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

POT : Python Optimal Transport

Multiple-Object Tracking with Transformer

Benchmark library for high-dimensional HPO of black-box models based on Weighted Lasso regression

Benchmark for Answering Existential First Order Queries with Single Free Variable

Generative Models for Graph-Based Protein Design

TorchOk - The toolkit for fast Deep Learning experiments in Computer Vision

E2VID_ROS - E2VID_ROS: E2VID to a real-time system

Unofficial implementation of "TTNet: Real-time temporal and spatial video analysis of table tennis" (CVPR 2020)

StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation

RGB-stacking 🛑 🟩 🔷 for robotic manipulation

Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding

Code release for our paper, "SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo"

Graph Neural Networks with Keras and Tensorflow 2.

Reusable constraint types to use with typing.Annotated

img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation

On the Limits of Pseudo Ground Truth in Visual Camera Re-Localization

The repository offers the official implementation of our BMVC 2021 paper in PyTorch.

Generate text captions for images from their CLIP embeddings. Includes PyTorch model code and example training script.

Official implementation of deep Gaussian process (DGP)-based multi-speaker speech synthesis with PyTorch.

Provide baselines and evaluation metrics of the task: traffic flow prediction