[ICCV'21] Pri3D: Can 3D Priors Help 2D Representation Learning?

Related tags

Deep Learningpythoncomputer-visiondeep-learning3diccv2021
Overview

Pri3D: Can 3D Priors Help 2D Representation Learning? [ICCV 2021]

Pri3D

Pri3D leverages 3D priors for downstream 2D image understanding tasks: during pre-training, we incorporate view-invariant and geometric priors from color-geometry information given by RGB-D datasets, imbuing geometric priors into learned features. We show that these 3D-imbued learned features can effectively transfer to improved performance on 2D tasks such as semantic segmentation, object detection, and instance segmentation.

[ICCV 2021 Paper] [Video]

Environment

This codebase was tested with the following environment configurations.

Installation

We use conda for the installation process:

# Install virtual env and PyTorch
conda create -n sparseconv051 python=3.8
conda activate sparseconv051
conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=11.0 -c pytorch

# Complie and install MinkowskiEngine 0.5.1.
conda install mkl mkl-include -c intel
wget https://github.com/NVIDIA/MinkowskiEngine/archive/refs/tags/v0.5.1.zip
cd MinkowskiEngine-0.5.1 
python setup.py install

Next, clone the Pri3D repository and install the requirement from the root directory.

git clone https://github.com/Sekunde/Pri3D.git
cd Pri3D
pip install -r requirements.txt

Training Mask R-CNN models requires Detectron2.

[Logging] Pri3D will create runs for logging training curves and configurations in the project named pri3d in Weights & Biases. Additionally, checkpoints and a txt log file will be stored in the specified output folder. You will be asked to input username and password of Weights & Biases in the first time to run the training code.

[Optional] If you want to pre-train the view-consistent contrastive loss on MegaDepth data, you need to install COLMAP, see their installation ducomentation.

Pre-training Section

Data Pre-processing

Prepare ScanNet Pre-training Data

For pre-training view-invariant contrastive loss, pairs of ScanNet frames data can be generated by the following code (need to change the TARGET and SCANNET_DIR accordingly in the script). This piece of code first extracts pointcloud from partial frames, and then computes a filelist of overlapped partial frames for each scene.

cd pretrain/data_preprocess/scannet
./preprocess.sh

Then a combined txt file called overlap30.txt of filelists of each scene can be generated by running the following code. This overlap30.txt should be put into folder TARGET/splits.

cd pretrain/data_preprocess/scannet
python generate_list.py --target_dir TARGET

For pre-training geometric-prior, we first generate point cloud of ScanNet reconstruction. This can be done by running following code. We use SCANNET_DATA to refer where scannet data lives and SCANNET_OUT_PATH to denote the output path of processed scannet data.

# Edit path variables: SCANNET_DATA and SCANNET_OUT_PATH
cd pretrain/data_preprocess/scannet
python collect_indoor3d_data.py --input SCANNET_DATA --output SCANNET_OUT_PATH
# copy the filelists
cp -r split SCANNET_OUT_PATH

Afterwards, we further generate chunk bbox that is used for cropping chunks by running following code. TARGET points to where the previously generated pairs of ScanNet frames are located.

cd pretrain/data_preprocess/scannet/
python chunk.py --base TARGET

Prepare MegaDepth Pre-training Data

We borrow the MegaDepth data generation code from D2-Net. After installing COLMAP and downloading the MegaDepth Dataset(including SfM models), you can run the following code to pre-process data.

cd pretrain/data_preprocess/megadepth/
python undistort_reconstructions.py --colmap_path /path/to/colmap/executable --base_path /path/to/megadepth
bash preprocess_undistorted_megadepth.sh /path/to/megadepth /path/to/output/folder

We also provide visualization of the MegaDepth data.

