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README.md

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+---
+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.
+
+---
+
+## 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.
+
+### Reference
+
+For more J6 chip deployment details, see https://developer.horizon.auto/forum/11335