yolox_backbone is a deep-learning library and is a collection of YOLOX Backbone models.

Overview

YOLOX-Backbone

yolox-backbone is a deep-learning library and is a collection of YOLOX backbone models.

Install

pip install yolox-backbone

Load a Pretrained Model

Pretrained models can be loaded using yolox_backbone.create_model.

import yolox_backbone

m = yolox_backbone.create_model('yolox-s', pretrained=True)
m.eval()

List Supported Models

import yolox_backbone
from pprint import pprint

model_names = yolox_backbone.list_models()
pprint(model_names)

>>> ['yolox-s',
 'yolox-m',
 'yolox-l',
 'yolox-x',
 'yolox-nano',
 'yolox-tiny',
 'yolox-darknet53']

Select specific feature levels

There is one creation argument impacting the output features.

  • out_features selects which FPN features to output

Example

import yolox_backbone
import torch
from pprint import pprint

pprint(yolox_backbone.list_models())

model_names = yolox_backbone.list_models()
for model_name in model_names:
    print("model_name: ", model_name)
    model = yolox_backbone.create_model(model_name=model_name, 
                                        pretrained=True, 
                                        out_features=["P3", "P4", "P5"]
                                        )

    input_tensor = torch.randn((1, 3, 640, 640))
    fpn_output_tensors = model(input_tensor)

    p3 = fpn_output_tensors["P3"]
    p4 = fpn_output_tensors["P4"]
    p5 = fpn_output_tensors["P5"]
    
    print("input_tensor.shape: ", input_tensor.shape)
    print("p3.shape: ", p3.shape)
    print("p4.shape: ", p4.shape)
    print("p5.shape: ", p5.shape)
    print("-" * 50)
    

Output:

['yolox-s', 'yolox-m', 'yolox-l', 'yolox-x', 'yolox-nano', 'yolox-tiny', 'yolox-darknet53']
model_name:  yolox-s
input_tensor.shape:  torch.Size([1, 3, 640, 640])
p3.shape:  torch.Size([1, 128, 80, 80])
p4.shape:  torch.Size([1, 256, 40, 40])
p5.shape:  torch.Size([1, 512, 20, 20])
--------------------------------------------------
model_name:  yolox-m
input_tensor.shape:  torch.Size([1, 3, 640, 640])
p3.shape:  torch.Size([1, 192, 80, 80])
p4.shape:  torch.Size([1, 384, 40, 40])
p5.shape:  torch.Size([1, 768, 20, 20])
--------------------------------------------------
model_name:  yolox-l
input_tensor.shape:  torch.Size([1, 3, 640, 640])
p3.shape:  torch.Size([1, 256, 80, 80])
p4.shape:  torch.Size([1, 512, 40, 40])
p5.shape:  torch.Size([1, 1024, 20, 20])
--------------------------------------------------
model_name:  yolox-x
input_tensor.shape:  torch.Size([1, 3, 640, 640])
p3.shape:  torch.Size([1, 320, 80, 80])
p4.shape:  torch.Size([1, 640, 40, 40])
p5.shape:  torch.Size([1, 1280, 20, 20])
--------------------------------------------------
model_name:  yolox-nano
input_tensor.shape:  torch.Size([1, 3, 640, 640])
p3.shape:  torch.Size([1, 64, 80, 80])
p4.shape:  torch.Size([1, 128, 40, 40])
p5.shape:  torch.Size([1, 256, 20, 20])
--------------------------------------------------
model_name:  yolox-tiny
input_tensor.shape:  torch.Size([1, 3, 640, 640])
p3.shape:  torch.Size([1, 96, 80, 80])
p4.shape:  torch.Size([1, 192, 40, 40])
p5.shape:  torch.Size([1, 384, 20, 20])
--------------------------------------------------
model_name:  yolox-darknet53
input_tensor.shape:  torch.Size([1, 3, 640, 640])
p3.shape:  torch.Size([1, 128, 80, 80])
p4.shape:  torch.Size([1, 256, 40, 40])
p5.shape:  torch.Size([1, 512, 20, 20])
--------------------------------------------------
Owner
Yonghye Kwon
practical
Yonghye Kwon
Non-Imaging Transient Reconstruction And TEmporal Search (NITRATES)

Non-Imaging Transient Reconstruction And TEmporal Search (NITRATES) This repo contains the full NITRATES pipeline for maximum likelihood-driven discov

13 Nov 08, 2022
LSTM model trained on a small dataset of 3000 names written in PyTorch

LSTM model trained on a small dataset of 3000 names. Model generates names from model by selecting one out of top 3 letters suggested by model at a time until an EOS (End Of Sentence) character is no