Prepare KITTI Pre-training Data

Download KITTI Dataset and run the following code to pre-process the data. This will create an overlap.txt file indexing the pairs of frames as well as a mapping folder storing the coordinates mapping between frames located in /path/to/kitti/dataset.

cd pretrain/data_preprocess/kitti/
python kitti_process.py --input /path/to/kitti/dataset

Pre-training on Different Datasets

To pre-train on different datasets, we provide the scripts to train Pri3D with 8 GPUs (batch_size=64, 8 per GPU) on a single server under folder pretrain/pri3d/scripts. Pri3D can also be pre-trained on the server with fewer GPUs, e.g. 4 GPUs by setting train.batsh_size=32 (8 per GPUs) and optimizer.accumulate_step=2 (effective batch_size=32x2=64) to accumulate gradients. The code is competitable with facebook hydra. Our codebase enables multi-gpu training with distributed data parallel (DDP) module in pytorch.

Pre-train on ScanNet

TARGET and SCANNET_OUT_PATH refer to the pre-processed data locations that are defined in Prepare ScanNet Pre-train Data.

cd pretrain/pri3d
export DATAPATH=TARGET 
# if pre-train with geometric-contrastive loss
export POINTCLOUD_PATH=SCANNET_OUT_PATH
# Pretrain with view-invariant loss (ResNet50 backbone)
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet VIEW=True scripts/scannet.sh
# Pretrain with geometric-contrastive loss (ResNet50 backbone)
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet GEO=True scripts/scannet.sh
# Pretrain with view-invariant and geometric-contrastive loss (ResNet50 backbone)
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet VIEW=True GEO=True scripts/scannet.sh
# Pretrain with view-invariant loss (ResNet18 backbone)
LOG_DIR=/path/to/log/folder BACKBONE=Res18UNet VIEW=True scripts/scannet.sh

Pre-train on MegaDepth

cd pretrain/pri3d
export DATAPATH=/path/to/megadepth/processed/data/folder
# Pretrain with view-invariant loss (ResNet50 backbone)
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet scripts/megadepth.sh
# Pretrain with view-invariant loss (ResNet18 backbone)
LOG_DIR=/path/to/log/folder BACKBONE=Res18UNet scripts/megadepth.sh

Pre-train on KITTI

cd pretrain/pri3d
export DATAPATH=KITTI_PATH
# Pretrain with view-invariant loss (ResNet50 backbone)
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet scripts/kitti.sh
# Pretrain with view-invariant loss (ResNet18 backbone)
LOG_DIR=/path/to/log/folder BACKBONE=Res18UNet scripts/kitti.sh

Downstream Task Section

Semantic Segmentation on ScanNet

Download scannet_frames_25k and unzip to SCANNET_SEMSEG. It should have following structures.

SCANNET_SEMSEG/
    scene0000_00/
        color/
	depth/
	instance/
	label/
	pose/
	intrinsics_color.txt
	intrinsics_depth.txt
    scene0000_01/
    ...

Export path SCANNET_SEMSEG to enviromental variable $DATAPATH and run the code to train the ResUNet models.

cd downstream/semseg/unet
export DATAPATH=SCANNET_SEMSEG
export PHASE=train
# train the model with ResNet50 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=imagenet scripts/scannet.sh
# train the model with ResNet50 backbone, train from scratch 
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=scratch scripts/scannet.sh
# train the model with ResNet50 backbone, train from scratch with 20% data.
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=scratch PHASE=train20 scripts/scannet.sh
# train the model with ResNet50 backbone, initialized with specified pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=/path/to/saved/model scripts/scannet.sh
# train the model with ResNet18 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res18UNet INIT=imagenet scripts/scannet.sh

Similarly, export environmental variable and run the code to train PSPNet and DeepLabV3/V3+ models.

