fast-reid
yolov7
| fast-reid | yolov7 | |
|---|---|---|
| 3 | 33 | |
| 3,278 | 12,739 | |
| 0.8% | - | |
| 1.2 | 3.2 | |
| 5 months ago | 4 days ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | GNU General Public License v3.0 only |
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fast-reid
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DeepSort with PyTorch(support yolo series)
fast-reid
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Assign ID and track moving object with optical flow
On failure, you can try using a re-identification methods like FastReid: https://github.com/JDAI-CV/fast-reid in combination with your detector. A good pipeline that combines everything you seem to need is here: https://github.com/GeekAlexis/FastMOT. It uses a combination of Yolov4 (detector) + Kalman filters, Optical flow (tracker) and FastReid (re-identification)
- Safari translation won't work when lang="en" attribute is set!
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
What are some alternatives?
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
l2rpn-baselines - L2RPN Baselines a repository to host baselines for l2rpn competitions.
edgetpu - Coral issue tracker (and legacy Edge TPU API source)
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
edgetpu-yolo - Minimal-dependency Yolov5 export and inference demonstration for the Google Coral EdgeTPU
DOLG-pytorch - Unofficial PyTorch Implementation of "DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features"
YOLOv4 - Port of YOLOv4 to C# + TensorFlow
apex-configs-by-deafps - Apex config & tweaks
darknet - Convolutional Neural Networks
Hekate-Toolbox - A toolbox for Hekate
XMem - [ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model