PINTO_model_zoo
ONNX-YOLOv7-Object-Detection
| PINTO_model_zoo | ONNX-YOLOv7-Object-Detection | |
|---|---|---|
| 5 | 2 | |
| 3,301 | 182 | |
| - | - | |
| 9.7 | 0.0 | |
| 6 days ago | about 1 year ago | |
| Python | Python | |
| MIT License | MIT License |
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PINTO_model_zoo
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YOLOv7 object detection in Ruby in 10 minutes
Download the ONNX model from this project: 307_YOLOv7
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stereodemo: compare several recent stereo depth estimation methods in the wild
Hope it might be useful to more people, and thanks to PINTO0309 and ibaiGorordo for converting several pre-trained models to ONNX!
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Loading Saved Models for transfer learning
Check it out https://github.com/PINTO0309/PINTO_model_zoo
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[R][P]MobileStyleGAN: A Lightweight Convolutional Neural Network for High-Fidelity Image Synthesis
Someone reported, that he converted MobileStyleGAN to tfjs (https://github.com/PINTO0309/PINTO_model_zoo), but i didn't check it
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Can we increase the output class in transfer learning?
model:-https://github.com/PINTO0309/PINTO_model_zoo/blob/main/053_BlazePose/01_float32/02_pose_landmark_upper_body_tflite2h5_weight_int_fullint_float16_quant.py
ONNX-YOLOv7-Object-Detection
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[D] Extracting the class labels and bounding boxes for objects, from a YOLO7 model after converting to an ONNX model
Finally, I tried to look if someone has done similar work for the ONNX model and I found this repo which links the same repo I am trying to use, and I believe this function is doing exactly what I want to do, but I could not understand what it is doing (I don't understand how it knows exactly where the number of detections is, and where the bounding boxes are and the class labels, etc.) furthermore, I am not sure if removing end2end and the changing the version from 12 to 9 has any effect on the output shape or it has to do with the internal layers.
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YOLOv7 object detection in Ruby in 10 minutes
git clone https://github.com/ibaiGorordo/ONNX-YOLOv7-Object-Detection.git cd ONNX-YOLOv7-Object-Detection pip install -r requirements.txt
What are some alternatives?
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/
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
RobustVideoMatting - Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!
netron - Visualizer for neural network, deep learning and machine learning models
edgetpu - Coral issue tracker (and legacy Edge TPU API source)
onnxruntime-ruby - Run ONNX models in Ruby
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
models - A collection of pre-trained, state-of-the-art models in the ONNX format
tensorflow-onnx - Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX
AS-One - Easy & Modular Computer Vision Detectors and Trackers - Run YOLO-NAS,v8,v7,v6,v5,R,X in under 20 lines of code.
TensorFlow-object-detection-tutorial - The purpose of this tutorial is to learn how to install and prepare TensorFlow framework to train your own convolutional neural network object detection classifier for multiple objects, starting from scratch
blink-morse - Computer vision application to type based on detection of eyes blinking morse code.