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ktsiknos-brainchip merged 1 commit into from
Aug 20, 2025
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chore(doc): update introduction #7

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28 changes: 17 additions & 11 deletions README.md
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## Introduction

The **Akida Model Zoo** is an extension of our [foundation models](https://doc.brainchipinc.com/model_zoo_performance.html#akida-2-0-models),
featuring a curated set of models validated on our Akida 2.0 FPGA platform. These models are
made available to support the broader adoption of the Akida solution by developers, researchers,
and enthusiasts alike.
The **Akida Model Zoo** expands our [foundation models](https://doc.brainchipinc.com/model_zoo_performance.html#akida-2-0-models)
with a hand-picked collection of models accelerated by the Akida 2.0 IP. Designed for developers,
researchers, and AI enthusiasts, these ready-to-use models make it easier than ever to explore,
build, and innovate with the Akida solution.

## Models

Float and quantized models are available, with the quantized version converted and evaluated using
the Akida solution.
Both **float** and **quantized** models are available, with quantized versions converted and
evaluated on the **Akida solution**. For each model, the number of nodes required to run on a
minimal Akida IP configuration is provided, enabling straightforward assessment of performance
and deployment needs.

| Domain | Use case | Architecture | Resolution | Dataset | #Params | Quantization | Accuracy |
|--------|----------------|----------------------------------------------------------------|------------|----------|---------|--------------|----------|
| Vision | Classification | [MobileNetV2 1.0](vision/classification/mobilenetv2/imagenet) | 224 | ImageNet | 3.5M | 8 | 70.35% |
| Vision | Classification | [MobileNetV2 0.75](vision/classification/mobilenetv2/imagenet) | 160 | ImageNet | 2.6M | 8 | 62.85% |
| Vision | Classification | [MobileNetV2 0.35](vision/classification/mobilenetv2/imagenet) | 96 | ImageNet | 1.2M | 8 | 43.47% |
In addition, some models can be evaluated directly through [Akida Cloud](https://brainchip.com/aclp/)
☁️, offering a convenient way to explore and experiment without local hardware.


| Domain | Use case | Architecture | Resolution | Dataset | #Params | Quantization | Accuracy | Minimal #Nodes |
|--------|----------------|-------------------------------------------------------------------|------------|----------|---------|--------------|----------|----------------|
| Vision | Classification | [MobileNetV2 1.0](vision/classification/mobilenetv2/imagenet) | 224 | ImageNet | 3.5M | 8 | 70.35% | 7 |
| Vision | Classification | [MobileNetV2 0.75](vision/classification/mobilenetv2/imagenet) ☁️ | 160 | ImageNet | 2.6M | 8 | 62.85% | 4 |
| Vision | Classification | [MobileNetV2 0.35](vision/classification/mobilenetv2/imagenet) ☁️ | 96 | ImageNet | 1.2M | 8 | 43.47% | 2 |

## Download
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