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

Last update: Dec 13, 2022

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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

This repository contains the scripts for downloading and validating scripts for the documents

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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

This repository contains the scripts for downloading and validating scripts for the documents

Gif-caption - A straightforward GIF Captioner written in Python

PyTorch implementation for the visual prior component (i.e. perception module) of the Visually Grounded Physics Learner [Li et al., 2020].

S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (CVPR 2021)

[ICCV 2021 Oral] Just Ask: Learning to Answer Questions from Millions of Narrated Videos

Code for the Convolutional Vision Transformer (ConViT)

Pytorch implementation of Value Iteration Networks (NIPS 2016 best paper)

GestureSSD CBAM - A gesture recognition web system based on SSD and CBAM, using pytorch, flask and node.js

Example for AUAV 2022 with obstacle avoidance.

Multi-query Video Retreival

Official code of our work, AVATAR: A Parallel Corpus for Java-Python Program Translation.

Brax is a differentiable physics engine that simulates environments made up of rigid bodies, joints, and actuators

Official pytorch implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion"

SAGE: Sensitivity-guided Adaptive Learning Rate for Transformers

PyTorch GPU implementation of the ES-RNN model for time series forecasting

​ This is the Pytorch implementation of Progressive Attentional Manifold Alignment.

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

This repository contains the code for designing risk bounded motion plans for car-like robot using Carla Simulator.

PoolFormer: MetaFormer is Actually What You Need for Vision

🌳 A Python-inspired implementation of the Optimum-Path Forest classifier.

This is the Pytorch implementation of Progressive Attentional Manifold Alignment.