deeplab2
yolov7
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deeplab2 | yolov7 | |
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5 | 33 | |
980 | 12,530 | |
0.9% | - | |
4.0 | 4.0 | |
12 months ago | about 1 month ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | GNU General Public License v3.0 only |
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deeplab2
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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[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:
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DL for the Web: Repository of Models
Github Projects offering pretrained weights and train / run scripts. Example
- [D] yolov7 not finding GPU device: Solution
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yolov7-pose: What does the output layers of an exported onnx model mean?
I played around with the demo code of human pose estimation of the yolov7 model (https://github.com/WongKinYiu/yolov7/tree/pose) and wanted to convert this to onnx format in order to continue in a C++ environment.
- [P] Any object detection library
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I made the “AI invisibility cloak." Ask AI expert Tom Goldstein about security and safety of AI systems, and how to hack them.
I would add that Yolo has evolved over time, and variants of the architecture continue to come out today. Here's one that came out this summer: https://github.com/WongKinYiu/yolov7
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What is the correct strides when add decoding level to a mlmodel converted from yolov7
there is a trained custom model with yolov7 that was converted to mlmodel with export.py (from the yolov7 repository: https://github.com/WongKinYiu/yolov7).
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Use YOLOv5 tensorflow.js models to speed up annotation
Hi u/DDDqp! YOLOv7 is for sure on my list. Problem is that, as it is right now YOLOv7 does not offer export to tensorflow.js. I actually created the issue by asking if they plan to add that export: https://github.com/WongKinYiu/yolov7/issues/885 No response yet. But I think it is more than possible. I can even work on that export for them. If they would have exported to tfjs, than I most certainly can work on the NPM package.
What are some alternatives?
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
edgetpu - Coral issue tracker (and legacy Edge TPU API source)
edgetpu-yolo - Minimal-dependency Yolov5 export and inference demonstration for the Google Coral EdgeTPU
YOLOv4 - Port of YOLOv4 to C# + TensorFlow
darknet - Convolutional Neural Networks
XMem - [ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
BCNet - Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [CVPR 2021]
CATNet - 🛰️ Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images (TNNLS 2023)
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
yolact - A simple, fully convolutional model for real-time instance segmentation.
YOLOv6 - YOLOv6: a single-stage object detection framework dedicated to industrial applications.
Cream - This is a collection of our NAS and Vision Transformer work. [Moved to: https://github.com/microsoft/AutoML]