Pytorch implementation of the unsupervised object discovery method LOST.

Related tags

Deep LearningLOST
Overview

LOST

Pytorch implementation of the unsupervised object discovery method LOST. More details can be found in the paper:

Localizing Objects with Self-Supervised Transformers and no Labels [arXiv]
by Oriane Siméoni, Gilles Puy, Huy V. Vo, Simon Roburin, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Renaud Marlet and Jean Ponce

LOST visualizations LOST visualizations


If you use the LOST code or framework in your research, please consider citing:

@article{LOST,
   title = {Localizing Objects with Self-Supervised Transformers and no Labels},
   author = {Oriane Sim\'eoni and Gilles Puy and Huy V. Vo and Simon Roburin and Spyros Gidaris and Andrei Bursuc and Patrick P\'erez and Renaud Marlet and Jean Ponce},
   journal = {arXiv preprint arXiv:2109.14279},
   month = {09},
   year = {2021}
}

Installation

Dependencies

This code was implemented with python 3.7, PyTorch 1.7.1 and CUDA 10.2. Please install PyTorch. In order to install the additionnal dependencies, please launch the following command:

pip install -r requirements.txt

Install DINO

This method is based on DINO paper. The framework can be installed using the following commands:

> __init__.py; cd ../; "> 
git clone https://github.com/facebookresearch/dino.git
cd dino; 
touch __init__.py
echo -e "import sys\nfrom os.path import dirname, join\nsys.path.insert(0, join(dirname(__file__), '.'))" >> __init__.py; cd ../;

The code was made using the commit ba9edd1 of DINO repo (please rebase if breakage).

Apply LOST to one image

Following are scripts to apply LOST to an image defined via the image_path parameter and visualize the predictions (pred), the maps of the Figure 2 in the paper (fms) and the visulization of the seed expansion (seed_expansion). Box predictions are also stored in the output directory given by parameter output_dir.

python main_lost.py --image_path examples/VOC07_000236.jpg --visualize pred
python main_lost.py --image_path examples/VOC07_000236.jpg --visualize fms
python main_lost.py --image_path examples/VOC07_000236.jpg --visualize seed_expansion

Launching on datasets

Following are the different steps to reproduce the results of LOST presented in the paper.

PASCAL-VOC

Please download the PASCAL VOC07 and PASCAL VOC12 datasets (link) and put the data in the folder datasets. There should be the two subfolders: datasets/VOC2007 and datasets/VOC2012. In order to apply lost and compute corloc results (VOC07 61.9, VOC12 64.0), please launch:

python main_lost.py --dataset VOC07 --set trainval
python main_lost.py --dataset VOC12 --set trainval

COCO

Please download the COCO dataset and put the data in datasets/COCO. Results are provided given the 2014 annotations following previous works. The following command line allows you to get results on the subset of 20k images of the COCO dataset (corloc 50.7), following previous litterature. To be noted that the 20k images are a subset of the train set.

python main_lost.py --dataset COCO20k --set train

Different models

We have tested the method on different setups of the VIT model, corloc results are presented in the following table (more can be found in the paper).

arch pre-training dataset
VOC07 VOC12 COCO20k
ViT-S/16 DINO 61.9 64.0 50.7
ViT-S/8 DINO 55.5 57.0 49.5
ViT-B/16 DINO 60.1 63.3 50.0
ResNet50 DINO 36.8 42.7 26.5
ResNet50 Imagenet 33.5 39.1 25.5


Previous results on the dataset VOC07 can be obtained by launching:

python main_lost.py --dataset VOC07 --set trainval #VIT-S/16
python main_lost.py --dataset VOC07 --set trainval --patch_size 8 #VIT-S/8
python main_lost.py --dataset VOC07 --set trainval --arch vit_base #VIT-B/16
python main_lost.py --dataset VOC07 --set trainval --arch resnet50 #Resnet50/DINO
python main_lost.py --dataset VOC07 --set trainval --arch resnet50_imagenet #Resnet50/imagenet
Comments
  • Is LOST designed to perform well with DINO features specifically?

    Is LOST designed to perform well with DINO features specifically?

    I've replaced LOST's backbone (basically the dino weights) with the ones in CLIP, and it did not work well. But when switching back to dino weights, both ViT and ResNet50 backbone could generate good feature maps. Why would this happen?

    question 
    opened by zengyuy 3
  • Error in evaluation with Detectron2

    Error in evaluation with Detectron2

    Hi @osimeoni,

    Thank you for making the code available!

    When evaluating Detectron2 on VOC12 with the obtained pseudolables. I obtain the following error: AttributeError: "int object has no attribute 'value'. It seems that the coco_style_file is not registered by 'register_coco_instances' (see image underneath). Any idea how this can be fixed? Thanks.

    image

    opened by MarcVisions 2
  • Class-aware detection

    Class-aware detection

    Do you plan on releasing code for class-aware detection (i.e., to produce the results in Table 3 of https://arxiv.org/pdf/2109.14279.pdf)? I don't believe I see any of the necessary code for assigning object categories to boxes, but please correct me if I'm wrong.

    opened by gholste 2
  • Multi-object discovery

    Multi-object discovery

    HI, I have a confusion about the interesting work. How to perform multi-target discovery in the figure 1 (middle) of your paper？ Any advice is greatly appreciated.

    question 
    opened by rgbd-zml 1
  • Lost not performing well using DINO with fine-tuning

    Lost not performing well using DINO with fine-tuning

    I’ve trained DINO’s model with my own Dataset, doing a finetuning on the ViT’s pre trained models of DINO. After a feel experiments I noticed that, every time that a epoch of the DINO’s finetune ran, the loss of the training reduce, however the IoU (the validation metric that we are using) of the bounding boxes generated by the LOST algorithm gets worse. Can anyone explain me why this is happening and how can I fix it?

