Perceiver
Swin-Transformer-Object-Detection
| Perceiver | Swin-Transformer-Object-Detection | |
|---|---|---|
| 7 | 4 | |
| 85 | 1,710 | |
| - | 0.1% | |
| 2.6 | 0.0 | |
| about 3 years ago | about 1 year ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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.
Perceiver
- I implemented Deepmind's new Perceiver Model
- I Implemented Deepmind's Perceiver Model
-
[P] I implemented DeepMind's "Perceiver" in PyTorch
Great one, I implemented the Perceiver model too in TensorFlow: https://github.com/Rishit-dagli/Perceiver
- Deepmind's New Perceiver Model
-
[P] Implementing Perceiver: General perception with Iterative Attention in TensorFlow
The project: https://github.com/Rishit-dagli/Perceiver
- Perceiver, General Perception with Iterative Attention
Swin-Transformer-Object-Detection
-
Transfer Learning on Swin Transformer as a backbone for instance segmentation using MRCNN
I'm currently trying to transfer learn a set of custom classes of fish, for instance segmentation. I have found the official implementation of Swin Transformer as a backbone for instance segmentation using MRCNN: https://github.com/SwinTransformer/Swin-Transformer-Object-Detection.
-
Advice on Masters project | Vision transformers
Hi, So my project is to do with object detection on trash in the wild on this fairly obscure dataset: http://tacodataset.org/ and I was thinking of applying vision transformers to it for feature extraction. I was thinking of taking the YOLOX implementation and swapping out the backbone with swin transformers and perform bunch of comparisons/experiments for the write up. Sort of like how they applied swin transformers to mask R-CNN here but I am struggling to understand where to begin.
-
[P] I implemented DeepMind's "Perceiver" in PyTorch
Yes, have a look at this paper.
-
[P] Code and pretrained models for Swin Transformer are released (SOTA models on COCO and ADE20K)
Object detection on COCO: https://github.com/SwinTransformer/Swin-Transformer-Object-Detection
What are some alternatives?
performer-pytorch - An implementation of Performer, a linear attention-based transformer, in Pytorch
Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
Fast-Transformer - An implementation of Fastformer: Additive Attention Can Be All You Need, a Transformer Variant in TensorFlow
YOLOX - YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
TimeSformer-pytorch - Implementation of TimeSformer from Facebook AI, a pure attention-based solution for video classification
Video-Swin-Transformer - This is an official implementation for "Video Swin Transformers".
gato - Unofficial Gato: A Generalist Agent
Swin-Transformer-Tensorflow - Unofficial implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" (https://arxiv.org/abs/2103.14030)
deepmind-perceiver - My implementation of DeepMind's Perceiver
Swin-Transformer-Semantic-Segmentation - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.
conformer - Implementation of the convolutional module from the Conformer paper, for use in Transformers
Swin-Transformer-Serve - Deploy Swin Transformer using TorchServe