Torchyolo - Yolov3 ve Yolov4 modellerin Pytorch uygulamasıdır

Last update: Aug 22, 2022

Related tags

Deep Learning torchyolo

Overview

TORCHYOLO : Yolo Modellerin Pytorch Uygulaması

Yapılacaklar:

Yolov3 model.py ve detect.py dosyası basitleştirilecek.
Farklı nms algoritmaları test edilecek.

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Comments

Uninstalling the visualization module of Yolov6

This is model use their own visualization libraries. But the visualization parameters are not enough. That's why the visualization module of the torchyolo library will be added.
bug enhancement

opened by kadirnar 0

Releases(v0.0.1)

v0.0.1(Jan 7, 2023)
Yolov7

| Model | Test Size | AP^test | AP₅₀^test | AP₇₅^test | batch 1 fps | batch 32 average time | | :-- | :-: | :-: | :-: | :-: | :-: | :-: | | YOLOv7 | 640 | 51.4% | 69.7% | 55.9% | 161 fps | 2.8 ms | | YOLOv7-X | 640 | 53.1% | 71.2% | 57.8% | 114 fps | 4.3 ms | | | | | | | | | | YOLOv7-W6 | 1280 | 54.9% | 72.6% | 60.1% | 84 fps | 7.6 ms | | YOLOv7-E6 | 1280 | 56.0% | 73.5% | 61.2% | 56 fps | 12.3 ms | | YOLOv7-D6 | 1280 | 56.6% | 74.0% | 61.8% | 44 fps | 15.0 ms | | YOLOv7-E6E | 1280 | 56.8% | 74.4% | 62.1% | 36 fps | 18.7 ms |

Yolov6

Model | Size | mAPval0.5:0.95 | SpeedT4trt fp16 b1(fps) | SpeedT4trt fp16 b32(fps) | Params(M) | FLOPs(G) -- | -- | -- | -- | -- | -- | -- YOLOv6-N | 640 | 37.5 | 779 | 1187 | 4.7 | 11.4 YOLOv6-S | 640 | 45.0 | 339 | 484 | 18.5 | 45.3 YOLOv6-M | 640 | 50.0 | 175 | 226 | 34.9 | 85.8 YOLOv6-L | 640 | 52.8 | 98 | 116 | 59.6 | 150.7 YOLOv6-N6 | 1280 | 44.9 | 228 | 281 | 10.4 | 49.8 YOLOv6-S6 | 1280 | 50.3 | 98 |108 | 41.4 | 198.0 YOLOv6-M6 | 1280 | 55.2 | 47 | 55 | 79.6 | 379.5 YOLOv6-L6 | 1280 | 57.2 | 26 | 29 | 140.4 | 673.4

Yolov5

| Model | size
^{(pixels) | mAP^{val
50-95 | mAP^{val
50 | Speed
^{CPU b1
(ms) | Speed
^{V100 b1
(ms) | Speed
^{V100 b32
(ms) | params
^{(M) | FLOPs
^{@640 (B) |
|------------------------------------------------------------------------------------------------------|-----------------------|----------------------|-------------------|------------------------------|-------------------------------|--------------------------------|--------------------|------------------------|
| YOLOv5n | 640 | 28.0 | 45.7 | 45 | 6.3 | 0.6 | 1.9 | 4.5 |
| YOLOv5s | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
| YOLOv5m | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
| YOLOv5l | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
| YOLOv5x | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
| | | | | | | | | |
| YOLOv5n6 | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
| YOLOv5s6 | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
| YOLOv5m6 | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
| YOLOv5l6 | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
| YOLOv5x6
+ [TTA] | 1280
1536 | 55.0
55.8 | 72.7
72.7 | 3136
- | 26.2
- | 19.4
- | 140.7
- | 209.8
- |}}}}}}}}

