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| license: other |
| tags: |
| - heal |
| - horizon |
| --- |
| |
| # ResNet-50 |
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| 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. |
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| ## Deployment Metrics |
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| ### Model Parameters |
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| | Model | Model Input | Backbone | Neck | Model Output | |
| |---|---|---|---|---| |
| | ResNet-50 | `1x3x224x224` | ResNet-50 | — | Classification logits `(B,1000)` | |
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| ### Accuracy Metrics |
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| | March | Metric | float | calibration | qat | hbm | |
| | --- | --- | --- | --- | --- | --- | |
| | J6M | Accuracy | 0.774 | 0.7721 | — | 0.7691 | |
| | | TopKAccuracy(5) | — | — | — | — | |
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| > 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. |
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| ### Performance Metrics |
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| > **Performance test methodology**: FPS is measured with single-core eight-thread; Latency is measured with single-core single-thread; Memory is peak DDR usage. |
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| | March | latency (ms) | fps | Memory Usage | |
| |---|---|---|---| |
| | J6M | 0.90 | 1493.65 | 28.80 | |
| | J6P | 0.59 | 6008.89 | 29.10 | |
| | J6B | - | - | - | |
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| J6B performance is not available for this model. |
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| --- |
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| ## Model Overview |
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| ### Core Design |
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| 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. |
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| - **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. |
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| ### Official Repo and Paper |
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| Official repo: https://github.com/pytorch/vision (torchvision ResNet implementation) |
| Paper: https://arxiv.org/abs/1512.03385 |
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