Deep-Residual-Learning-for-Image-Recognition
Implementation of https://arxiv.org/pdf/1512.03385.pdf (by c1ph3rr)
tpu
Reference models and tools for Cloud TPUs. (by tensorflow)
| Deep-Residual-Learning-for-Image-Recognition | tpu | |
|---|---|---|
| 2 | 5 | |
| 7 | 5,182 | |
| - | 0.2% | |
| 10.0 | 7.5 | |
| about 5 years ago | 10 days ago | |
| Python | Jupyter Notebook | |
| - | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
Deep-Residual-Learning-for-Image-Recognition
Posts with mentions or reviews of Deep-Residual-Learning-for-Image-Recognition.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-28.
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Variance in reported results on ImageNet between papers [D]
Found relevant code at https://github.com/c1ph3rr/Deep-Residual-Learning-for-Image-Recognition + all code implementations here
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Resnet34 implementation for a beginner
Code for https://arxiv.org/abs/1512.03385 found: https://github.com/c1ph3rr/Deep-Residual-Learning-for-Image-Recognition
tpu
Posts with mentions or reviews of tpu.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-28.
-
Variance in reported results on ImageNet between papers [D]
Found relevant code at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + all code implementations here
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[D] What is the smallest, most capable, generative language model available now?
I'm looking for a generative-LM equivalent of an EfficientNet-Lite, for inference on devices with limited to no VRAM. I know about some popular ones like DistilGPT2. But it's been 2 years after its release. Surely, someone improved their size/performance ratio, right... right?
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Open source - that means free to use commercially right? ... right?
tensorflow/TPU 'apache' license - https://github.com/tensorflow/tpu/commit/6b3236d0271d2f2c3b2dfdc9d233ff00c4ba21cd
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[P] EfficientNet-lite in Keras (functional API).
According to original repository, the lite variants:
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Why do some architectures use no bias?
I was quite surprised to see that some architectures, like efficient net (official implementation: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py) don't use bias (bias=False). Does anyone know why is that? Apart from the obvious benefit of reducing parameters, doesn't it make the network less capable of learning certain representations?
What are some alternatives?
When comparing Deep-Residual-Learning-for-Image-Recognition and tpu you can also consider the following projects:
mask-rcnn - Mask-RCNN training and prediction in MATLAB for Instance Segmentation
efficientnet-lite-keras - Keras reimplementation of EfficientNet Lite.
Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow