OpenExplorer/deform_detr_resnet50

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.

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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.

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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