Add README.md

README.md

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+---
+license: other
+tags:
+  - heal
+  - horizon
+---
+
+# EfficientNet-B0
+
+EfficientNet-B0 consists of MBConv (mobile inverted bottleneck) convolution blocks, scaled jointly in depth/width/resolution via compound scaling; this config uses ReLU activation and disables SE Block (better suited for BPU quantization deployment).
+
+---
+
+## Deployment Metrics
+
+### Model Parameters
+
+| Model | Model Input | Backbone | Neck | Model Output |
+|---|---|---|---|---|
+| EfficientNet-B0 | `1x3x224x224` | EfficientNet-B0 | — | Classification logits `(B,1000)` |
+
+### Accuracy Metrics
+
+| March | Metric | float | calibration | qat | hbm |
+| --- | --- | --- | --- | --- | --- |
+| J6M | Accuracy | 0.7491 | 0.7433 | — | 0.7436 |
+|  | TopKAccuracy(5) | — | — | — | — |
+
+> Results are based on `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 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 | 0.40 | 4938.45 | 8.90 |
+| J6P | 0.35 | 10480.93 | 9.00 |
+| J6B | - | - | - |
+
+J6B performance is not available for this model.
+
+---
+
+## Model Overview
+
+### Core Design
+
+EfficientNet-B0 consists of MBConv (mobile inverted bottleneck) convolution blocks, scaled jointly in depth/width/resolution via compound scaling; this config uses ReLU activation and disables SE Block (better suited for BPU quantization deployment).
+
+- **Task type**: Image classification (Image Classification).
+- **backbone**: EfficientNet-B0 (`efficientnet`, `model_type="b0"`, `activation="relu"`, `use_se_block=False`, `num_classes=1000`), composed of MBConv blocks scaled via compound scaling in depth/width/resolution; this config uses ReLU activation and disables SE Block (better suited for BPU quantization deployment).
+- **neck**: — (EfficientNet-B0 has built-in fully-connected classification head; no separate neck).
+- **Classification head**: EfficientNet-B0 built-in fully-connected classification head, directly outputs 1000-class logits.
+- **Loss**: `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/tensorflow/tpu/tree/master/models/official/efficientnet
+Paper: https://arxiv.org/abs/1905.11946