SSD-pytorch
ssd_keras
| SSD-pytorch | ssd_keras | |
|---|---|---|
| 6 | 4 | |
| 159 | 1,846 | |
| - | - | |
| 0.0 | 0.0 | |
| about 3 years ago | about 2 years ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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SSD-pytorch
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Minimal implementation of SSD: Single Shot MultiBox Detector
Source code: https://github.com/uvipen/SSD-pytorch
Source code: https://github.com/uvipen/SSD-pytorch
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My minimal implementation of SSD: Single Shot MultiBox Detector
Source code: https://github.com/uvipen/SSD-pytorch
ssd_keras
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Failed to get convolution algorithm. This is probably because cuDNN failed to initialize,
In Tensorflow/ Keras when running the code from https://github.com/pierluigiferrari/ssd_keras, use the estimator: ssd300_evaluation. I received this error.
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Shared weights between different implementations
Yeah, the order of axes was different between those 2. Another guy used https://github.com/pierluigiferrari/ssd_keras https://github.com/uhfband/keras2caffe/blob/master/keras2caffe/convert.py probably not much actual use but maybe some more reassurance?
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Simplest way to deploy Keras NN model into C++?
Don't know about simplest, but we either used caffe or tensorrt, it is maybe a bit difficult to use but I'd actually say simple fast GPU inference is what it's geared towards. There is a keras -> caffe converter https://github.com/pierluigiferrari/ssd_keras here, I think. Caffe is a c++ lib, typical, with dependencies and all. I've never heard anything of tensorflow running on c++. But with tensorrt you should get an "artifact" that you'd load, no matter where it comes from
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ValueError: Layer model expects 1 input(s), but it received 2 input tensors. Help?
Tensorflow V1 Keras code (original repo): Github Repo
What are some alternatives?
a-PyTorch-Tutorial-to-Object-Detection - SSD: Single Shot MultiBox Detector | a PyTorch Tutorial to Object Detection
layout-parser - A Unified Toolkit for Deep Learning Based Document Image Analysis
faceswap - Deepfakes Software For All
cppflow - Run TensorFlow models in C++ without installation and without Bazel
DeepFaceLab - DeepFaceLab is the leading software for creating deepfakes.
zero-shot-object-tracking - Object tracking implemented with the Roboflow Inference API, DeepSort, and OpenAI CLIP.
torchsde - Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
efficientnet-lite-keras - Keras reimplementation of EfficientNet Lite.
pytorch-efficientdet-api - A PyTorch EfficientDet API for easy training and inference on custom datasets.
Mask-RCNN-TF2 - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow 2.0
DETReg - Official implementation of the CVPR 2022 paper "DETReg: Unsupervised Pretraining with Region Priors for Object Detection".