# edgefirst-decoder
[](https://crates.io/crates/edgefirst-decoder)
[](https://docs.rs/edgefirst-decoder)
[](LICENSE)
**High-performance ML model output decoding for object detection and segmentation.**
This crate provides efficient post-processing for YOLO and ModelPack model outputs, supporting both floating-point and quantized inference results.
## Supported Models
| **YOLO** | YOLOv5, v8, v11, v26 | Instance seg | float32, int8, uint8 |
| **ModelPack** | SSD-style | Semantic seg | float32, int8, uint8 |
## Features
- **Quantized decoding** - Direct int8/uint8 processing without dequantization overhead
- **Configurable NMS** - Class-agnostic or class-aware non-maximum suppression
- **Batch processing** - Efficient handling of batched model outputs
- **Builder pattern** - Flexible configuration with sensible defaults
## Quick Start
```rust,ignore
use edgefirst_decoder::{DecoderBuilder, DetectBox, Segmentation};
// Build decoder from model config
let decoder = DecoderBuilder::new()
.with_score_threshold(0.25)
.with_iou_threshold(0.7)
.with_config_json_str(model_config_json)
.build()?;
// Decode quantized model output
let mut detections: Vec<DetectBox> = Vec::with_capacity(100);
let mut masks: Vec<Segmentation> = Vec::with_capacity(100);
decoder.decode_quantized(
&[model_output.view().into()],
&mut detections,
&mut masks,
)?;
// Process results
for det in &detections {
println!("Class {} at [{:.1}, {:.1}, {:.1}, {:.1}] score={:.2}",
det.label, det.bbox.xmin, det.bbox.ymin, det.bbox.xmax, det.bbox.ymax, det.score);
}
```
## Low-Level API
For known model types, use the direct decoding functions:
```rust,ignore
use edgefirst_decoder::yolo::decode_yolo_det;
use edgefirst_decoder::Quantization;
let mut detections = Vec::with_capacity(100);
decode_yolo_det(
(output_array.view(), Quantization::new(0.012345, 26)),
0.25, // score threshold
0.7, // IOU threshold
Some(edgefirst_decoder::configs::Nms::ClassAgnostic),
&mut detections,
);
```
## Configuration
Decoders can be configured via JSON/YAML matching the model's output specification:
```json
{
"decoder": "ultralytics",
"shape": [1, 84, 8400],
"quantization": [0.012345, 26],
"normalized": true
}
```
## NMS Modes
- `ClassAgnostic` - Suppress overlapping boxes regardless of class (default)
- `ClassAware` - Only suppress boxes with the same class label
- `None` - Bypass NMS (for models with built-in NMS)
## End-to-End Models (YOLO26)
YOLO26 models embed NMS directly in the model architecture (one-to-one matching heads), eliminating the need for external NMS post-processing.
Configure via the `decoder_version` field in the model config:
```json
{
"decoder": "ultralytics",
"decoder_version": "yolo26",
"shape": [1, 300, 6],
"quantization": [0.012345, 26],
"normalized": true
}
```
When `decoder_version` is `"yolo26"`, the decoder:
- Bypasses NMS entirely (the `nms` config field is ignored)
- Expects post-NMS output format: `[batch, N, 6+]` where columns are `[x1, y1, x2, y2, conf, class, ...]`
- Supports both detection-only and detection+segmentation variants
For non-end-to-end YOLO26 exports (`end2end=false`), use `decoder_version: "yolov8"` with explicit NMS configuration.
### Non-End-to-End Mode
Models exported with `end2end=false` require external NMS, configurable via the `nms` field:
```json
{
"decoder": "ultralytics",
"decoder_version": "yolov8",
"nms": "class_agnostic",
"shape": [1, 84, 8400]
}
```
## Proto Mask API
For segmentation models, the decoder provides two APIs for accessing mask prototype data:
- `decode_quantized_proto()` — returns raw quantized proto data and mask coefficients without materializing pixel masks
- `decode_float_proto()` — returns float proto data and mask coefficients
These are preferred when passing mask data to GPU rendering pipelines (e.g., `ImageProcessor::draw_proto_masks()`), as they avoid the CPU cost of materializing full-resolution masks.
