yolov3-tf2
saliency
| yolov3-tf2 | saliency | |
|---|---|---|
| 3 | 4 | |
| 2,510 | 932 | |
| - | 0.6% | |
| 2.4 | 3.6 | |
| 24 days ago | 2 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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yolov3-tf2
saliency
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[D] Is the math in Integrated gradients (4K citations) wrong?
Found relevant code at https://github.com/PAIR-code/saliency + all code implementations here
- How to display which parts of a single image a Keras model found to be the most significant when making a prediction?
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Gradients of model output layer and intermediate layer wrt inputs
I’m trying to visualize model layer outputs using the saliency core package package on a simple conv net. This requires me to compute the gradients of the model output layer and intermediate convolutional layer output w.r.t the input. I’ve attempted to do this in the last code block, but I run into the error
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A Visual History of Interpretation for Image Recognition
[2]: https://github.com/PAIR-code/saliency
What are some alternatives?
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yolact - Tensorflow 2.x implementation YOLACT
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docs - TensorFlow documentation
TACO - 🌮 Trash Annotations in Context Dataset Toolkit
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tensorflow-lite-YOLOv3 - YOLOv3: convert .weights to .tflite format for tensorflow lite. Convert .weights to .pb format for tensorflow serving
One-Piece-Image-Classifier - A quick image classifier trained with manually selected One Piece images.