Hugging Face's logo

STMicroelectronics
/
GMP-HAR-model

Model card Files Community
FBAGSTM commited on
Commit
230a459
·
verified ·
1 Parent(s): 4e02e2f

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +8 -8
README.md CHANGED
@@ -2,7 +2,7 @@
2
  license: other
3
  license_name: sla0044
4
  license_link: >-
5
- https://github.com/STMicroelectronics/stm32ai-modelzoo/human_activity_recognition/gmp/ST_pretrainedmodel_public_dataset/LICENSE.md
6
  ---
7
  # GMP HAR model
8
 
@@ -64,8 +64,8 @@ The inference time is reported is calculated on STM32 board **B-U585I-IOT02A** r
64
 
65
  | Model | Format | Input Shape | Target Board | Activation RAM (KiB) | Runtime RAM (KiB) | Weights Flash (KiB) | Code Flash (KiB) | Total RAM (KiB) | Total Flash (KiB) | Inference Time (ms) | STM32Cube.AI version |
66
  |:----------------------------------------------------------------------------:|:------:|:-----------:|:-------:|:--------------------:|:-----------------:|:-------------------:|:----------------:|:-----------------:|:-----------------:|:---------------------:|:---------------------:|
67
- | [GMP wl 24](https://github.com/STMicroelectronics/stm32ai-modelzoo/human_activity_recognition/gmp/ST_pretrainedmodel_public_dataset/WISDM/gmp_wl_24/gmp_wl_24.h5) | FLOAT32| 24 x 3 x 1 | B-U585I-IOT02A | 4.25 | 2.08 | 5.70 | 12.29 | 6.33 | 18.96 | 4.42 | 10.0.0 |
68
- | [GMP wl 48](https://github.com/STMicroelectronics/stm32ai-modelzoo/human_activity_recognition/gmp/ST_pretrainedmodel_public_dataset/WISDM/gmp_wl_48/gmp_wl_48.h5) | FLOAT32| 48 x 3 x 1 | B-U585I-IOT02A | 8.83 | 2.08 | 5.70 | 12.29 | 10.91 | 18.96 | 10.64 | 10.0.0 |
69
 
70
 
71
 
@@ -77,13 +77,13 @@ Dataset details: A custom dataset and not publically available, Number of classe
77
 
78
  | Model | Format | Resolution | Accuracy (%) |
79
  |:----------------------------------------------------------------------------------------------:|:--------:|:----------:|:-------------:|
80
- | [GMP wl 24](https://github.com/STMicroelectronics/stm32ai-modelzoo/human_activity_recognition/gmp/ST_pretrainedmodel_custom_dataset/mobility_v1/gmp_wl_24/gmp_wl_24.h5) | FLOAT32 | 24 x 3 x 1 | 94.08 |
81
- | [GMP wl 48](https://github.com/STMicroelectronics/stm32ai-modelzoo/human_activity_recognition/gmp/ST_pretrainedmodel_custom_dataset/mobility_v1/gmp_wl_48/gmp_wl_48.h5) | FLOAT32 | 48 x 3 x 1 | 93.84 |
82
 
83
 
84
  Confusion matrix for GMP wl 24 with Float32 weights for mobility_v1 dataset is given below.
85
 
86
- ![plot](https://github.com/STMicroelectronics/stm32ai-modelzoo/human_activity_recognition/gmp/doc/img/mobility_v1_gmp_wl_24_confusion_matrix.png)
87
 
88
  ### Accuracy with WISDM dataset
89
 
@@ -92,8 +92,8 @@ Dataset details: [link](([WISDM](https://www.cis.fordham.edu/wisdm/dataset.php))
92
 
