---
license: agpl-3.0
tags:
- executorsch
- xnnpack
- yolo26
- yolo
- object-detection
- dynamic-shapes
- edge-ai
- mobile
- cpu
library_name: executorsch
---

# YOLO26S-seg - ExecuTorch with XNNPACK (Dynamic Shapes)

**YOLO26S-seg** exported to [ExecuTorch](https://github.com/pytorch/executorch) `.pte` format with [XNNPACK](https://github.com/google/XNNPACK) backend for accelerated CPU inference.

## Model Details

- **Base Model**: Ultralytics YOLO26S-Instance Segmentation
- **Format**: ExecuTorch (.pte)
- **Backend**: XNNPACK (CPU-optimized)
- **Quantization**: FP32
- **File Size**: 42.9 MB

## Dynamic Shape Support

This model supports **dynamic input shapes** within the following constraints:

| Dimension | Min | Max | Constraint |
|-----------|-----|-----|------------|
| Height    | 320 | 8192 | Multiple of 32 |
| Width     | 320 | 8192 | Multiple of 32 |
| Batch     | 1   | 1    | Static |

**Supported resolutions**: 320×320, 640×640, 1280×1280, 2560×1440, 7680×4320 (8K), and any size that's a multiple of 32.

## Usage

```python
import torch
from executorch.runtime import Runtime

# Load the model
with open("yolo26s-seg_dynamic_xnnpack.pte", "rb") as f:
    pte_buffer = f.read()

runtime = Runtime.get()
program = runtime.load_program(pte_buffer)
method = program.load_method("forward")

# Run inference with different input sizes
for h, w in [(640, 640), (1280, 1280), (2560, 1440)]:
    input_tensor = torch.randn(1, 3, h, w)
    output = method.execute([input_tensor])
    print(f"Input shape: {(h, w)}, Output shape: {output[0].shape}")
```

## Model Architecture

YOLO26 is an end-to-end NMS-free object detector optimized for edge devices:
- End-to-end design (no NMS post-processing required)
- Up to 43% faster CPU inference than previous YOLO versions
- Optimized for mobile and edge deployment

## Performance

Based on Ultralytics YOLO26 benchmarks:

| Metric | Value |
|--------|-------|
| Parameters | 9.7M |
| Input Size | 640×640 (training) |
| Inference | Supports 320-8192 px (multiples of 32) |

## Tasks

This model performs **instance segmentation**, outputting both bounding boxes and segmentation masks.

Output includes detection results and mask coefficients.


## Troubleshooting

### Low confidence / incorrect outputs with non-contiguous inputs

If your outputs look wrong (for object-detection models this can show up as all confidences capped around **~0.20 / 20%** and **no detections**), ensure the input tensor passed to ExecuTorch is **contiguous**.

Example:

```python
import torch

# img_hwc: float32 HWC image (e.g. RGB) in [0, 1]
x = torch.from_numpy(img_hwc).permute(2, 0, 1).unsqueeze(0)  # NCHW (often non-contiguous)
x = x.contiguous()  # IMPORTANT

outputs = method.execute([x])
```

Detection symptom example (before fix):

```
Confidence range: [0.0004, 0.2012]
Detections: 0
```

After fix (`.contiguous()`):

```
Confidence range: [0.0001, 0.9589]
Detections: 12
```

## License

This model is released under AGPL-3.0 license. See [Ultralytics YOLO26](https://github.com/ultralytics/ultralytics) for more details.

## Credits

- **Base Model**: [Ultralytics YOLO26](https://github.com/ultralytics/ultralytics)
- **Export Framework**: [ExecuTorch](https://github.com/pytorch/executorch)
- **Backend**: [XNNPACK](https://github.com/google/XNNPACK)

## Export Details

- **ExecuTorch Version**: Latest main branch
- **Export Date**: 2025-02-06
- **Dynamic Shapes**: Enabled (height/width: 320-8192, multiples of 32)