| | --- |
| | license: mit |
| | license_name: sla0044 |
| | license_link: LICENSE |
| | --- |
| | # OSNet |
| |
|
| | ## **Use case** : `Re-Identification` |
| |
|
| | # Model description |
| |
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| |
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| | OSNet is a lightweight convolutional neural network architecture designed specifically for person re-identification tasks. It introduces omni-scale feature learning, enabling the network to capture multi-scale information efficiently within a single residual block. |
| |
|
| | Key features of OSNet: |
| | - Omni-scale feature learning for robust representation. |
| | - Lightweight design with fewer parameters compared to traditional re-identification models. |
| | - Suitable for deployment on resource-constrained devices. |
| |
|
| | For more details, see the OSNet paper: https://arxiv.org/abs/1905.00953 |
| |
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| | The model is quantized using ONNX quantization tools. |
| |
|
| | ## Network information |
| |
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| | | Network Information | Value | |
| | |-------------------------|-----------------| |
| | | Framework | TensorFlow Lite | |
| | | MParams alpha=0.25 | 0.197 M | |
| | | Quantization | int8 | |
| | | Provenance | https://kaiyangzhou.github.io/deep-person-reid/index.html | |
| | | Paper | https://arxiv.org/abs/1905.0095 | |
| |
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| | The models are quantized using TF Lite post-training quantization tools. |
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| |
|
| | ## Network inputs / outputs |
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| | 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 | |
| |
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| | | Output Shape | Description | |
| | | ----- | ----------- | |
| | | (1, P) | Per-class confidence for P classes in FLOAT32| |
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| |
|
| | ## Recommended platforms |
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| | | Platform | Supported | Recommended | |
| | |----------|-----------|-----------| |
| | | STM32L0 |[]|[]| |
| | | STM32L4 |[x]|[]| |
| | | STM32U5 |[x]|[]| |
| | | STM32H7 |[x]|[x]| |
| | | STM32MP1 |[x]|[x]| |
| | | STM32MP2 |[x]|[x]| |
| | | STM32N6 |[x]|[x]| |
| |
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| | # Performances |
| |
|
| | ## Metricss |
| |
|
| | - Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option. |
| | - `tfs` stands for "training from scratch", meaning that the model weights were randomly initialized before training. |
| | - `tl` stands for "transfer learning", meaning that the model backbone weights were initialized from a pre-trained model, then only the last layer was unfrozen during the training. |
| | - `fft` stands for "full fine-tuning", meaning that the full model weights were initialized from a transfer learning pre-trained model, and all the layers were unfrozen during the training. |
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| |
|
| | ### Reference **NPU** memory footprint on DeepSportradar dataset (see Accuracy for details on dataset) |
| | |Model | Dataset | Format | Resolution | Series | Internal RAM | External RAM | Weights Flash | STEdgeAI Core version | |
| | |----------|------------------|--------|-------------|------------------|------------------|---------------------|---------------|-------------------------| |
| | | [OSNet 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/re_identification/osnet/ST_pretrainedmodel_public_dataset/DeepSportradar/osnet_a025_256_128_tfs/osnet_a025_256_128_tfs_int8.tflite) | DeepSportradar | Int8 | 256x128x3 | STM32N6 | 480 | 0 | 404.94 | 3.0.0 | |
| | | [OSNet 1.0 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/re_identification/osnet/ST_pretrainedmodel_public_dataset/DeepSportradar/osnet_a100_256_128_tfs/osnet_a100_256_128_tfs_int8.tflite) | DeepSportradar | Int8 | 256x128x3 | STM32N6 | 1440 | 0 | 2375.33 | 3.0.0 | |
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| |
|
| | ### Reference **NPU** inference time on DeepSportradar dataset (see Accuracy for details on dataset) |
| | | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version | |