93
  | Model | Format | Resolution | Accuracy (%) |
94
  |:--------------------------------------------------------------------------------------:|:--------:|:-----------:|:--------------:|
95
- | [GMP wl 24](https://github.com/STMicroelectronics/stm32ai-modelzoo/human_activity_recognition/gmp/ST_pretrainedmodel_public_dataset/WISDM/gmp_wl_24/gmp_wl_24.h5) | FLOAT32 | 24 x 3 x 1 | 84.49 |
96
- | [GMP wl 48](https://github.com/STMicroelectronics/stm32ai-modelzoo/human_activity_recognition/gmp/ST_pretrainedmodel_public_dataset/WISDM/gmp_wl_48/gmp_wl_48.h5) | FLOAT32 | 48 x 3 x 1 | 87.05 |
97
 
98
 
99
  ## Retraining and Integration in a simple example:
 
2
  license: other
3
  license_name: sla0044
4
  license_link: >-
5
+ https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/human_activity_recognition/gmp/ST_pretrainedmodel_public_dataset/LICENSE.md
6
  ---
7
  # GMP HAR model
8
 
 
64
 
65
  | Model | Format | Input Shape | Target Board | Activation RAM (KiB) | Runtime RAM (KiB) | Weights Flash (KiB) | Code Flash (KiB) | Total RAM (KiB) | Total Flash (KiB) | Inference Time (ms) | STM32Cube.AI version |
66
  |:----------------------------------------------------------------------------:|:------:|:-----------:|:-------:|:--------------------:|:-----------------:|:-------------------:|:----------------:|:-----------------:|:-----------------:|:---------------------:|:---------------------:|
67
+ | [GMP wl 24](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/human_activity_recognition/gmp/ST_pretrainedmodel_public_dataset/WISDM/gmp_wl_24/gmp_wl_24.h5) | FLOAT32| 24 x 3 x 1 | B-U585I-IOT02A | 4.25 | 2.08 | 5.70 | 12.29 | 6.33 | 18.96 | 4.42 | 10.0.0 |
68
+ | [GMP wl 48](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/human_activity_recognition/gmp/ST_pretrainedmodel_public_dataset/WISDM/gmp_wl_48/gmp_wl_48.h5) | FLOAT32| 48 x 3 x 1 | B-U585I-IOT02A | 8.83 | 2.08 | 5.70 | 12.29 | 10.91 | 18.96 | 10.64 | 10.0.0 |
69
 
70
 
71
 
 
77
 
78
  | Model | Format | Resolution | Accuracy (%) |
79
  |:----------------------------------------------------------------------------------------------:|:--------:|:----------:|:-------------:|
80
+ | [GMP wl 24](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/human_activity_recognition/gmp/ST_pretrainedmodel_custom_dataset/mobility_v1/gmp_wl_24/gmp_wl_24.h5) | FLOAT32 | 24 x 3 x 1 | 94.08 |
81
+ | [GMP wl 48](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/human_activity_recognition/gmp/ST_pretrainedmodel_custom_dataset/mobility_v1/gmp_wl_48/gmp_wl_48.h5) | FLOAT32 | 48 x 3 x 1 | 93.84 |
82
 
83
 
84
  Confusion matrix for GMP wl 24 with Float32 weights for mobility_v1 dataset is given below.
85
 
86
+ ![plot](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/human_activity_recognition/gmp/doc/img/mobility_v1_gmp_wl_24_confusion_matrix.png)
87
 
88
  ### Accuracy with WISDM dataset
89
 
 
92
 
93
  | Model | Format | Resolution | Accuracy (%) |
94
  |:--------------------------------------------------------------------------------------:|:--------:|:-----------:|:--------------:|
95
+ | [GMP wl 24](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/human_activity_recognition/gmp/ST_pretrainedmodel_public_dataset/WISDM/gmp_wl_24/gmp_wl_24.h5) | FLOAT32 | 24 x 3 x 1 | 84.49 |
96
+ | [GMP wl 48](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/human_activity_recognition/gmp/ST_pretrainedmodel_public_dataset/WISDM/gmp_wl_48/gmp_wl_48.h5) | FLOAT32 | 48 x 3 x 1 | 87.05 |
97
 
98
 
99
  ## Retraining and Integration in a simple example: