xiao-edgeimpulse-train

Original：🇺🇸 English

Translated

Train and deploy a TinyML model for the XIAO ESP32S3 (Sense) using the Edge Impulse REST API only — no edge-impulse-cli needed (its serialport dep fails to build on modern Node/Windows). Covers: dataset upload, impulse creation (audio MFCC / vision transfer-learning), training jobs, downloading the Arduino library, and the on-device fixes required to actually run it on the ESP32-S3. Use this skill whenever the user wants to train/retrain a model ("재훈련", "edge impulse", "TinyML 훈련", "모델 배포"), upload a dataset to Edge Impulse, or gets EI Arduino-library build/runtime errors (mel filterbank, objs.a, tensor arena, EI_MAX_OVERFLOW_BUFFER_COUNT).

3installs

Sourceshain1912/esp32-skills

Added on2026-08-11

NPX Install

npx skill4agent add shain1912/esp32-skills xiao-edgeimpulse-train

SKILL.md Content

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Edge Impulse training pipeline for XIAO ESP32S3 (REST API, verified)

Everything below was executed and verified end-to-end (2026-07). The edge-impulse-cli is NOT needed — do not try to install it (its serialport@8 dependency needs VS C++ build tools on Node 22+).

Step 0 — API key (ask the user)

Ask the user for an Edge Impulse admin API key (

ei_...

), or read it from the project's

.env

(

EI_API_KEY=...

) if the user points you there.

The auto-created project key has role "Ingestion & deployment" — it can upload data but cannot create impulses or start training (symptom:
```
insufficient permissions (valid roles: [admin])
```
).
If needed, tell the user: Studio → Dashboard → Keys → Add new API key → Role Admin (full access) → copy the full key immediately (it is truncated in the list afterwards).

Get the project id:

GET https://studio.edgeimpulse.com/v1/api/projects

with header

x-api-key

→

projects[0].id

.

PowerShell warning:
```
$pid
```
is a READ-ONLY reserved variable — use
```
$proj
```
.

Step 1 — upload dataset (ingestion API)

One POST per file; label comes from the

x-label

header:

powershell

curl.exe -s -X POST -H "x-api-key: $key" -H "x-label: $cls" `
  -F "data=@$($f.FullName)" https://ingestion.edgeimpulse.com/api/training/files

WAV: 16 kHz 16-bit mono. Images: jpg/png as-is. After uploading everything, auto-split train/test:

POST /v1/api/$proj/rebalance

.

One project = one model. To retrain for a different task first wipe:

POST /v1/api/$proj/raw-data/delete-all

(archive any deployed lib zip first).

Step 2 — create the impulse

POST https://studio.edgeimpulse.com/v1/api/$proj/impulse

(POST, not PUT).

Audio (keyword spotting) —

implementationVersion: 4

on the MFCC block is REQUIRED; without it you get v1 and feature generation fails with "mel filterbank contains all zeros":

json

{"inputBlocks":[{"id":1,"type":"time-series","name":"Time series data","title":"Time series data","windowSizeMs":1000,"windowIncreaseMs":500,"frequencyHz":16000,"padZeros":true}],
 "dspBlocks":[{"id":2,"type":"mfcc","name":"MFCC","axes":["audio"],"title":"MFCC","implementationVersion":4}],
 "learnBlocks":[{"id":3,"type":"keras","name":"Classifier","dsp":[2],"title":"Classification"}]}

Vision (image classification, MobileNet transfer learning):

json

{"inputBlocks":[{"id":1,"type":"image","name":"Images","title":"Image data","imageWidth":96,"imageHeight":96,"resizeMode":"squash"}],
 "dspBlocks":[{"id":2,"type":"image","name":"Image","axes":["image"],"title":"Image","implementationVersion":1}],
 "learnBlocks":[{"id":3,"type":"keras-transfer-image","name":"Transfer learning","dsp":[2],"title":"Transfer learning (Images)"}]}

