LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image.

Last update: Dec 13, 2022

Related tags

Deep Learning LF-YOLO

Overview

This project is based on ultralytics/yolov3.

LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image.

Download

$ git clone https://github.com/lmomoy/LF-YOLO

Train

We provide multiple versions of LF-YOLO with different depths.

$ python train.py --data coco.yaml --cfg LF-YOLO.yaml      --weights '' --batch-size 1
                                         LF-YOLO-1.25.yaml                           1
                                         LF-YOLO-0.75.yaml                           1
                                         LF-YOLO-0.5.yaml                            1

Results

We test our model on public dataset MS COCO, and it also achieves competitive results.

Model	size ^(pixels)	mAP^val 0.5:0.95	mAP^{test 0.5:0.95}	mAP^val 0.5	params ^(M)	FLOPS ^{640 (B)}
YOLOv3-tiny	640	17.6	17.6	34.8	8.8	13.2
YOLOv3	640	43.3	43.3	63.0	61.9	156.3
YOLOv3-SPP	640	44.3	44.3	64.6	63.0	157.1
LF-YOLO	640	27.8	27.9	47.8	7.4	17.1

Table Notes (click to expand)

AP^test denotes COCO test-dev2017 server results, all other AP results denote val2017 accuracy.
AP values are for single-model single-scale unless otherwise noted. Reproduce mAP by python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65
Speed_GPU averaged over 5000 COCO val2017 images using a GCP n1-standard-16 V100 instance, and includes FP16 inference, postprocessing and NMS. Reproduce speed by python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45
All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).

Requirements

Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:

$ pip install -r requirements.txt

Inference

$ python detect.py --source data/images --weights LF-YOLO.pt --conf 0.25

LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image.

Related tags

Overview

Download

Train

Results

Requirements

Inference

Owner

Experiments with differentiable stacks and queues in PyTorch

Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes, ICCV 2017

WarpRNNT loss ported in Numba CPU/CUDA for Pytorch

Boosting Adversarial Attacks with Enhanced Momentum (BMVC 2021)

KUIELAB-MDX-Net got the 2nd place on the Leaderboard A and the 3rd place on the Leaderboard B in the MDX-Challenge ISMIR 2021

End-to-end machine learning project for rices detection

PECOS - Prediction for Enormous and Correlated Spaces

The codebase for our paper "Generative Occupancy Fields for 3D Surface-Aware Image Synthesis" (NeurIPS 2021)

🔥3D-RecGAN in Tensorflow (ICCV Workshops 2017)

git《Joint Entity and Relation Extraction with Set Prediction Networks》(2020) GitHub:

Implementation for the paper: Invertible Denoising Network: A Light Solution for Real Noise Removal (CVPR2021).

This is the implementation of our work Deep Extreme Cut (DEXTR), for object segmentation from extreme points.

FeTaQA: Free-form Table Question Answering

A pytorch implementation of MBNET: MOS PREDICTION FOR SYNTHESIZED SPEECH WITH MEAN-BIAS NETWORK

BasicNeuralNetwork - This project looks over the basic structure of a neural network and how machine learning training algorithms work

The official homepage of the (outdated) COCO-Stuff 10K dataset.

Siamese-nn-semantic-text-similarity - A repository containing comprehensive Neural Networks based PyTorch implementations for the semantic text similarity task

Learning Modified Indicator Functions for Surface Reconstruction

Low-code/No-code approach for deep learning inference on devices

Hierarchical Attentive Recurrent Tracking