OpenExplorer/resnet50

ResNet-50

ResNet-50 consists of 4 stages of residual blocks; each stage downsamples via stride=2, then global average pooling followed by a fully connected layer outputs class probabilities.

---

Deployment Metrics

Model Parameters

Model	Model Input	Backbone	Neck	Model Output
ResNet-50	1x3x224x224	ResNet-50	—	Classification logits (B,1000)

Accuracy Metrics

March	Metric	float	calibration	qat	hbm
J6M	Accuracy	0.774	0.7721	—	0.7691
	TopKAccuracy(5)	—	—	—	—

Data measured with march = March.NASH_M (J6M) configuration; this task has no QAT stage (qat column is —).

HEAL version: heal 0.0.2 / hbdk4-compiler 4.11.11 / horizon_plugin_pytorch 3.3.10.

Performance Metrics

Performance test methodology: 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	0.90	1493.65	28.80
J6P	0.59	6008.89	29.10
J6B	-	-	-

J6B performance is not available for this model.

---

Model Overview

Core Design

ResNet-50 consists of 4 stages of residual blocks; each stage downsamples via stride=2, then global average pooling followed by a fully connected layer outputs class probabilities.

• Task type: Image classification (Image Classification).
• backbone: ResNet-50 (ResNet50, num_classes=1000), 4 stages of residual blocks, each stage downsamples via stride=2.
• neck: — (ResNet-50 has built-in fully connected classification head, no standalone neck).
• Classification head: ResNet-50 built-in fully connected classification head, directly outputs 1000-class logits.
• Loss function: CEWithLabelSmooth (cross-entropy with label smoothing).
• Model input: Single RGB image, resolution 224 × 224 (1x3x224x224).
• Model output: 1000-class prediction logits; argmax gives predicted class.

Official Repo and Paper

Official repo: https://github.com/pytorch/vision (torchvision ResNet implementation) Paper: https://arxiv.org/abs/1512.03385