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Target: Cortex-M4F 80MHz Clone this project

Impulse #1

Upload pretrained model - Step 1: Upload a model

1. Upload your trained model

Upload a TensorFlow SavedModel (saved_model.zip), ONNX model (.onnx), TensorFlow Lite model (.tflite), LGBM model (.txt), XGBoost model (.json) or pickle model (.pkl) to get started.

2. Model performance

Do you want performance characteristics (latency, RAM and ROM) for a specific device?

No, show me performance for a range of device types.

Yes, run performance profiling for:

Cortex-M4F 80MHz

Alif HE (Cortex-M55 160MHz + U55-128MACC) Alif HP (Cortex-M55 400MHz + U55-256MACC) Ambiq Apollo4 EVB (Cortex-M4F 192MHz) Arduino Nano 33 BLE Sense (Cortex-M4F 64MHz) Arduino Nicla Vision (Cortex-M7 480MHz) Arduino Nicla Vision M4 (Cortex-M4 240MHz) Arduino Portenta H7 (Cortex-M7 480MHz) BrainChip AKD1000 MINI PCIe Board BrickML (Cortex-M33 192MHz) Cortex-M4F 80MHz Cortex-M7 216MHz Digi ConnectCore 93 (NXP i.MX 93 - Cortex-A55 1.7GHz + Ethos-U65-256) Digi ConnectCore 93 (NXP i.MX 93 - Cortex-A55 1.7GHz) Espressif ESP-EYE (ESP32 240MHz) Himax WE-I (ARC DSP 400MHz) Himax WiseEye2 (Cortex-M55 400 MHz) Himax WiseEye2 (Cortex-M55 400 MHz) + Ethos-U55-64 Infineon PSoC6 CY8C624 (Cortex-M4F 150 MHz) Infineon PSoC6 CY8C6347 (Cortex-M4F 150 MHz) MacBook Pro 16" 2020 (Intel Core i9 2.4GHz) MemryX MX3 Microchip SAMA7G54 Evaluation Kit Nordic nRF52840 DK (Cortex-M4F 64MHz) Nordic nRF5340 DK (Cortex-M33 128MHz) Nordic nRF9151 DK (Cortex-M33 64MHz) Nordic nRF9160 DK (Cortex-M33 64MHz) Nordic nRF9161 DK (Cortex-M33 64MHz) Nvidia Jetson Nano Nvidia Jetson Orin NX Nvidia Jetson Orin Nano OpenMV Cam H7 Plus Particle Boron Particle Photon 2 Raspberry Pi 4 Raspberry Pi 5 Raspberry Pi RP2040 (Cortex-M0+ 133MHz) Renesas RA6M5 (Cortex-M33 200MHz) Renesas RA8D1 (Cortex-M85 480MHz) Renesas RZ/G2L Renesas RZ/V2H (CPU) Renesas RZ/V2H (with DRP-AI3 accelerator) Renesas RZ/V2L (CPU) Renesas RZ/V2L (with DRP-AI accelerator) ST IoT Discovery Kit (Cortex-M4F 80MHz) ST STM32N6 (Cortex-M33 180MHz) Seeed SenseCAP A1101 (HX6537-A ARC DSP 400MHz) Seeed Studio Wio Terminal (Cortex-M4F 120MHz) Seeed Vision AI Module (HX6537-A ARC DSP 400MHz) SiLabs EFR32MG24 (Cortex-M33 78MHz) SiLabs Thunderboard Sense 2 (Cortex-M4F 40MHz) Sony Spresense (Cortex-M4F 156MHz) Synaptics KA10000 TI AM62A (with Deep Learning Accelerator) TI AM68A (with Deep Learning Accelerator) TI LAUNCHXL-CC1352P (Cortex-M4F 48MHz) TI TDA4VM (with matrix multiply accelerator (MMA)) Think Silicon Neox GA100 (200 MHz) [BETA]

3. Upload representative features (Optional)

If you upload a set of representative features - for example, your validation set - as an .npy file we can automatically quantize this model for better on-device performance.

Upload progress

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Click 'Build' to begin

Step 2: Process ""

Configure model settings for optimal processing.

Model input

Input shape: ()

How is your input scaled?

Input should be in RGB format (one value per pixel). If your model uses a different channel order, or is scaled differently, then select "Other".

Data frequency

Hz.

Input length: NaN ms.

Model output

Output shape: ()

Output layer

Unsupported final layer? Let your solutions engineer know, or ask on the forum.

Minimum confidence rating

Found objects with a score below this rating are ignored (enter a value between 0 and 1).

Number of classes

Output labels (undefined)

Enter labels for your model separated by ','.

On-device performance

Check model behavior

Upload test data to ensure correct model settings and proper model processing. (Optional)

Target device

Configure your target device for model performance calculations, and to enable target specific optimizations.

Show logs

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