# train PSPNet (ResNet50 as backbones)
cd downstream/semseg/pspnet
export DATAPATH=SCANNET_SEMSEG
# train PSPNet with ResNet50 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder INIT=imagenet scripts/scannet.sh
# train PSPNet with ResNet50 backbone, initialized with specified pre-trained model
LOG_DIR=/path/to/log/folder INIT=/path/to/checkpoint scripts/scannet.sh

# train DeepLabV3 and DeepLabV3+ models (ResNet50 as backbones)
cd downstream/semseg/deeblabv3
export DATAPATH=SCANNET_SEMSEG
# train DeepLabV3 with ResNet50 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder INIT=imagenet scripts/train_scannet_deeplapv3.sh
# train DeepLabV3+ with ResNet50 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder INIT=imagenet scripts/train_scannet_deeplapv3plus.sh
# train DeepLabV3+ with ResNet50 backbone, initialized with specified pre-trained model
LOG_DIR=/path/to/log/folder INIT=/path/to/checkpoint scripts/train_scannet_deeplabv3plus.sh

Model Zoo

PSPNet and DeepLabV3/V3+ use checkpoints in torchvision format, thus we provide the code for converting from our Pri3D pre-trained checkpoint to torchvision checkpoint.

cd downstream/conversion
python pri3d_to_torchvision.py /path/to/pre-trained/pri3d/checkpoint /path/to/output/checkpoint/in/torchvison/format

The provided pre-trained models for PSPNet and DeepLabV3/V3+ are already converted to torchvision format.

Training Data mIoU (val) Backbone Pre-trained Model (on ScanNet) Curves Logs
100% scenes 61.7 ResNet50 Pri3D (View + Geo) link link
100% scenes 55.7 ResNet18 Pri3D (View + Geo) link link
100% scenes 62.8 PSPNet Pri3D (View + Geo) link link
100% scenes 61.3 DeepLabV3 Pri3D (View + Geo) link link
100% scenes 61.6 DeepLabV3+ Pri3D (View + Geo) link link
80% scenes 60.3 ResNet50 Pri3D (View + Geo) link link
60% scenes 58.9 ResNet50 Pri3D (View + Geo) link link
40% scenes 56.2 ResNet50 Pri3D (View + Geo) link link
20% scenes 51.5 ResNet50 Pri3D (View + Geo) link link

Semantic Segmentation on KITTI

Download and unzip label for semantic and instance segmentation. unzip and organize the data folder as following structures.

KITTI_SESEG/
    image_2/
    instance/
    semantic/
    semantic_rgb/

Use following code snippets to train semantic segmentation models on KITTI data.

cd downstream/semseg
export DATAPATH=KITTI_SEMSEG
# train the model with ResNet50 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=imagenet scripts/kitti.sh
# train the model with ResNet50 backbone, train from scratch 
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=scratch scripts/kitti.sh
# train the model with ResNet50 backbone, initialized with specified pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=/path/to/saved/model scripts/kitti.sh
# train the model with ResNet18 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res18UNet INIT=imagenet scripts/kitti.sh

Model Zoo

Training Data mIoU (val) Backbone Pre-trained Model Curves Logs
100% scenes 33.2 ResNet50 Pri3D (View) on KITTI link link

Semantic Segmentation on NYUv2

Download NYUv2 Dataset and unzip it to path NYUv2_SEMSEG.

cd downstream/semseg
export DATAPATH=NYUv2_SEMSEG
# train the model with ResNet50 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=imagenet scripts/nyuv2.sh
# train the model with ResNet50 backbone, train from scratch 
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=scratch scripts/nyuv2.sh
# train the model with ResNet50 backbone, initialized with specified pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=/path/to/saved/model scripts/nyuv2.sh
# train the model with ResNet18 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res18UNet INIT=imagenet scripts/nyuv2.sh

Model Zoo

Training Data mIoU (val) Backbone Pre-trained Model (on ScanNet) Curves Logs
100% scenes 54.7 ResNet50 Pri3D (View + Geo) link link
100% scenes 47.6 ResNet50 MoCoV2-supIN->SN link link

Semantic Segmentation on Cityscapes

Download gtFine_trainvaltest and leftImg8bit_trainvaltest. Unzip and organize as following data structures.