YOLOX

|Model |size |mAP^{val
0.5:0.95 |mAP^{test
0.5:0.95 | Speed V100
(ms) | Params
(M) |FLOPs
(G)| weights |
| ------ |:---: | :---: | :---: |:---: |:---: | :---: | :----: |
|YOLOX-s |640 |40.5 |40.5 |9.8 |9.0 | 26.8 | github |
|YOLOX-m |640 |46.9 |47.2 |12.3 |25.3 |73.8| github |
|YOLOX-l |640 |49.7 |50.1 |14.5 |54.2| 155.6 | github |
|YOLOX-x |640 |51.1 |51.5 | 17.3 |99.1 |281.9 | github |
|YOLOX-Darknet53 |640 | 47.7 | 48.0 | 11.1 |63.7 | 185.3 | github |}}

|Model |size |mAP^{val
0.5:0.95 | Params
(M) |FLOPs
(G)| weights |
| ------ |:---: | :---: |:---: |:---: | :---: |
|YOLOX-Nano |416 |25.8 | 0.91 |1.08 | github |
|YOLOX-Tiny |416 |32.8 | 5.06 |6.45 | github |}

What's Changed

The base config of the torchyolo library has been improved. by @kadirnar in https://github.com/kadirnar/torchyolo/pull/1

Add the Yolov5 model. by @kadirnar in https://github.com/kadirnar/torchyolo/pull/2

Add show image by @kadirnar in https://github.com/kadirnar/torchyolo/pull/3

Added automodel module. by @kadirnar in https://github.com/kadirnar/torchyolo/pull/4

Added yolov7,yolov6 and yolox models. by @kadirnar in https://github.com/kadirnar/torchyolo/pull/11

The readme file has been updated. by @kadirnar in https://github.com/kadirnar/torchyolo/pull/12

Added pip support. by @kadirnar in https://github.com/kadirnar/torchyolo/pull/13

Added script for package update. by @kadirnar in https://github.com/kadirnar/torchyolo/pull/14

Updated the Yollov6 visualization module. by @kadirnar in https://github.com/kadirnar/torchyolo/pull/19

Updated the Yolox visualization module. by @kadirnar in https://github.com/kadirnar/torchyolo/pull/21

New Contributors

@kadirnar made their first contribution in https://github.com/kadirnar/torchyolo/pull/1

Full Changelog: https://github.com/kadirnar/torchyolo/commits/v0.0.1
Source code(tar.gz)
Source code(zip)

Torchyolo - Yolov3 ve Yolov4 modellerin Pytorch uygulamasıdır

Related tags

Overview

TORCHYOLO : Yolo Modellerin Pytorch Uygulaması

Yapılacaklar:

You might also like...

This project deals with the detection of skin lesions within the ISICs dataset using YOLOv3 Object Detection with Darknet.

🔥 TensorFlow Code for technical report: "YOLOv3: An Incremental Improvement"

Object Detection with YOLOv3

Multiple custom object count and detection using YOLOv3-Tiny method

这是一个mobilenet-yolov4-lite的库，把yolov4主干网络修改成了mobilenet，修改了Panet的卷积组成，使参数量大幅度缩小。

YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4

I tried to apply the CAM algorithm to YOLOv4 and it worked.

People movement type classifier with YOLOv4 detection and SORT tracking.

Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.

Comments

Uninstalling the visualization module of Yolov6

Releases(v0.0.1)

v0.0.1(Jan 7, 2023)

Yolov7

Yolov6

Yolov5

YOLOX

What's Changed

New Contributors

Owner

Kadir Nar

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Official repository for "Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems"

SegNet model implemented using keras framework

Bayesian algorithm execution (BAX)

Implementation for the IJCAI2021 work "Beyond the Spectrum: Detecting Deepfakes via Re-synthesis"

Experiment about Deep Person Re-identification with EfficientNet-v2

Serving PyTorch 1.0 Models as a Web Server in C++

The implementation of the lifelong infinite mixture model

Source Code of NeurIPS21 paper: Recognizing Vector Graphics without Rasterization

a reimplementation of LiteFlowNet in PyTorch that matches the official Caffe version

Official PyTorch implementation of "BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation" (NeurIPS 2021)

Learning Synthetic Environments and Reward Networks for Reinforcement Learning

Experiments with Fourier layers on simulation data.

PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)

Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

🔅 Shapash makes Machine Learning models transparent and understandable by everyone

[WACV 2022] Contextual Gradient Scaling for Few-Shot Learning

Video Instance Segmentation using Inter-Frame Communication Transformers (NeurIPS 2021)

Video-based open-world segmentation

PyTorch and Tensorflow functional model definitions