---
license: other
tags:
  - heal
  - horizon
---

# FCOS (EfficientNet-b3)

FCOS eliminates anchors and predicts classification scores, centerness, and distances to box edges per pixel; `BiFPN` performs bidirectional feature fusion across 5 scales to improve small-object detection; training jointly supervises classification, centerness, and regression losses.

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## Deployment Metrics

### Model Parameters

| Model | Model Input | Backbone | Neck | Model Output |
|---|---|---|---|---|
| FCOS | Single image `1x3x896x896` | EfficientNet-b3 | `BiFPN` | Detection boxes `(B,N,cls+reg)` |

### Accuracy Metrics

| March | Metric | float | calibration | qat | hbm |
| --- | --- | --- | --- | --- | --- |
| J6M | mAP | 0.4802 | 0.4634 | 0.4773 | 0.4772 |

> Results are based on `march = March.NASH_M` (J6M) configuration.
>
> HEAL version: heal 0.0.2 / hbdk4-compiler 4.11.11 / horizon_plugin_pytorch 3.3.10.

### Performance Metrics

> **Performance measurement**: FPS is measured with single-core eight-thread; Latency is measured with single-core single-thread; Memory is peak DDR usage.

| March | latency (ms) | fps | Memory Usage |
|---|---|---|---|
| J6M | 3.90 | 276.48 | 30.90 |
| J6P | 2.98 | 1439.97 | 35.10 |
| J6B | - | - | - |

J6B performance is not available for this model.

---

## Model Overview

### Core Design

FCOS eliminates anchors and predicts classification scores, centerness, and distances to box edges per pixel; `BiFPN` performs bidirectional feature fusion across 5 scales to improve small-object detection; training jointly supervises classification, centerness, and regression losses.

- **Task type**: 2D object detection (2D Object Detection).
- **backbone**: EfficientNet-b3 (`efficientnet`, `model_type=b3`, `include_top=False` removes classification head, `use_se_block=False`, ReLU activation).
- **neck**: `BiFPN` (`out_channels=120`, `stack=6`, input stride `[2,4,8,16,32]` → output stride `[8,16,32,64,128]`, `bifpn_sum` fusion).
- **Detection head**: `FCOSHead` (`feat_channels=120`, `stacked_convs=4`, 5 feature layers stride `[8,16,32,64,128]`, `dequant_output=True`).
- **Target assignment**: `DynamicFcosTarget` (`topK=10`, classification `FocalLoss`, regression `GIoULoss`).
- **Post-processing**: `FCOSDecoder` (`score_thr=0.05`, `nms_pre=1000`, NMS `iou_threshold=0.6`, `max_per_img=100`, uses centerness).
- **Loss**: Classification `FocalLoss` (`alpha=0.25, gamma=2.0`) + centerness `CrossEntropyLoss` (sigmoid) + regression `GIoULoss`.
- **Model input**: Single image, size `896 × 896`.
- **Model output**: 80-class detection boxes + confidence scores.

### Official Repo and Paper

Official repo: https://github.com/tianzhi0549/FCOS
Paper: https://arxiv.org/abs/1904.01355

Note: backbone is EfficientNet-b3; official repo uses a different backbone.