CityScapes_SEMSEG/
    gtFine/
    leftImg8bit/

Export the data path (CityScapes_SEMSEG) to $DATAPATH environmental variable and train the models.

cd downstream/semseg
export DATAPATH=Cityscapes_SEMSEG
# train the model with ResNet50 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=imagenet scripts/cityscapes.sh
# train the model with ResNet50 backbone, train from scratch 
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=scratch scripts/cityscapes.sh
# train the model with ResNet50 backbone, initialized with specified pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=/path/to/saved/model scripts/cityscapes.sh
# train the model with ResNet18 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res18UNet INIT=imagenet scripts/cityscapes.sh

Model Zoo

Training Data mIoU (val) Backbone Pre-trained Model Curves Logs
100% scenes 56.3 ResNet50 Pri3D (View) on KITTI link link
100% scenes 55.2 ResNet50 Pri3D (View) on MegaDepth link link

Instance Segmentation/Detection on ScanNet

For training an instance segmentation/detection model, a COCO format json file of annotation needs to be generated. We provide code to convert the ScanNet Annotation into COCO format (json file). Path SCANNET_SEMSEG refers to the location of ScanNet semantic segmentation data.

cd downstream/insseg/dataset
# generate json file of annotations for training
python scanet2coco.py --scannet_path SCANNET_SEMSEG --phase train
# generate json file of annotations for validation
python scanet2coco.py --scannet_path SCANNET_SEMSEG --phase val
# generate json file of annotations for training on 20%,40%,60%,80% data.
python scanet2coco.py --scannet_path SCANNET_SEMSEG --phase train20
python scanet2coco.py --scannet_path SCANNET_SEMSEG --phase train40
python scanet2coco.py --scannet_path SCANNET_SEMSEG --phase train60
python scanet2coco.py --scannet_path SCANNET_SEMSEG --phase train80

The code above generates json files, such as scannet_train.coco.json and scannet_val.coco.json. After having json files, the following code will train Mask R-CNN models.

cd downstream/insseg
export JSON_PATH=/path/to/json/file
export IMAGE_PATH=SCANNET_SEMSEG
# train the model with ImageNet pre-trained model 
LOG_DIR=/path/to/log/folder INIT=imagenet sbatch script/train_scannet.sh
# train the model with pre-trained model (remove 'sbatch' if training on a local machine)
LOG_DIR=/path/to/log/folder INIT=/path/to/model sbatch script/train_scannet.sh
# train the model on ScanNet 20% data
JSON_PATH=/path/to/scannet_train20.coco.json LOG_DIR=/path/to/log/folder INIT=imagenet script/train_scannet.sh

Model Zoo

Detectron2 requires a specific checkpoint format, thus we provide the code for converting from our Pri3D pre-trained checkpoint to the required checkpoint format.

cd downstream/conversion
python pri3d_to_torchvision.py /path/to/pri3d/format/checkpoint /path/to//torchvison/format/checkpoint
ptthon torchvision_to_detectron.py /path/to//torchvison/format/checkpoint /path/to/detectron/format/checkpoint

The provided pre-trained models in the following are already converted to detectron2 checkpoints.

Data [email protected] (bbox) [email protected] (segm) Backbone Pre-trained Model (on ScanNet) Curves Logs
100% 44.5 35.8 ResNet50 Pri3D (View + Geo) link link
100% 43.5 33.9 ResNet50 MoCoV2-supIN->SN link link

Instance Segmentation/Detection on NYUv2

Similarly to ScanNet, we provide code to convert the NYUv2 Annotation into COCO format (json files). Path NYUv2_SEMSEG refers to the location of NYUv2 semantic segmentation data.

cd downstream/insseg/dataset
# generate json file of annotations for training
python nyu2coco.py --nyu_path NYUv2_SEMSEG --phase train
# generate json file of annotations for validation
python nyu2coco.py --scannet_path NYUv2_SEMSEG --phase val

The code above generates json files, such as nyu_train.coco.json and nyu_val.coco.json. After having json files, the following code will train Mask R-CNN models.

