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tensorflow's Introduction

tensorflow

Customizations of tensorflow-based applications.

Currently available:

  • efficientdet - Docker image for EfficientDet: Scalable and Efficient Object Detection
  • image-segmentation-keras - Docker image for image segmentation using Keras
  • image classification - Docker images for training image classification models and making predictions with them (uses TensorFlow 1)
  • image classification2 - Docker images for training image classification models and making predictions with them (uses TensorFlow 2)
  • object detection - for building Docker images of TensorFlow's object detection framework
  • tflite_model_maker - library for training models and making predictions using tflite's model maker
  • tfutils - library for some common TensorFlow operations

For generating tfrecords, please see the wai.annotations library, which can convert to and fro various file formats.

tensorflow's People

Contributors

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tensorflow's Issues

Object Detection: Add support for background/negative images

  • using object detection API as of b9ef963d1e84da0bb9c0a6039457c39a699ea149
  • update following files to support use_negative_images_only
    • research/object_detection/core/losses.py
    • research/object_detection/protos/losses.proto
  • Dockerfile generation replaces original files with the modified ones

DeepSpeech: normalize audio

Optional normalization of audio files when batch-processing to adjust for quiet audio:

from pydub import effects
_sound = AudioSegment.from_file("./input.wav", "wav")
sound = effects.normalize(_sound)
sound.export("./output.wav", format="wav")

Taken from here:
jiaaro/pydub#90 (comment)

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