tfops-aug
TFOps-Aug: Implementation of policy-based image augmentation techniques based on TF2 Operations. All augmentations as efficient Tensorflow 2.11.0 operations. Easy integration into a tf.data API pipeline. (by TillBeemelmanns)
tensorflow-yolov4-tflite
YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite (by hunglc007)
tfops-aug | tensorflow-yolov4-tflite | |
---|---|---|
1 | 4 | |
14 | 2,225 | |
- | - | |
4.9 | 0.0 | |
over 1 year ago | 11 months ago | |
Python | Python | |
MIT License | MIT License |
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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tfops-aug
Posts with mentions or reviews of tfops-aug.
We have used some of these posts to build our list of alternatives
and similar projects.
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tfops-aug: Lightweight and fast image augmentation library based on TensorFlow Ops
With tfops-aug, you can easily apply an augmentation policy to your images, including shearing, translations, random gamma, random color shifts, solarization, posterization, histogram equalization, and more. The library is fully compatible with Tensorflow's data pipelines, so you can easily integrate it into your existing projects. And because it uses only Tensorflow operations, it's fast and efficient and can be directly applied on a tf.Tensor of type tf.uint8.
tensorflow-yolov4-tflite
Posts with mentions or reviews of tensorflow-yolov4-tflite.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-05-27.
- Object tracking on Android
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Tensorflow Yolo Implementation
Hello all, I've been desperate for help in the tensorflow yolo objection detection framework. Basically, in https://github.com/hunglc007/tensorflow-yolov4-tflite 's implementation, there is problem whereby with GPU, it only detects the first frame in the whole video while CPU works fine but absolutely slow. There are issues opened at https://github.com/hunglc007/tensorflow-yolov4-tflite/issues/282 but no proper solution is found. I've also opened a question stackoverflow: https://stackoverflow.com/questions/68333281/tensorflow-yolov4-detect-video
- “ValueError: cannot reshape array of size 278540 into shape (256,128,3,3)” Conversion YOLOv3 .weights to .pb