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README.md

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+---
+license: other
+tags:
+  - heal
+  - horizon
+---
+
+# Deformable DETR (ResNet-50)
+
+Deformable DETR replaces DETR's global self-attention with multi-scale deformable attention: each query only samples a small number of points near reference points, enabling faster convergence and lower compute. Four feature levels plus 900 queries provide multi-scale candidates, then the decoder progressively refines boxes layer by layer.
+
+---
+
+## Deployment Metrics
+
+### Model Parameters
+
+| Model | Model Input | Backbone | Neck | Model Output |
+|---|---|---|---|---|
+| DeformableDETR | Single image `1x3x800x1332` | ResNet-50 | `ChannelMapperNeck` | Detection boxes `(B,N,cls+reg)` |
+
+### Accuracy Metrics
+
+| March | Metric | float | calibration | qat | hbm |
+| --- | --- | --- | --- | --- | --- |
+| J6M | mAP | 0.4384 | 0.412 | 0.4526 | 0.4529 |
+
+> 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 | 144.84 | 6.92 | 656.00 |
+| J6P | 78.02 | 28.89 | 672.80 |
+| J6B | - | - | - |
+
+J6B performance is not available for this model.
+
+---
+
+## Model Overview
+
+### Core Design
+
+Deformable DETR replaces DETR's global self-attention with multi-scale deformable attention: each query only samples a small number of points near reference points, enabling faster convergence and lower compute. Four feature levels plus 900 queries provide multi-scale candidates, then the decoder progressively refines boxes layer by layer.
+
+- **Task type**: 2D object detection (2D Object Detection).
+- **backbone**: ResNet-50 (`ResNet50`, `include_top=False` removes classification head).
+- **neck**: `ChannelMapperNeck` (`in_channels=[512,1024,2048]`, `out_channel=256`, 1×1 conv, `extra_convs=1`).
+- **Position encoding**: `PositionEmbeddingSine` (`num_pos_feats=128`, normalized).
+- **Transformer**: `DeformableDetrTransformer` (encoder 6 layers + decoder 6 layers, `embed_dim=256`, `num_heads=8`, `feedforward_dim=1024`, `num_feature_levels=4`, `num_queries=900`).
+- **Post-processing**: `DeformDetrPostProcess` (evaluation selects `select_box_nums_for_evaluation=300` boxes).
+- **Loss**: `DeformableCriterion`: classification focal loss + L1 bbox + GIoU, `HungarianMatcher` bipartite matching, `aux_loss=True`.
+- **Key flags**: `with_box_refine=False`, `as_two_stage=False`.
+- **Model input**: Single image, size `800 × 1332`.
+- **Model output**: 80-class detection boxes + confidence scores.
+
+### Official Repo and Paper
+
+Official repo: https://github.com/fundamentalvision/Deformable-DETR
+Paper: https://arxiv.org/abs/2010.04159