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README.md

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----
-license: other
-license_name: sla0081
-license_link: https://github.com/STMicroelectronics/stm32ai-modelzoo/hand_posture/LICENSE.md
----
+---
+license: other
+license_name: sla0081
+license_link: https://github.com/STMicroelectronics/stm32ai-modelzoo/hand_posture/LICENSE.md
+---
+# CNN2D_ST_HandPosture model
+
+## **Use case** : `Hand posture recognition`
+
+# Model description
+
+CNN2D_ST_HandPosture is a network topology designed by ST Teams to solve basic Hand Posture recognition use cases based on ST multi-zone Time-of-Flight sensor data. It is a convolutional neural network based model before feeding the data to the fully-connected (Dense) layer. It uses the distance and signal per spad 8x8 data. This is a very light model with very small foot prints in terms of FLASH and RAM as well as computational requirements.
+
+We recommend to use input size (8 x 8 x 2) but this network can support greater input size.
+
+The only input required to the model is the input shape and the number of outputs.
+
+In this folder you will find multiple copies of the CNN2D_ST_HandPosture model pretrained on a ST custom datasets - Please refer to th [stm32ai-modelzoo-services](https://github.com/STMicroelectronics/stm32ai-modelzoo-services) GitHub for more informations
+
+## Network information (for 8 hand postures)
+
+
+| Network Information     |  Value          |
+|:-----------------------:|:---------------:|
+|  Framework              | TensorFlow      |
+|  Params                 | 2,752           |
+
+
+## Network inputs / outputs
+
+
+For an Time of Flight frame resolution of 8x8 and P classes
+
+| Input Shape | Description |
+| :----:| :-----------: |
+| (N, 8, 8, 2) | Batch ( 8 x 8 x 2 ) matrix of Time of Flight values (distance, signal per spad) for a 8x8 frame in FLOAT32.|
+
+| Output Shape | Description |
+| :----:| :-----------: |
+| (N, P) | Batch of per-class confidence for P classes in FLOAT32|
+
+
+## Recommended platforms
+
+
+| Platform | Supported | Recommended |
+|:--------:|:---------:|:-----------:|
+| STM32F4  |    [x]    |      [x]    |
+| STM32L4  |    [x]    |      [x]    |
+| STM32U5  |    [x]    |      []     |
+
+
+# Performances
+
+## Metrics
+
+Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.
+
+
+### Reference memory footprint based on ST_VL53LxCX_handposture_dataset (see Accuracy for details on dataset)
+
+| Model             | Format | Input Shape | Series  | Activation RAM (KiB) | Runtime RAM (KiB) | Weights Flash (KiB) | Code Flash (KiB) | Total RAM (KiB)   | Total Flash (KiB) | STM32Cube.AI version  |
+|:-----------------:|:------:|:-----------:|:-------:|:--------------:|:-----------:|:-------------:|:----------:|:-----------:|:-----------:|:---------------------:|
+| [CNN2D_ST_HandPosture](https://github.com/STMicroelectronics/stm32ai-modelzoo/ST_pretrainedmodel_custom_dataset/ST_VL53L8CX_handposture_dataset/CNN2D_ST_HandPosture_8classes/CNN2D_ST_HandPosture_8classes.h5) | FLOAT32   | 8 x 8 x 2    | STM32F4 | 1.07     | 2.08       | 10.75    | 14.37       |  3.15    | 25.12  | 10.0.0                 |
+| [CNN2D_ST_HandPosture](https://github.com/STMicroelectronics/stm32ai-modelzoo/ST_pretrainedmodel_custom_dataset/ST_VL53L5CX_handposture_dataset/CNN2D_ST_HandPosture_8classes/CNN2D_ST_HandPosture_8classes.h5) | FLOAT32   | 8 x 8 x 2    | STM32F4 | 1.07      | 2.08        | 10.75     | 14.37        |  3.15    | 25.12   | 10.0.0                 |
+
+
+### Reference inference time based on ST_VL53LxCX_handposture_dataset (see Accuracy for details on dataset)
+
+
+| Model             | Format | Resolution | Board            |   Frequency   | Inference time (ms) | STM32Cube.AI version  |
+|:-----------------:|:------:|:----------:|:----------------:|:-------------:|:-------------------:|:---------------------:|
+| [CNN2D_ST_HandPosture](https://github.com/STMicroelectronics/stm32ai-modelzoo/ST_pretrainedmodel_custom_dataset/ST_VL53L8CX_handposture_dataset/CNN2D_ST_HandPosture_8classes/CNN2D_ST_HandPosture_8classes.h5) | FLOAT32   | 8 x 8 x 2    | STM32F401 | 84 MHz       |    1.54  ms       | 10.0.0                 |
+| [CNN2D_ST_HandPosture](https://github.com/STMicroelectronics/stm32ai-modelzoo/ST_pretrainedmodel_custom_dataset/ST_VL53L5CX_handposture_dataset/CNN2D_ST_HandPosture_8classes/CNN2D_ST_HandPosture_8classes.h5) | FLOAT32   | 8 x 8 x 2    | STM32F401 | 84 MHz       |    1.53  ms       | 10.0.0                 |
+
+### Accuracy with ST_VL53LxCX_handposture_dataset
+
+Number of classes: 8 [None, FlatHand, Like, Dislike, Fist, Love, BreakTime, CrossHands]. Training dataset number of frames:  3,031. Test dataset number of frames: 1146.
+
+
+| Model | Format | Resolution | Accuracy |
+|:-----------------:|:------:|:----------:|:----------------:|
+| [CNN2D_ST_HandPosture](https://github.com/STMicroelectronics/stm32ai-modelzoo/ST_pretrainedmodel_custom_dataset/ST_VL53L8CX_handposture_dataset/CNN2D_ST_HandPosture_8classes/CNN2D_ST_HandPosture_8classes.h5) | FLOAT32   | 8 x 8 x 2    | 99.43 %    |
+| [CNN2D_ST_HandPosture](https://github.com/STMicroelectronics/stm32ai-modelzoo/ST_pretrainedmodel_custom_dataset/ST_VL53L5CX_handposture_dataset/CNN2D_ST_HandPosture_8classes/CNN2D_ST_HandPosture_8classes.h5) | FLOAT32   | 8 x 8 x 2    | 97.17 %    |
+
+
+## Retraining and Integration in a simple example:
+
+Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services)
+
+