Loading repository data…
MeqdadDev / repository
A lightweight Python package optimized for integrating exported models from Google's Teachable Machine Platform into robotics and embedded systems environments. This streamlined version of Teachable Machine Package is specifically designed for resource-constrained devices, making it easier to deploy and use your trained models in embedded apps.
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
By: Meqdad Darwish
A lightweight Python package optimized for integrating exported models from Google's Teachable Machine Platform into robotics and embedded systems environments. This streamlined version of Teachable Machine Package is specifically designed for resource-constrained devices, making it easier to deploy and use your trained models in embedded applications. With a focus on efficiency and minimal dependencies, this tool maintains the core functionality while being more suitable for robotics and IoT projects.
Source Code is published on GitHub
Read more about the project (requirements, installation, examples and more) in the Documentation Website
Image Classification: Use exported and quantized TensorFlow Lite model from Teachable Machine Platform (a model file with tflite extension).
For detailed information about package requirements and dependencies, please visit our documentation
Python >= 3.9
numpy < 2.0 (v1.26.4 recommended)
pip install teachable-machine-lite
numpy
tflite-runtime
Pillow
Selected from shared topics, language and repository description—not editorial ratings.
wkcn /
A Lightweight & Flexible Deep Learning (Neural Network) Framework in Python
48/100 healthFestus0 /
A lightweight PyTorch framework for building, training, and documenting deep neural network experiments.
46/100 healthmihaicodes /
⚡TinyTensor is a lightweight open-source deep learning library built from scratch in Python, inspired by Joel Grus' approach.
An example for teachable machine lite package with OpenCV:
from teachable_machine_lite import TeachableMachineLite
import cv2 as cv
cap = cv.VideoCapture(0)
model_path = "model.tflite"
labels_path = "labels.txt"
image_file_name = "screenshot.jpg"
tm_model = TeachableMachineLite(model_path=model_path, labels_file_path=labels_path)
while True:
ret, img = cap.read()
cv.imwrite(image_file_name, img)
results, resultImage = tm_model.classify_and_show(image_file_name, convert_to_bgr=True)
print("results:", results)
cv.imshow("Camera", resultImage)
k = cv.waitKey(1)
if k == 27: # Press ESC to close the camera view
break
cap.release()
cv.destroyAllWindows()
Values of results are assigned based on the content of labels.txt file.
For more; take a look on these examples
mohamedlefliti /
M2K provides a high-level, PyTorch-like interface for defining models, training loops, and TPU setup, while staying lightweight and transparent. Designed for researchers and students who want to learn how deep learning frameworks work or experiment quickly without heavy dependencies.
48/100 health