Pointnet_Pointnet2_pytorch
segmentation_models.pytorch
Pointnet_Pointnet2_pytorch | segmentation_models.pytorch | |
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3 | 14 | |
3,187 | 8,844 | |
- | - | |
0.0 | 4.1 | |
8 days ago | 2 days ago | |
Python | Python | |
MIT License | MIT License |
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Pointnet_Pointnet2_pytorch
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Help me understand how to use this PointNet implementation (pytorch, point cloud classification)
here is a sample from one of these log files
I am trying to use this implementation of PointNet (GitHub repo)
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Are the New M1 Macbooks Any Good for Deep Learning? Let’s Find Out
Here you go: https://github.com/yanx27/Pointnet_Pointnet2_pytorch - no need for any custom cuda code.
Note the cuda kernels in the original repo were added in August 2017. It might have been the case at the time they needed them, but again, if you need to do something like that today, you're probably an outlier. Modern DL library have a pretty vast assortment of ops. There have been a few cases in the last couple of years when I thought I'd need write a custom op in cuda (e.g. np.unpackbits) but every time I found a way to implement it with native Pytorch ops.
If you're doing DL/CV research, can you give an example from your own work where you really need to run custom cuda code today?
segmentation_models.pytorch
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Instance segmentation of small objects in grainy drone imagery
Also, I’d suggest considering switching to the segmentation-models library - it provides U-Net models with a variety of pretrained backbones of as encoders. The author also put out a PyTorch version. https://github.com/qubvel/segmentation_models.pytorch https://github.com/qubvel/segmentation_models
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[D] Improvements/alternatives to U-net for medical images segmentation?
SMP offers a wide variety of segmentation models with the option to use pre-trained weights.
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Improvements/alternatives to U-net for medical images segmentation?
SMP has a lot of different choices for architecture other than unet, and a ton of different encoders. I like deeplabv3+/unet with regnety encoder, works well for most things https://github.com/qubvel/segmentation_models.pytorch
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Medical Image Segmentation Human Retina
This basic example from segmentation models PyTorch repo would be good tutorial to start with. The library is very good, I like the unet, fpn and deeplabv3+ architectures with regnety as encoder https://github.com/qubvel/segmentation_models.pytorch/blob/master/examples/binary_segmentation_intro.ipynb
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Automatic generation of image-segmentation mask pairs with StableDiffusion
Sounds like a good semantic segmentation problem, I like this repo: https://github.com/qubvel/segmentation_models.pytorch
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Dice Score not decreasing when doing semantic segmentation
When i pass the CT-Scans and the masks to the Loss Function, which is the Jaccard-Loss from the segmentation_models.pytorch library, the value does not decrease but stay in the range of 1.0-0.9 over 50 epochs training on only one batch of 32 images. As far as I have understood, my network should overfit and the loss should decrease since I am only training on one batch of a small amount of images. However this does not happen. I also tried more batches with all the data over 100 epochs, but the loss does not decrease either obviously. Does anyone have an idea what I might have done wrong? Do I have to change anything when passing the masks to my loss function?
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Good Brain Tumor segmentation model !?
I know there is a decent one in segmentation models python (MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation)
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Advice needed
You could also use qubvel's segmentation models if you would like to explore semantic segmentation.
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[D][R] Is there a standard architecture for U-Nets, pixel-to-pixel models, VAEs, and the like?
Check out segmentation models pytorch, really easy to use, has a great interface.
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Pytorch GPU Memory Leak Problem: Cuda Out of Memory Error !!
Have you tried another implementation? For example: qubvel/segmentation_models.pytorch
What are some alternatives?
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
yolact - A simple, fully convolutional model for real-time instance segmentation.
pointnet2 - PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.
Pix2Vox - The official implementation of "Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images". (Xie et al., ICCV 2019)
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
RAFT
EfficientNet-PyTorch - A PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!)
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
SegmentationCpp - A c++ trainable semantic segmentation library based on libtorch (pytorch c++). Backbone: VGG, ResNet, ResNext. Architecture: FPN, U-Net, PAN, LinkNet, PSPNet, DeepLab-V3, DeepLab-V3+ by now.
SimpleView - Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding