mmpretrain
mmsegmentation
| mmpretrain | mmsegmentation | |
|---|---|---|
| 2 | 7 | |
| 3,171 | 7,436 | |
| 2.6% | 2.1% | |
| 7.8 | 8.2 | |
| 17 days ago | 12 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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mmpretrain
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMClassification: OpenMMLab image classification toolbox and benchmark.
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how to recognize digits from this pics(i have many of them) so kindly suggest generic that can work for other similar images. I have searched alot for the source code on github but not found the correct solution. most of these solutions were incorrect while other were incomplete. Kindly help me :(
MMClassification or TIMM would be good starting points for training a classification model.
mmsegmentation
- [D] The MMSegmentation library from OpenMMLab appears to return the wrong results when computing basic image segmentation metrics such as the Jaccard index (IoU - intersection-over-union). It appears to compute recall (sensitivity) instead of IoU, which artificially inflates the performance metrics.
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Is there any ML model out there for room surfaces detection? (ceiling, floor, windows)
Segmentation models trained on datasets like ADE20k could probably be used for that, because it has separate classes for these things iirc. https://github.com/open-mmlab/mmsegmentation should have suitable pretrained models available.
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
- Mmsegmentation - Openmmlab semantic segmentation toolbox and benchmark.
- Mmsegmentation – Openmmlab semantic segmentation toolbox and benchmark
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Semantic Segmentation models
This repo is amazing: https://github.com/open-mmlab/mmsegmentation
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What's A Simple Custom Segmentation Pipeline?
Mmsegmentation would be a good place to start for basic segmentation. They have lots of recent methods and pretained models you could fine-tune from. They also support quite a few datasets including VOC. There is a custom dataset format which looks straightforward to create.
What are some alternatives?
efficientnet - Implementation of EfficientNet model. Keras and TensorFlow Keras.
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
ModelZoo.pytorch - Hands on Imagenet training. Unofficial ModelZoo project on Pytorch. MobileNetV3 Top1 75.64🌟 GhostNet1.3x 75.78🌟
Swin-Transformer-Semantic-Segmentation - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.
senet.pytorch - PyTorch implementation of SENet
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
AdelaiDet - AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.
Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
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
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
TFLiteClassification - TensorFlow Lite Image Classification Python Implementation
PaddleSeg - Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.