cd downstream/insseg
export JSON_PATH=/path/to/json/file
export IMAGE_PATH=NYUv2_SEMSEG
# train the model with ImageNet pre-trained model 
LOG_DIR=/path/to/log/folder INIT=imagenet sbatch script/train_nyu.sh
# train the model with pre-trained model (remove 'sbatch' if training on a local machine)
LOG_DIR=/path/to/log/folder INIT=/path/to/model sbatch script/train_nyu.sh

Model Zoo

The provided pre-trained models in the following are already converted to Detectron2 checkpoints (convert to detectrion2 shows how to convert from Pri3D checkpoint to Detectron2 format).

Data [email protected] (bbox) [email protected] (segm) Backbone Pre-trained Model (on ScanNet) Curves Logs
100% 34.0 29.5 ResNet50 Pri3D (View + Geo) link link
100% 31.1 27.2 ResNet50 MoCoV2-supIN->SN link link

Instance Segmentation/Detection on COCO

Download 2017 Train Images, 2017 Val Images and 2017 Train/Val Annotations. Unzip and organize them as following structures.

$DETECTRON2_DATASETS/
    coco/
        annotations/
	    instances_{train,val}2017.json
        {train,val}2017/

Then using the following code to train instance segmentation/detection models.

cd downstream/insseg
export DETECTRON2_DATASETS=/path/to/datasets
# train the model with ImageNet pre-trained model 
LOG_DIR=/path/to/log/folder INIT=imagenet sbatch script/train_coco.sh
# train the model with pre-trained model (remove 'sbatch' if training on a local machine)
LOG_DIR=/path/to/log/folder INIT=/path/to/model sbatch script/train_coco.sh

Model Zoo

The provided pre-trained models in the following are already converted to Detectron2 checkpoints (convert to detectron2 shows how to convert from Pri3D checkpoint to Detectron2 format).

Data [email protected] (bbox) [email protected] (segm) Backbone Pre-trained Model (on ScanNet) Curves Logs
100% 60.6 57.5 ResNet50 Pri3D (View) link link

Citing our paper

@article{hou2021pri3d,
  title={Pri3D: Can 3D Priors Help 2D Representation Learning?},
  author={Hou, Ji and Xie, Saining and Graham, Benjamin and Dai, Angela and Nie{\ss}ner, Matthias},
  journal={arXiv preprint arXiv:2104.11225},
  year={2021}
}

License

Pri3D is relased under the MIT License. See the LICENSE file for more details.

Owner
Ji Hou
PhD Candidate in TUM
Ji Hou
GitHub Repository
Mask-invariant Face Recognition through Template-level Knowledge Distillation

Mask-invariant Face Recognition through Template-level Knowledge Distillation This is the official repository of "Mask-invariant Face Recognition thro

Fadi Boutros 35 Dec 06, 2022
ALFRED - A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

ALFRED A Benchmark for Interpreting Grounded Instructions for Everyday Tasks Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han,

ALFRED 204 Dec 15, 2022
Download and preprocess popular sequential recommendation datasets

Sequential Recommendation Datasets This repository collects some commonly used sequential recommendation datasets in recent research papers and provid

125 Dec 06, 2022
Unsupervised Attributed Multiplex Network Embedding (AAAI 2020)

Unsupervised Attributed Multiplex Network Embedding (DMGI) Overview Nodes in a multiplex network are connected by multiple types of relations. However

Chanyoung Park 114 Dec 06, 2022
This is the official implementation code repository of Underwater Light Field Retention : Neural Rendering for Underwater Imaging (Accepted by CVPR Workshop2022 NTIRE)

Underwater Light Field Retention : Neural Rendering for Underwater Imaging (UWNR) (Accepted by CVPR Workshop2022 NTIRE) Authors: Tian Ye†, Sixiang Che

jmucsx 17 Dec 14, 2022
[ICCV 2021] FaPN: Feature-aligned Pyramid Network for Dense Image Prediction

