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README.md

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+---
+license: other
+tags:
+  - heal
+  - horizon
+---
+
+# 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