| | |--------|------------------|--------|-------------|------------------|------------------|---------------------|-----------| -----------------------| |
| | | [OSNet 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/re_identification/osnet/ST_pretrainedmodel_public_dataset/DeepSportradar/osnet_a025_256_128_tfs/osnet_a025_256_128_tfs_int8.tflite) | DeepSportradar | Int8 | 256x128x3 | STM32N6570-DK | NPU/MCU | 3.53 | 283.3 | 3.0.0 | |
| | | [OSNet 1.0 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/re_identification/osnet/ST_pretrainedmodel_public_dataset/DeepSportradar/osnet_a100_256_128_tfs/osnet_a100_256_128_tfs_int8.tflite) | DeepSportradar | Int8 | 256x128x3 | STM32N6570-DK | NPU/MCU | 13.44 | 74.4 | 3.0.0 | |
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| |
|
| | ### Reference **MCU** memory footprint based on DeepSportradar dataset (see Accuracy for details on dataset) |
| | |Model | Dataset | Format | Resolution | Series | Internal RAM | External RAM | Weights Flash | STEdgeAI Core version | |
| | |----------|------------------|--------|-------------|------------------|------------------|---------------------|---------------|-------------------------| |
| | | [OSNet 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/re_identification/osnet/ST_pretrainedmodel_public_dataset/DeepSportradar/osnet_a025_256_128_tfs/osnet_a025_256_128_tfs_int8.tflite) | DeepSportradar | Int8 | 256x128x3 | STM32H7 | 331.45 | 0 | 139.52 | 3.0.0 | |
| | | [OSNet 1.0 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/re_identification/osnet/ST_pretrainedmodel_public_dataset/DeepSportradar/osnet_a100_256_128_tfs/osnet_a100_256_128_tfs_int8.tflite) | DeepSportradar | Int8 | 256x128x3 | STM32H7 | 396.01 | 1024.0 | 1892.75 | 3.0.0 | |
| |
|
| | ### Reference **MCU** inference time on DeepSportradar dataset (see Accuracy for details on dataset) |
| | | Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version | |
| | |--------|------------------|--------|-------------|------------------|------------------|---------------------|-----------| -----------------------| |
| | | [OSNet 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/re_identification/osnet/ST_pretrainedmodel_public_dataset/DeepSportradar/osnet_a025_256_128_tfs/osnet_a025_256_128_tfs_int8.tflite) | DeepSportradar | Int8 | 256x128x3 | STM32H747I-DISCO | 1 CPU | 495.13 | 2.02 | 3.0.0 | |
| | | [OSNet 1.0 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/re_identification/osnet/ST_pretrainedmodel_public_dataset/DeepSportradar/osnet_a100_256_128_tfs/osnet_a100_256_128_tfs_int8.tflite) | DeepSportradar | Int8 | 256x128x3 | STM32H747I-DISCO | 1 CPU | 3894.82 | 0.26 | 3.0.0 | |
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| | ### Performance with DeepSportradar ReID dataset |
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| | Dataset details: [link](https://github.com/DeepSportradar/player-reidentification-challenge) , License [Apache-2.0](https://github.com/DeepSportradar/player-reidentification-challenge?tab=Apache-2.0-1-ov-file#readme) , Number of identities: 486 (train: 436, test: 50), Number of images: 9529 (train: 8569, test_query: 50, test_gallery: 910) |
| |
|
| | | Model | Format | Resolution | mAP | rank-1 accuracy |rank-5 accuracy |rank-10 accuracy | |
| | |-------|--------|------------|----------------|-----------------|----------------|-----------------| |
| | | [OSNet 0.25 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/re_identification/osnet/ST_pretrainedmodel_public_dataset/DeepSportradar/osnet_a025_256_128_tfs/osnet_a025_256_128_tfs_int8.tflite) | Int8 | 256x128 | 70.27 % | 92.0 % | 96.0 % | 96.0 % | |
| | | [OSNet 1.0 tfs](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/re_identification/osnet/ST_pretrainedmodel_public_dataset/DeepSportradar/osnet_a100_256_128_tfs/osnet_a100_256_128_tfs_int8.tflite) | Int8 | 256x128 | 73.84 % | 90.0 % | 98.0 % | 98.0 % | |
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| | ## Retraining and Integration in a simple example: |
| |
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| | Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services) |
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| |
|
| | # References |
| |
|
| | <a id="1">[1]</a> |
| | The DeepSportradar Player Re-Identification Challenge (2023) [Online]. Available: https://github.com/DeepSportradar/player-reidentification-challenge. |
