Code for Transformers Solve Limited Receptive Field for Monocular Depth Prediction

Related tags

Deep LearningTransDepth
Overview

Official PyTorch code for Transformers Solve Limited Receptive Field for Monocular Depth Prediction.
Guanglei Yang, Hao Tang, Mingli Ding, Nicu Sebe, Elisa Ricci.
Apply Transformer into depth predciton and surface normal estimation.

Prepare pretrain model

we choose R50-ViT-B_16 as our encoder.

wget https://storage.googleapis.com/vit_models/imagenet21k/R50+ViT-B_16.npz 
mkdir ./model/vit_checkpoint/imagenet21k 
mv R50+ViT-B_16.npz ./model/vit_checkpoint/imagenet21k/R50+ViT-B_16.npz

Prepare Dateset

prepare nyu

mkdir -p pytorch/dataset/nyu_depth_v2
python utils/download_from_gdrive.py 1AysroWpfISmm-yRFGBgFTrLy6FjQwvwP pytorch/dataset/nyu_depth_v2/sync.zip
cd pytorch/dataset/nyu_depth_v2
unzip sync.zip

prepare kitti

cd dataset
mkdir kitti_dataset
cd kitti_dataset
### image move kitti_archives_to_download.txt into kitti_dataset
wget -i kitti_archives_to_download.txt

### label
wget https://s3.eu-central-1.amazonaws.com/avg-kitti/data_depth_annotated.zip
unzip data_depth_annotated.zip
cd train
mv * ../
cd ..  
rm -r train
cd val
mv * ../
cd ..
rm -r val
rm data_depth_annotated.zip

Environment

pip install -r requirement.txt

Run

Train

CUDA_VISIBLE_DEVICES=0,1,2,3 python bts_main.py arguments_train_nyu.txt
CUDA_VISIBLE_DEVICES=0,1,2,3 python bts_main.py arguments_train_eigen.txt

Test: Pick up nice result

CUDA_VISIBLE_DEVICES=1 python bts_test.py arguments_test_nyu.txt
python ../utils/eval_with_pngs.py --pred_path vis_att_bts_nyu_v2_pytorch_att/raw/ --gt_path ../../dataset/nyu_depth_v2/official_splits/test/ --dataset nyu --min_depth_eval 1e-3 --max_depth_eval 10 --eigen_crop
CUDA_VISIBLE_DEVICES=1 python bts_test.py arguments_test_eigen.txt
python ../utils/eval_with_pngs.py --pred_path vis_att_bts_eigen_v2_pytorch_att/raw/ --gt_path ./dataset/kitti_dataset/ --dataset kitti --min_depth_eval 1e-3 --max_depth_eval 80 --do_kb_crop --garg_crop

Debug

CUDA_VISIBLE_DEVICES=1 python bts_main.py arguments_train_nyu_debug.txt

Download Pretrained Model

sh scripts/download_TransDepth_model.sh kitti_depth

sh scripts/download_TransDepth_model.sh nyu_depth

sh scripts/download_TransDepth_model.sh nyu_surfacenormal

Reference

BTS

ViT

Do‘s code

Visualization result share

We provide all vis result of all tasks. link

Owner
stanley
stanley
GitHub Repository
Restricted Boltzmann Machines in Python.

How to Use First, initialize an RBM with the desired number of visible and hidden units. rbm = RBM(num_visible = 6, num_hidden = 2) Next, train the m

Edwin Chen 928 Dec 30, 2022
Fast Axiomatic Attribution for Neural Networks (NeurIPS*2021)

Fast Axiomatic Attribution for Neural Networks This is the official repository accompanying the NeurIPS 2021 paper: R. Hesse, S. Schaub-Meyer, and S.

