XMem
[ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model (by hkchengrex)
yolov7
Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (by WongKinYiu)
| XMem | yolov7 | |
|---|---|---|
| 11 | 33 | |
| 1,722 | 13,215 | |
| - | - | |
| 6.3 | 1.3 | |
| 6 months ago | 29 days ago | |
| Python | Jupyter Notebook | |
| MIT License | GNU General Public License v3.0 only |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
XMem
Posts with mentions or reviews of XMem.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-11.
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[D] Which open source models can replicate wonder dynamics's drag'n'drop cg characters?
Use Segmentation Model (SAM) combined with Inpainting model (E2FGVI) and Xmem to cut out the live action subject.
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Track-Anything: a flexible and interactive tool for video object tracking and segmentation, based on Segment Anything and XMem.
Nvm just found the occlusion video on https://github.com/hkchengrex/XMem holy shit
- XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
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[D] Most important AI Paper´s this year so far in my opinion + Proto AGI speculation at the end
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model ( Added because of the Atkinson-Shiffrin Memory Model ) Paper: https://arxiv.org/abs/2207.07115 Github: https://github.com/hkchengrex/XMem
- [D] Most Popular AI Research July 2022 pt. 2 - Ranked Based On GitHub Stars
- Most Popular AI Research July 2022 pt. 2 - Ranked Based On GitHub Stars
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I trained a neural net to watch Super Smash Bros
Yeah MiVOS would speed up your tagging a lot. I also was curious if you saw XMem which just came out. I found that worked really well too.
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University of Illinois Researchers Develop XMem; A Long-Term Video Object Segmentation Architecture Inspired By Atkinson-Shiffrin Memory Model
Continue reading | Check out the paper and github link.
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[R] Unicorn: 🦄 : Towards Grand Unification of Object Tracking(Video Demo)
Have you check XMem?
yolov7
Posts with mentions or reviews of yolov7.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-10-17.
- FLaNK Stack Weekly 16 October 2023
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Train a ML model able to identify animal species
If you want something off-the-shelf, try YoloV7.
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A video based Latin dictionary: get what you see in Latin (beta) - What do you think?
The current dictionary is still in a beta state and has only been trained on 80 words (e.g. 'man', 'dog', 'car', 'keyboard', 'book', etc.; see list of words, see dataset). I used the object detection model Yolov7 (paper, all credits to them).
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[D] Extracting the class labels and bounding boxes for objects, from a YOLO7 model after converting to an ONNX model
(Please note, this is a re-post of my original question here, I think this subreddit might be more appropriate for asking this question)At work, we use Unity, we have a project that needs object detection and classification. We decided to use this YOLO7 model (for non-technical reasons, It had to be the exact same model as the company does have pre-trained weights for this exact model). However, Unity only supports ONNX so I exported the model as an ONNX model, using the code provided in the repo:
- Coding Question Help
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DL for the Web: Repository of Models
Github Projects offering pretrained weights and train / run scripts. Example
- [OC] Football Player 3D Pose Estimation using YOLOv7 and Matplotlib
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Finding a good Tiny Yolo to train in Python
The only project I found is this one that implements Yolov7
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Visualizing image augmentations from YOLOV7
I'm wondering if there's an efficient way to visualize the image augmentations from the Yolov7 hyperparameters list here
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Train YOLOv8 ObjectDetection on Custom Dataset Tutorial
yolov7: https://github.com/WongKinYiu/yolov7#performance
What are some alternatives?
When comparing XMem and yolov7 you can also consider the following projects:
flash-attention - Fast and memory-efficient exact attention
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
NAFNet - The state-of-the-art image restoration model without nonlinear activation functions.
edgetpu - Coral issue tracker (and legacy Edge TPU API source)
hivemind - Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.
edgetpu-yolo - Minimal-dependency Yolov5 and Yolov8 export and inference demonstration for the Google Coral EdgeTPU
EfficientZero - Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021.
YOLOv4 - Port of YOLOv4 to C# + TensorFlow
deeplab2 - DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a unified and state-of-the-art TensorFlow codebase for dense pixel labeling tasks.
darknet - Convolutional Neural Networks
NUWA - A unified 3D Transformer Pipeline for visual synthesis
BCNet - Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [CVPR 2021]