DarkNetTiny

Use case : Image classification

Model description

DarkNet is a convolutional neural network architecture that serves as the backbone for the YOLO (You Only Look Once) object detection series. It is designed for efficient feature extraction with a focus on real-time object detection applications.

DarkNetTiny is the compact version designed for embedded deployment. It features Leaky ReLU activations, batch normalization after every convolutional layer, and uses global average pooling instead of fully connected layers to reduce parameter count.

The straightforward stack of convolutional layers makes it easy to implement and modify, while maintaining competitive performance on edge devices.

(source: https://arxiv.org/abs/1804.02767)

The model is quantized to int8 using ONNX Runtime and exported for efficient deployment.

Network information

Network Information	Value
Framework	Torch
MParams	~1.04 M
Quantization	Int8
Provenance	https://github.com/pjreddie/darknet
Paper	https://arxiv.org/abs/1804.02767

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
darknettiny_pt_224	Imagenet	Int8	224×224×3	STM32N6	539	0	1067.11	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
darknettiny_pt_224	Imagenet	Int8	224×224×3	STM32N6570-DK	NPU/MCU	7.54	132.63	3.0.0

Accuracy with Imagenet dataset

Dataset details: link Number of classes: 1000. To perform the quantization, we calibrated the activations with a random subset of the training set. For the sake of simplicity, the accuracy reported here was estimated on the 50000 labelled images of the validation set.

model	Format	Resolution	Top 1 Accuracy
darknettiny_pt	Float	224x224x3	60.34 %
darknettiny_pt	Int8	224x224x3	52.64 %

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: DarkNet — https://github.com/pjreddie/darknet