Sahil Lamba 1 Dec 20, 2021
LQM - Improving Object Detection by Estimating Bounding Box Quality Accurately

Improving Object Detection by Estimating Bounding Box Quality Accurately Abstract Object detection aims to locate and classify object instances in ima

IM Lab., POSTECH 0 Sep 28, 2022
Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection

SAGA Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection Please refer to the Jupyter notebook (Example.ipynb) for an example of using t

9 Dec 28, 2022
Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE

TensorFlow Tutorial - used by Nvidia Learn TensorFlow from scratch by examples and visualizations with interactive jupyter notebooks. Learn to compete

Alexander R Johansen 1.9k Dec 19, 2022
An Evaluation of Generative Adversarial Networks for Collaborative Filtering.

An Evaluation of Generative Adversarial Networks for Collaborative Filtering. This repository was developed by Fernando B. Pérez Maurera. Fernando is

Fernando Benjamín PÉREZ MAURERA 0 Jan 19, 2022
some classic model used to segment the medical images like CT、X-ray and so on

github_project This is a project for medical image segmentation. This project includes common medical image segmentation models such as U-net, FCN, De

2 Mar 30, 2022
🧑‍🔬 verify your TEAL program by experiment and observation

Graviton - Testing TEAL with Dry Runs Tutorial Local Installation The following instructions assume that you have make available in your local environ

Algorand 18 Jan 03, 2023
PyTorch implementation of the NIPS-17 paper "Poincaré Embeddings for Learning Hierarchical Representations"

Poincaré Embeddings for Learning Hierarchical Representations PyTorch implementation of Poincaré Embeddings for Learning Hierarchical Representations

Facebook Research 1.6k Dec 25, 2022
Implementation of Segformer, Attention + MLP neural network for segmentation, in Pytorch

Segformer - Pytorch Implementation of Segformer, Attention + MLP neural network for segmentation, in Pytorch. Install $ pip install segformer-pytorch

Phil Wang 208 Dec 25, 2022
[CVPR 2021] Few-shot 3D Point Cloud Semantic Segmentation

Few-shot 3D Point Cloud Semantic Segmentation Created by Na Zhao from National University of Singapore Introduction This repository contains the PyTor

117 Dec 27, 2022
Implementation of ToeplitzLDA for spatiotemporal stationary time series data.

Code for the ToeplitzLDA classifier proposed in here. The classifier conforms sklearn and can be used as a drop-in replacement for other LDA classifiers. For in-depth usage refer to the learning from

Jan Sosulski 5 Nov 07, 2022
Learning to See by Looking at Noise

Learning to See by Looking at Noise This is the official implementation of Learning to See by Looking at Noise. In this work, we investigate a suite o

Manel Baradad Jurjo 82 Dec 24, 2022
Diffusion Probabilistic Models for 3D Point Cloud Generation (CVPR 2021)

Diffusion Probabilistic Models for 3D Point Cloud Generation [Paper] [Code] The official code repository for our CVPR 2021 paper "Diffusion Probabilis

Shitong Luo 323 Jan 05, 2023
Chainer Implementation of Semantic Segmentation using Adversarial Networks

Semantic Segmentation using Adversarial Networks Requirements Chainer (1.23.0) Differences Use of FCN-VGG16 instead of Dilated8 as Segmentor. Caution

Taiki Oyama 99 Jun 28, 2022
Python based Advanced AI Assistant

Knick is a virtual artificial intelligence project, fully developed in python. The objective of this project is to develop a virtual assistant that can handle our minor, intermediate as well as heavy

19 Nov 15, 2022
YolactEdge: Real-time Instance Segmentation on the Edge

YolactEdge, the first competitive instance segmentation approach that runs on small edge devices at real-time speeds. Specifically, YolactEdge runs at up to 30.8 FPS on a Jetson AGX Xavier (and 172.7

Haotian Liu 1.1k Jan 06, 2023
Breast Cancer Classification Model is applied on a different dataset

Breast Cancer Classification Model is applied on a different dataset

1 Feb 04, 2022
PyTorch implementation of MICCAI 2018 paper "Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector"

Grouped SSD (GSSD) for liver lesion detection from multi-phase CT Note: the MICCAI 2018 paper only covers the multi-phase lesion detection part of thi

Sang-gil Lee 36 Oct 12, 2022
A repository for the updated version of CoinRun used to collect MUGEN, a multimodal video-audio-text dataset.

A repository for the updated version of CoinRun used to collect MUGEN, a multimodal video-audio-text dataset. This repo contains scripts to train RL agents to navigate the closed world and collect vi

MUGEN 11 Oct 22, 2022