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

A Python script that creates subtitles of a given length from text paragraphs that can be easily imported into any Video Editing software such as FinalCut Pro for further adjustments.

Reading list for research topics in Masked Image Modeling

Hydra: an Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems

For the paper entitled ''A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion Mining''

PyTorch Implementation of Region Similarity Representation Learning (ReSim)

We simulate traveling back in time with a modern camera to rephotograph famous historical subjects.

Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them"

Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.

A simplistic and efficient pure-python neural network library from Phys Whiz with CPU and GPU support.

DUE: End-to-End Document Understanding Benchmark

Code for paper "Which Training Methods for GANs do actually Converge? (ICML 2018)"

Multi-task Multi-agent Soft Actor Critic for SMAC

Walk with fastai

Code for EmBERT, a transformer model for embodied, language-guided visual task completion.

The official implementation of VAENAR-TTS, a VAE based non-autoregressive TTS model.

A series of Jupyter notebooks with Chinese comment that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

New approach to benchmark VQA models

Code for paper "Learning to Reweight Examples for Robust Deep Learning"

[NeurIPS 2020] Official Implementation: "SMYRF: Efficient Attention using Asymmetric Clustering".

SIR model parameter estimation using a novel algorithm for differentiated uniformization.