```rust,ignore
// GPU rendering path: decode proto data, pass to GL for fused rendering
let (detections, proto_data) = decoder.decode_quantized_proto(
&[model_output.view().into()],
)?;
// Pass proto_data directly to GPU for fused mask overlay
processor.draw_proto_masks(&mut frame, &detections, &proto_data)?;
```
## Model Type Variants
The decoder automatically selects the appropriate model type based on the config:
| `YoloDet` | 1 (detection) | Standard YOLO detection |
| `YoloSegDet` | 2 (detection + protos) | YOLO detection + segmentation |
| `YoloSegDet2Way` | 3 (detection + mask_coefs + protos) | YOLO segmentation with 2-way split output |
| `YoloSplitDet` | 2 (boxes + scores) | Split-output detection |
| `YoloSplitSegDet` | 4 (boxes + scores + mask_coeff + protos) | Split-output segmentation |
| `YoloEndToEndDet` | 1 (detection) | End-to-end detection (post-NMS) |
| `YoloEndToEndSegDet` | 2 (detection + protos) | End-to-end segmentation |
| `YoloSplitEndToEndDet` | 3 (boxes + scores + classes) | Split end-to-end detection |
| `YoloSplitEndToEndSegDet` | 5 (boxes + scores + classes + mask_coeff + protos) | Split end-to-end segmentation |
| `ModelPackDet` | 2 (boxes + scores) | ModelPack detection |
| `ModelPackSegDet` | 3 (boxes + scores + segmentation) | ModelPack segmentation |
| `ModelPackDetSplit` | N (detection layers) | ModelPack split detection |
| `ModelPackSegDetSplit` | N+1 (detection layers + segmentation) | ModelPack split segmentation |
| `ModelPackSeg` | 1 (segmentation) | ModelPack semantic segmentation |
### YOLO-SEG 2-Way Split Format
`YoloSegDet2Way` handles TFLite INT8 segmentation models that use a **2-way split** to separate mask coefficients from the combined detection output. Boxes and class scores remain merged — they share similar value ranges so splitting them yields no quantization benefit. Only mask coefficients (unbounded linear projection) need a separate quantization scale.
#### Output Tensor Layout
| `output0` | detection | `[1, nc+4, N]` | Boxes (4 channels) + class scores (nc channels), combined |
| `output1` | protos | `[1, H/4, W/4, 32]` | Prototype masks (4D NHWC) |
| `output2` | mask_coefs | `[1, 32, N]` | Mask coefficients per anchor |
Where `N=8400` for 640×640 input (standard YOLO multi-scale anchors: 80×80 + 40×40 + 20×20).
**COCO example** (nc=80): detection is `[1, 84, 8400]`, mask_coefs is `[1, 32, 8400]`, protos is `[1, 160, 160, 32]`.
#### Output Identification by Shape
The builder classifies the three outputs by shape alone:
- **4D tensor** → protos (only protos are 4D)
- **3D, feature_dim == 32** → mask_coefs
- **3D, feature_dim != 32** → detection (nc+4 channels)
The feature dimension is the smaller of the two non-batch dimensions (channels-first: `shape[1]`; channels-last: `shape[2]`).
#### Edge Case: nc=28
When `num_classes + 4 == 32`, the detection and mask_coefs feature dimensions collide, making shape-based classification ambiguous. In this case the split is **not performed** — the model exports 2 outputs (unsplit detection + protos) and is decoded as `YoloSegDet` instead.
#### Auto-Selection
The builder selects `YoloSegDet2Way` automatically when it detects exactly 3 outputs whose shapes match the pattern above (one 4D proto tensor, one 3D tensor with feature_dim=32, one 3D tensor with feature_dim≠32). No extra configuration is required.
#### Backwards Compatibility
Older 4-output models (separate boxes + scores + mask_coefs + protos, i.e., `YoloSplitSegDet`) remain supported. The decoder classifies by shape, not by output count.
## Tracked Decoding
The `tracker` feature adds `decode_tracked` to integrate object tracking directly into the decode step. Each decoded detection box is matched to a persistent track and assigned a stable UUID for the lifetime of the track.
Enable the feature in `Cargo.toml`:
```toml
edgefirst-decoder = { version = "0.13", features = ["tracker"] }
```
### Usage
```rust,ignore
use edgefirst_decoder::{DecoderBuilder, DetectBox, Segmentation, TrackInfo};
use edgefirst_decoder::Tracker; // re-exported from edgefirst-tracker
use edgefirst_tracker::ByteTrackBuilder;
let decoder = DecoderBuilder::new()
.with_score_threshold(0.25)
.with_iou_threshold(0.7)
.with_config_json_str(model_config_json)
.build()?;
let mut tracker = ByteTrackBuilder::new()
.track_high_conf(0.5)
.track_update(0.1)
.build();
let mut detections: Vec<DetectBox> = Vec::with_capacity(100);
let mut masks: Vec<Segmentation> = Vec::with_capacity(100);
let mut tracks: Vec<TrackInfo> = Vec::with_capacity(100);
decoder.decode_tracked(
&mut tracker,
timestamp, // u64 frame timestamp
&model_outputs, // &[&TensorDyn]
&mut detections,
&mut masks,
&mut tracks,
)?;
// detections[i] and tracks[i] correspond to the same object
for (det, track) in detections.iter().zip(tracks.iter()) {
println!(
"Track {} class {} score={:.2} at [{:.1}, {:.1}, {:.1}, {:.1}]",
track.uuid, det.label, det.score,
det.bbox.xmin, det.bbox.ymin, det.bbox.xmax, det.bbox.ymax,
);
}
```
### TrackInfo Fields
| `uuid` | `Uuid` | Stable unique identifier for the track |
| `tracked_location` | `[f32; 4]` | Kalman-smoothed location in XYXY format |
| `count` | `i32` | Number of times the track has been updated |
| `created` | `u64` | Timestamp when the track was first created |
| `last_updated` | `u64` | Timestamp of the most recent update |
The `tracked_location` reflects the Kalman-filter prediction and may differ slightly from `det.bbox`, which is the raw decoded box before smoothing.
## License
Licensed under the Apache License, Version 2.0. See [LICENSE](https://github.com/EdgeFirstAI/hal/blob/main/LICENSE) for details.