Visual Inference Lab @TU Darmstadt 11 Nov 21, 2022
[ICME 2021 Oral] CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning

CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning This repository is the official PyTorch implementation of CORE-Text, a

Jingyang Lin 18 Aug 11, 2022
Code release for Convolutional Two-Stream Network Fusion for Video Action Recognition

Convolutional Two-Stream Network Fusion for Video Action Recognition

Christoph Feichtenhofer 676 Dec 31, 2022
A curated list of references for MLOps

A curated list of references for MLOps

Larysa Visengeriyeva 9.3k Jan 07, 2023
A Simple LSTM-Based Solution for "Heartbeat Signal Classification and Prediction" in Tianchi

LSTM-Time-Series-Prediction A Simple LSTM-Based Solution for "Heartbeat Signal Classification and Prediction" in Tianchi Contest. The Link of the Cont

KevinCHEN 1 Jun 13, 2022
FG-transformer-TTS Fine-grained style control in transformer-based text-to-speech synthesis

LST-TTS Official implementation for the paper Fine-grained style control in transformer-based text-to-speech synthesis. Submitted to ICASSP 2022. Audi

Li-Wei Chen 64 Dec 30, 2022
Code for DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning

DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning Pytorch Implementation for DisCo: Remedy Self-supervi

79 Jan 06, 2023
Generative Exploration and Exploitation - This is an improved version of GENE.

GENE This is an improved version of GENE. In the original version, the states are generated from the decoder of VAE. We have to check whether the gere

33 Mar 23, 2022
Graph Regularized Residual Subspace Clustering Network for hyperspectral image clustering

Graph Regularized Residual Subspace Clustering Network for hyperspectral image clustering

Yaoming Cai 5 Jul 18, 2022
An experiment on the performance of homemade Q-learning AIs in Agar.io depending on their state representation and available actions

Agar.io_Q-Learning_AI An experiment on the performance of homemade Q-learning AIs in Agar.io depending on their state representation and available act

1 Jun 09, 2022
Hyperbolic Procrustes Analysis Using Riemannian Geometry

Hyperbolic Procrustes Analysis Using Riemannian Geometry The code in this repository creates the figures presented in this article: Please notice that

Ronen Talmon's Lab 2 Jan 08, 2023
Frigate - NVR With Realtime Object Detection for IP Cameras

A complete and local NVR designed for HomeAssistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.

Blake Blackshear 6.4k Dec 31, 2022
ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation

ST++ This is the official PyTorch implementation of our paper: ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation. Lihe Ya

Lihe Yang 147 Jan 03, 2023
Can we do Customers Segmentation using PHP and Unsupervized Machine Learning ? Yes we can ! 🤡

Customers Segmentation using PHP and Rubix ML PHP Library Can we do Customers Segmentation using PHP and Unsupervized Machine Learning ? Yes we can !

Mickaël Andrieu 11 Oct 08, 2022
[arXiv22] Disentangled Representation Learning for Text-Video Retrieval

Disentangled Representation Learning for Text-Video Retrieval This is a PyTorch implementation of the paper Disentangled Representation Learning for T

Qiang Wang 49 Dec 18, 2022
Implementation of Hierarchical Transformer Memory (HTM) for Pytorch

Hierarchical Transformer Memory (HTM) - Pytorch Implementation of Hierarchical Transformer Memory (HTM) for Pytorch. This Deepmind paper proposes a si

Phil Wang 63 Dec 29, 2022
Framework for Spectral Clustering on the Sparse Coefficients of Learned Dictionaries

Dictionary Learning for Clustering on Hyperspectral Images Overview Framework for Spectral Clustering on the Sparse Coefficients of Learned Dictionari

Joshua Bruton 6 Oct 25, 2022
Code for the Image similarity challenge.

ISC 2021 This repository contains code for the Image Similarity Challenge 2021. Getting started The docs subdirectory has step-by-step instructions on

Facebook Research 173 Dec 12, 2022
Realtime Face Anti Spoofing with Face Detector based on Deep Learning using Tensorflow/Keras and OpenCV

Realtime Face Anti-Spoofing Detection 🤖 Realtime Face Anti Spoofing Detection with Face Detector to detect real and fake faces Please star this repo

Prem Kumar 86 Aug 03, 2022