espressif/esp-dl

3.3.4

uploaded 2 months ago
esp-dl is a lightweight and efficient neural network inference framework designed specifically for ESP series chips.
Readme Dependencies 4 Dependents 14 Examples 19 Versions 26

19 examples

  • how_to_deploy_streaming_model 1
  • cat_detect
    A simple image inference example using cat detection with configurable options for model components.
    226.87 KB
  • color_detect
    A simple image inference example.
    13.26 KB
  • dog_detect
    A simple image inference example for dog detection using a test image with configurable model options.
    634.21 KB
  • hand_detect
    A simple image inference example using hand detection. It provides results before and after quantization on ESP32-P4.
    366.08 KB
  • hand_gesture_recognition
    A simple image inference example that recognizes 10 hand gestures plus a 'no_hand' category using the HaGRID dataset.
    751.48 KB
  • how_to_run_model
    12.84 KB
  • human_face_detect
    A simple image inference example for human face detection using ESP-IDF. See full example in the esp-who repository.
    43.03 KB
  • human_face_recognition
    A simple image inference example for human face recognition using ESP-IDF with configurable model options.
    103.18 KB
  • mobilenetv2_cls
    A simple image inference example for IMAGENET classification using ESP-IDF.
    39.50 KB
  • model_in_flash_partition
    10.74 KB
  • model_in_flash_rodata
    10.88 KB
  • model_in_sdcard
    10.71 KB
  • motion_detect
    52.61 KB
  • pedestrian_detect
    A simple image inference example for pedestrian detection, part of the esp-who project.
    97.44 KB
  • speaker_verification
    A simple audio inference example for speaker verification using three audio samples from different speakers.
    682.25 KB
  • yolo11_detect
    A simple image inference example using Yolo11 for object detection, demonstrating results before and after quantization.
    670.85 KB
  • yolo11_pose
    A simple image inference example using Yolo11 Pose with ESP32-P4 and default settings.
    267.49 KB
  • yolo26_detect
    This is a complete end-to-end example for running quantized YOLOv26n inference on Espressif SoCs.
    3.21 MB