    opened by ericyoshida 1
  • corLoc evaluation

    corLoc evaluation

    Hi @osimeoni . I am suspicious about the corLoc evaluation part in the code. The corLoc for each image is true whenever one of the ground truth objects is hit! https://github.com/valeoai/LOST/blob/2b678aca89c18aa79c56ec3f6d4a0b979a91608d/main_lost.py#L311 What about other objects? Is it right?

    opened by Mirsadeghi 1
Owner
Valeo.ai
The GitHub account of Valeo.ai
Valeo.ai
GitHub Repository
Pre-trained BERT Models for Ancient and Medieval Greek, and associated code for LaTeCH 2021 paper titled - "A Pilot Study for BERT Language Modelling and Morphological Analysis for Ancient and Medieval Greek"

Ancient Greek BERT The first and only available Ancient Greek sub-word BERT model! State-of-the-art post fine-tuning on Part-of-Speech Tagging and Mor

Pranaydeep Singh 22 Dec 08, 2022
These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations"

Few-shot-NLEs These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations". You can find the smal

Yordan Yordanov 0 Oct 21, 2022
Twins: Revisiting the Design of Spatial Attention in Vision Transformers

Twins: Revisiting the Design of Spatial Attention in Vision Transformers Very recently, a variety of vision transformer architectures for dense predic

482 Dec 18, 2022
adversarial_multi_armed_bandit_variable_plays

Adversarial Multi-Armed Bandit with Variable Plays This code is for paper: Adversarial Online Learning with Variable Plays in the Evasion-and-Pursuit

Yiyang Wang 1 Oct 28, 2021
The official PyTorch implementation for the paper "sMGC: A Complex-Valued Graph Convolutional Network via Magnetic Laplacian for Directed Graphs".

Magnetic Graph Convolutional Networks About The official PyTorch implementation for the paper sMGC: A Complex-Valued Graph Convolutional Network via M

3 Feb 25, 2022
This is the official implementation of 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection, built on SECOND.

3D-CVF This is the official implementation of 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object

YecheolKim 97 Dec 20, 2022
[ICLR 2022] Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators

AMOS This repository contains the scripts for fine-tuning AMOS pretrained models on GLUE and SQuAD 2.0 benchmarks. Paper: Pretraining Text Encoders wi

Microsoft 22 Sep 15, 2022
YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931)

Introduction Yolov5-face is a real-time,high accuracy face detection. Performance Single Scale Inference on VGA resolution（max side is equal to 640 an

DeepCam Shenzhen 1.4k Jan 07, 2023
FIRA: Fine-Grained Graph-Based Code Change Representation for Automated Commit Message Generation

FIRA is a learning-based commit message generation approach, which first represents code changes via fine-grained graphs and then learns to generate commit messages automatically.

Van 21 Dec 30, 2022
Direct application of DALLE-2 to video synthesis, using factored space-time Unet and Transformers

DALLE2 Video (wip) ** only to be built after DALLE2 image is done and replicated, and the importance of the prior network is validated ** Direct appli

Phil Wang 105 May 15, 2022
LoL Runes Recommender With Python

LoL-Runes-Recommender Para ejecutar la aplicación se debe llamar a execute_app.p

Sebastián Salinas 1 Jan 10, 2022
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search

BossNAS This repository contains PyTorch evaluation code, retraining code and pretrained models of our paper: BossNAS: Exploring Hybrid CNN-transforme

Changlin Li 127 Dec 26, 2022
Code repository for the work "Multi-Domain Incremental Learning for Semantic Segmentation", accepted at WACV 2022

Multi-Domain Incremental Learning for Semantic Segmentation This is the Pytorch implementation of our work "Multi-Domain Incremental Learning for Sema

Pgxo20 24 Jan 02, 2023
Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! Very tiny! Stock Market Financial Technical Analysis Python library . Quant Trading automation or cryptocoin exchange

MyTT Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! to Stock Market Financial Technical Analysis Python

dev 34 Dec 27, 2022
Intent parsing and slot filling in PyTorch with seq2seq + attention

PyTorch Seq2Seq Intent Parsing Reframing intent parsing as a human - machine translation task. Work in progress successor to torch-seq2seq-intent-pars

Sean Robertson 160 Jan 07, 2023
PyTorch implementation of Value Iteration Networks (VIN): Clean, Simple and Modular. Visualization in Visdom.

VIN: Value Iteration Networks This is an implementation of Value Iteration Networks (VIN) in PyTorch to reproduce the results.(TensorFlow version) Key

Xingdong Zuo 215 Dec 07, 2022
Official repository for Automated Learning Rate Scheduler for Large-Batch Training (8th ICML Workshop on AutoML)

Automated Learning Rate Scheduler for Large-Batch Training The official repository for Automated Learning Rate Scheduler for Large-Batch Training (8th

Kakao Brain 35 Jan 04, 2023
Repository providing a wide range of self-supervised pretrained models for computer vision tasks.

Hierarchical Pretraining: Research Repository This is a research repository for reproducing the results from the project "Self-supervised pretraining

Colorado Reed 53 Nov 09, 2022
Text-to-Image generation

Generate vivid Images for Any (Chinese) text CogView is a pretrained (4B-param) transformer for text-to-image generation in general domain. Read our p

THUDM 1.3k Dec 29, 2022
IRON Kaggle project done while doing IRONHACK Bootcamp where we had to analyze and use a Machine Learning Project to predict future sales

IRON Kaggle project done while doing IRONHACK Bootcamp where we had to analyze and use a Machine Learning Project to predict future sales. In this case, we ended up using XGBoost because it was the o

1 Jan 04, 2022