Step 3 — generate features, then train

powershell

# start: returns a job id
POST /v1/api/$proj/jobs/generate-features   body: {"dspId":2,"calculateFeatureImportance":false}
# poll until finished (10-15 s interval):
GET  /v1/api/$proj/jobs/$jobId/status   -> job.finished / job.finishedSuccessful
# on failure read newest-first logs:
GET  /v1/api/$proj/jobs/$jobId/stdout   -> stdout[0..] .data

# train (after features succeed):
POST /v1/api/$proj/jobs/train/keras/3
#   audio body:  {"trainingCycles":100,"learningRate":0.005}
#   vision body: {"trainingCycles":20,"learningRate":0.0005}

Accuracy is in the train job stdout (

val_accuracy

lines). Keep each poll loop's total wait under your tool timeout — resume polling in a new call rather than one giant sleep.

Step 4 — build + download the Arduino library

powershell

POST /v1/api/$proj/jobs/build-ondevice-model?type=arduino   body: {"engine":"tflite-eon"}
# poll job, then:
GET /v1/api/$proj/deployment/download?type=arduino   -> save as <name>.zip

Install: find the sketchbook with

arduino-cli config get directories.user

, extract into

<sketchbook>\libraries\<project>_inferencing

. The zip usually nests one folder — flatten so

src\<project>_inferencing.h

exists. Archive the zip per task (audio/vision) — swapping tasks overwrites the lib.

Step 5 — on-device fixes (ESP32-S3, REQUIRED for vision)

--clean
on the first compile after swapping the library. The arduino-cli cache keeps stale objects for the same library name/version — symptoms:
```
objs.a ... is not an object
```
link errors, or edits that appear to change nothing (identical byte sizes).

Tensor arena overflow crash (

Failed to allocate persistent buffer ... EI_MAX_OVERFLOW_BUFFER_COUNT

+ Guru Meditation on boot): edit

src\edge-impulse-sdk\porting\ei_classifier_porting.h

— near the end,

#if defined(CONFIG_IDF_TARGET_ESP32S3)

hard-defines

EI_MAX_OVERFLOW_BUFFER_COUNT 30

with no #ifndef guard, so -D flags and model-file defaults are ALL ignored. Change

to

, then

--clean

.

Put the arena in PSRAM — add to the sketch (the SDK's allocators are weak symbols) and build with
```
--board-options PSRAM=opi
```
:

cpp

#include "esp_heap_caps.h"
void *ei_malloc(size_t size) {
  void *p = heap_caps_aligned_alloc(16, size, MALLOC_CAP_SPIRAM);
  if (!p) p = heap_caps_aligned_alloc(16, size, MALLOC_CAP_DEFAULT);
  return p;
}
void *ei_calloc(size_t n, size_t s) { void *p = ei_malloc(n*s); if (p) memset(p,0,n*s); return p; }
void ei_free(void *ptr) { heap_caps_free(ptr); }

Step 6 — inference sketch integration

Feed data via the static-buffer pattern (
```
signal.get_data
```
), NOT the EI example's
```
<I2S.h>
```
code (breaks on esp32 core 3.x — use
```
ESP_I2S.h
```
).
Vision feature packing: one float per pixel =
```
(r<<16)|(g<<8)|b
```
.
Camera on XIAO Sense renders rotated 90°: rotate CW while downscaling (
```
sx = y*W/H; sy = H-1-(x*H/W)
```
) or accuracy collapses. Verify orientation empirically by classifying one frame 3 ways (none/cw/ccw) and comparing.
Audio level gap: mic audio is far quieter than full-scale training WAVs — best fix is collecting training data through the device mic itself.
Camera AWB needs warmup: grab and discard ~8 frames before the real capture.

xiao-edgeimpulse-train

NPX Install

Tags

SKILL.md Content

Edge Impulse training pipeline for XIAO ESP32S3 (REST API, verified)

Step 0 — API key (ask the user)

Step 1 — upload dataset (ingestion API)

Step 2 — create the impulse

Step 3 — generate features, then train

Step 4 — build + download the Arduino library

Step 5 — on-device fixes (ESP32-S3, REQUIRED for vision)

Step 6 — inference sketch integration