SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Paper
•
1602.07360
•
Published
•
1
SqueezeNet
Use case : Image classification
Model description
SqueezeNet is a pioneering compact architecture that achieves AlexNet-level accuracy with 50x fewer parameters. It introduced the "Fire module" combining squeeze and expand operations.
The architecture features Fire Modules with squeeze (1x1) followed by expand (1x1 + 3x3) layers, employing delayed downsampling to maintain larger activation maps longer. It uses no fully connected layers, relying on global average pooling, resulting in an extremely compact model (<0.5MB original size).
SqueezeNet is ideal for extremely constrained deployment scenarios, model compression research, and applications where model size is critical.
(source: https://arxiv.org/abs/1602.07360)
The model is quantized to int8 using ONNX Runtime and exported for efficient deployment.
Network information
| Network Information | Value |
|---|---|
| Framework | Torch |
| MParams | ~1.24 M |
| Quantization | Int8 |
| Provenance | https://github.com/DeepScale/SqueezeNet |
| Paper | https://arxiv.org/abs/1602.07360 |
Network inputs / outputs
For an image resolution of NxM and P classes
| Input Shape | Description |
|---|---|
| (1, N, M, 3) | Single NxM RGB image with UINT8 values between 0 and 255 |
| Output Shape | Description |
|---|---|
| (1, P) | Per-class confidence for P classes in FLOAT32 |
Recommended platforms
| Platform | Supported | Recommended |
|---|---|---|
| STM32L0 | [] | [] |
| STM32L4 | [] | [] |
| STM32U5 | [] | [] |
| STM32H7 | [] | [] |
| STM32MP1 | [] | [] |
| STM32MP2 | [] | [] |
| STM32N6 | [x] | [x] |
Performances
Metrics
- Measures are done with default STEdgeAI Core configuration with enabled input / output allocated option.
- All the models are trained from scratch on Imagenet dataset
Reference NPU memory footprint on Imagenet dataset (see Accuracy for details on dataset)
| Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STEdgeAI Core version |
|---|---|---|---|---|---|---|---|---|
| squeezenetv10_pt_224 | Imagenet | Int8 | 224×224×3 | STM32N6 | 2278.12 | 6683.06 | 1266.61 | 3.0.0 |
Reference NPU inference time on Imagenet dataset (see Accuracy for details on dataset)
| Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version |
|---|---|---|---|---|---|---|---|---|
| squeezenetv10_pt_224 | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 121.20 | 8.25 | 3.0.0 |
Accuracy with Imagenet dataset
| Model | Format | Resolution | Top 1 Accuracy |
|---|---|---|---|
| squeezenetv10_pt | Float | 224x224x3 | 62.11 % |
| squeezenetv10_pt | Int8 | 224x224x3 | 58.43 % |
| Model | Format | Resolution | Top 1 Accuracy |
|---|---|---|---|
| squeezenetv10_pt | Float | 224x224x3 | 62.11 % |
| squeezenetv10_pt | Int8 | 224x224x3 | 58.43 % |
Retraining and Integration in a simple example:
Please refer to the stm32ai-modelzoo-services GitHub here
References
[1] - Dataset: Imagenet (ILSVRC 2012) — https://www.image-net.org/
[2] - Model: SqueezeNet — https://github.com/DeepScale/SqueezeNet