 | Linux ARM64 (Rockchip device) |
| `native-camera` | Real camera capture (V4L2 / AVFoundation / MediaFoundation) | All |
| `blas` | OpenBLAS matmul on Linux/Windows (opt-in; default is yscv's hand-tuned kernels). macOS always uses Accelerate/AMX — no flag needed | Linux / Windows |

### Multi-architecture SIMD

All hot paths have hand-tuned SIMD for three architectures with runtime CPU detection:

| | macOS (Apple Silicon) | Linux/Windows (x86_64) | Linux (ARM64) |
|---|---|---|---|
| **f32 tensor ops** | NEON 4× unroll | AVX 4× unroll → SSE fallback | NEON 4× unroll |
| **u8 image ops** | NEON 16B/iter | AVX2 32B/iter → SSE2/SSSE3 16B/iter | NEON 16B/iter |
| **Activations** | NEON 3-term poly | AVX/SSE poly | NEON 3-term poly |
| **MatMul / Conv** | Accelerate AMX (always on) + NEON kernels | Hand-tuned AVX/SSE kernels (opt-in OpenBLAS) | Hand-tuned NEON kernels (opt-in OpenBLAS) |
| **Vectorized math** | vDSP (Accelerate) | AVX/SSE kernels | NEON kernels |
| **Threading** | GCD dispatch_apply (~0.3µs) | std::thread::scope (~1µs) | std::thread::scope (~1µs) |
| **GPU inference** | MPSGraph | wgpu/Vulkan | wgpu/Vulkan |
| **Softmax** | Fused NEON | Fused AVX/SSE | Fused NEON |
| **Allocator** | mimalloc | mimalloc | mimalloc |

SIMD dispatch is automatic at runtime — no need for `-C target-cpu` flags (though they help: `-C target-cpu=apple-m1` or `-C target-cpu=native` for best codegen). The framework detects CPU features once through `yscv-cpu` and routes kernels through cached `host_cpu().features` gates. `yscv_kernels::runtime_dispatch_report()` exposes the typed CPU/kernel selection snapshot, while `runtime_config_report()` records active `YSCV_*` A/B overrides for reproducible benchmark logs. 570 `#[target_feature]`-gated functions total, all with scalar fallback for WASM/RISC-V/Miri.

### Recommended release profile

For best performance, the workspace already includes an optimized release profile in `Cargo.toml`:

```toml
[profile.release]
lto = "thin"
codegen-units = 1
```

For Apple Silicon specifically, add to `.cargo/config.toml`:

```toml
[target.aarch64-apple-darwin]
rustflags = ["-C", "target-cpu=apple-m1"]
```

## Architecture

```
yscv (prelude)
    ↓
┌─────────────┐ ┌──────────┐ ┌───────────┐
│ yscv-model   │ │ yscv-    │ │ yscv-     │
│ (39 layers,  │ │ imgproc  │ │ detect/   │
│  trainer,    │ │ (160 ops)│ │ track/    │
│  zoo, LoRA)  │ │          │ │ recognize │
└──────┬───────┘ └────┬─────┘ └─────┬─────┘
       ↓              ↓             ↓
┌──────────────┐ ┌──────────┐ ┌──────────┐
│ yscv-autograd│ │ yscv-    │ │ yscv-    │
│ (61 backward │ │ kernels  │ │ video    │
│  op variants)│ │ (SIMD+   │ │ (H.264,  │
└──────┬───────┘ │ GPU)     │ │ HEVC,AV1,│
       ↓         └────┬─────┘ │ camera)  │
┌──────────────┐      ↓       └──────────┘
│ yscv-tensor  │←─────┘
│ (115 ops,    │
│  f32/f16/bf16│
│  SIMD)       │
└──────────────┘
```

## Cookbook

See [docs/cookbook.md](docs/cookbook.md) for practical recipes: image processing, model training, ONNX inference (CPU/GPU/Metal), YOLO detection, tracking, preprocessing pipelines, fine-tuning, video, cross-compilation, and benchmarking.

## When to use YSCV (and when not to)

**Use YSCV when** you need to deploy a trained model as a single binary without Python. When you're building a real-time CV pipeline on edge hardware. When you want to train a CNN or ViT on moderate-scale data without setting up a GPU cluster. When you need image processing that's faster than OpenCV and memory-safe.

**Don't use YSCV when** you need the Python ML ecosystem — Hugging Face model hub, thousands of community architectures, Jupyter notebook prototyping, dynamic graph debugging with breakpoints. When you're training foundation models across thousands of GPUs with NCCL. For that, use PyTorch.

YSCV is faster than PyTorch on many individual operations, but PyTorch has a decade-old ecosystem with millions of pretrained models and research tooling. Train in PyTorch, export to ONNX, deploy with YSCV — or train directly in YSCV if your model fits within our 39 layer types and you don't need the Python ecosystem.

## License

Licensed under [LICENSE](LICENSE).

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