FaPN: Feature-aligned Pyramid Network for Dense Image Prediction [arXiv] [Project Page] @inproceedings{ huang2021fapn, title={{FaPN}: Feature-alig

Shihua Huang 23 Jul 22, 2022
We will release the code of "ConTNet: Why not use convolution and transformer at the same time?" in this repo

ConTNet Introduction ConTNet (Convlution-Tranformer Network) is proposed mainly in response to the following two issues: (1) ConvNets lack a large rec

93 Nov 08, 2022
Tesla Light Show xLights Guide With python

Tesla Light Show xLights Guide Welcome to the Tesla Light Show xLights guide! You can create and run your own light shows on Tesla vehicles. Running a

Tesla, Inc. 2.5k Dec 29, 2022
Tensorflow2 Keras-based Semantic Segmentation Models Implementation

Tensorflow2 Keras-based Semantic Segmentation Models Implementation

Hah Min Lew 1 Feb 08, 2022
Easy and comprehensive assessment of predictive power, with support for neuroimaging features

Documentation: https://raamana.github.io/neuropredict/ News As of v0.6, neuropredict now supports regression applications i.e. predicting continuous t

Pradeep Reddy Raamana 93 Nov 29, 2022
A Python reference implementation of the CF data model

cfdm A Python reference implementation of the CF data model. References Compliance with FAIR principles Documentation https://ncas-cms.github.io/cfdm

NCAS CMS 25 Dec 13, 2022
General Virtual Sketching Framework for Vector Line Art (SIGGRAPH 2021)

General Virtual Sketching Framework for Vector Line Art - SIGGRAPH 2021 Paper | Project Page Outline Dependencies Testing with Trained Weights Trainin

Haoran MO 118 Dec 27, 2022
A Jinja extension (compatible with Flask and other frameworks) to compile and/or compress your assets.

A Jinja extension (compatible with Flask and other frameworks) to compile and/or compress your assets.

Jayson Reis 94 Nov 21, 2022
The code written during my Bachelor Thesis "Classification of Human Whole-Body Motion using Hidden Markov Models".

This code was written during the course of my Bachelor thesis Classification of Human Whole-Body Motion using Hidden Markov Models. Some things might

Matthias Plappert 14 Dec 06, 2022
A curated list and survey of awesome Vision Transformers.

English | 简体中文 A curated list and survey of awesome Vision Transformers. You can use mind mapping software to open the mind mapping source file. You c

OpenMMLab 281 Dec 21, 2022
Github project for Attention-guided Temporal Coherent Video Object Matting.

Attention-guided Temporal Coherent Video Object Matting This is the Github project for our paper Attention-guided Temporal Coherent Video Object Matti

71 Dec 19, 2022
Provide baselines and evaluation metrics of the task: traffic flow prediction

Note: This repo is adpoted from https://github.com/UNIMIBInside/Smart-Mobility-Prediction. Due to technical reasons, I did not fork their code. Introd

Zhangzhi Peng 11 Nov 02, 2022
⚡️Optimizing einsum functions in NumPy, Tensorflow, Dask, and more with contraction order optimization.

Optimized Einsum Optimized Einsum: A tensor contraction order optimizer Optimized einsum can significantly reduce the overall execution time of einsum

Daniel Smith 653 Dec 30, 2022
Neural Surface Maps

Neural Surface Maps Official implementation of Neural Surface Maps - Luca Morreale, Noam Aigerman, Vladimir Kim, Niloy J. Mitra [Paper] [Project Page]

Luca Morreale 49 Dec 13, 2022
Iowa Project - My second project done at General Assembly, focused on feature engineering and understanding Linear Regression as a concept

Project 2 - Ames Housing Data and Kaggle Challenge PROBLEM STATEMENT Inferring or Predicting? What's more valuable for a housing model? When creating

Adam Muhammad Klesc 1 Jan 03, 2022