yolov7
multiface
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| yolov7 | multiface | |
|---|---|---|
| 33 | 4 | |
| 12,681 | 703 | |
| - | 1.8% | |
| 4.0 | 4.4 | |
| 7 days ago | 9 months ago | |
| Jupyter Notebook | Python | |
| GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
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.
yolov7
- 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
multiface
- [D] Most Popular AI Research July 2022 pt. 2 - Ranked Based On GitHub Stars
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Researchers at Meta AI Develop Multiface: A Dataset for Neural Face Rendering
Continue reading | Checkout the paper and github link.
- Most Popular AI Research July 2022 pt. 2 - Ranked Based On GitHub Stars
- Multiface: Meta multi-view, high-resolution human face dataset
What are some alternatives?
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
XMem - [ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
edgetpu - Coral issue tracker (and legacy Edge TPU API source)
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.
edgetpu-yolo - Minimal-dependency Yolov5 export and inference demonstration for the Google Coral EdgeTPU
Cream - This is a collection of our NAS and Vision Transformer work. [Moved to: https://github.com/microsoft/AutoML]
YOLOv4 - Port of YOLOv4 to C# + TensorFlow
hivemind - Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.
darknet - Convolutional Neural Networks
theseus - A library for differentiable nonlinear optimization
NUWA - A unified 3D Transformer Pipeline for visual synthesis