yolact
segmentation_models.pytorch
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| yolact | segmentation_models.pytorch | |
|---|---|---|
| 4 | 14 | |
| 4,919 | 8,773 | |
| - | - | |
| 0.0 | 2.8 | |
| 5 months ago | 7 days ago | |
| Python | Python | |
| MIT License | MIT License |
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yolact
- YOLOv6: Redefine state-of-the-art for object detection
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Instance segmentation
YOLACT
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Advice needed
The Github repo is not highly structured and is probably written during the time of the author's research. Been using this for well over a year and does the job so well.
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A question on Instance Segmentation Labelling
I am using yolact : https://github.com/dbolya/yolact
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?
Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.
yolact_edge - The first competitive instance segmentation approach that runs on small edge devices at real-time speeds.
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
yolov3-tf2 - YoloV3 Implemented in Tensorflow 2.0
EfficientNet-PyTorch - A PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!)
mmdetection - OpenMMLab Detection Toolbox and Benchmark
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
yolo_tracking - BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models
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.
pytorch-image-models - PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
efficientdet-pytorch - A PyTorch impl of EfficientDet faithful to the original Google impl w/ ported weights