Repo for my Tensorflow/Keras CV experiments. Mostly revolving around the Danbooru20xx dataset

Related tags

Deep LearningSW-CV-ModelZoo
Overview

SW-CV-ModelZoo

Repo for my Tensorflow/Keras CV experiments. Mostly revolving around the Danbooru20xx dataset

Framework: TF/Keras 2.7

Training SQLite DB built using fire-egg's tools: https://github.com/fire-eggs/Danbooru2019

Currently training on Danbooru2021, 512px SFW subset (sans the rating:q images that had been included in the 2022-01-21 release of the dataset)

Reference:

Anonymous, The Danbooru Community, & Gwern Branwen; “Danbooru2021: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset”, 2022-01-21. Web. Accessed 2022-01-28 https://www.gwern.net/Danbooru2021

Journal

06/02/2022: great news crew! TRC allowed me to use a bunch of TPUs!

To make better use of this amount of compute I had to overhaul a number of components, so a bunch of things are likely to have fallen to bitrot in the process. I can only guarantee NFNet can work pretty much as before with the right arguments.
NFResNet changes should have left it retrocompatible with the previous version.
ResNet has been streamlined to be mostly in line with the Bag-of-Tricks paper (arXiv:1812.01187) with the exception of the stem. It is not compatible with the previous version of the code.

The training labels have been included in the 2021_0000_0899 folder for convenience.
The list of files used for training is going to be uploaded as a GitHub Release.

Now for some numbers:
compared to my previous best run, the one that resulted in NFNetL1V1-100-0.57141:

  • I'm using 1.86x the amount of images: 2.8M vs 1.5M
  • I'm training bigger models: 61M vs 45M params
  • ... in less time: 232 vs 700 hours of processor time
  • don't get me started on actual wall clock time
  • with a few amenities thrown in: ECA for channel attention, SiLU activation

And it's all thanks to the folks at TRC, so shout out to them!

I currently have a few runs in progress across a couple of dimensions:

  • effect of model size with NFNet L0/L1/L2, with SiLU and ECA for all three of them
  • effect of activation function with NFNet L0, with SiLU/HSwish/ReLU, no ECA

Once the experiments are over, the plan is to select the network definitions that lay on the Pareto curve between throughput and F1 score and release the trained weights.

One last thing.
I'd like to call your attention to the tools/cleanlab_stuff.py script.
It reads two files: one with the binarized labels from the database, the other with the predicted probabilities.
It then uses the cleanlab package to estimate whether if an image in a set could be missing a given label. At the end it stores its conclusions in a json file.
This file could, potentially, be used in some tool to assist human intervention to add the missing tags.

You might also like...
Human head pose estimation using Keras over TensorFlow.
Human head pose estimation using Keras over TensorFlow.

RealHePoNet: a robust single-stage ConvNet for head pose estimation in the wild.

Rafael Berral Soler 71 Jan 5, 2023
Graph Neural Networks with Keras and Tensorflow 2.

Welcome to Spektral Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2. The main goal of this project is to

Daniele Grattarola 2.2k Jan 8, 2023
QKeras: a quantization deep learning library for Tensorflow Keras

QKeras github.com/google/qkeras QKeras 0.8 highlights: Automatic quantization using QKeras; Stochastic behavior (including stochastic rouding) is disa

Google 437 Jan 3, 2023
Hyperparameter Optimization for TensorFlow, Keras and PyTorch
Hyperparameter Optimization for TensorFlow, Keras and PyTorch

Hyperparameter Optimization for Keras Talos • Key Features • Examples • Install • Support • Docs • Issues • License • Download Talos radically changes

Autonomio 1.6k Dec 15, 2022
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.

MMdnn MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model manage

Microsoft 5.7k Jan 9, 2023
Deep GPs built on top of TensorFlow/Keras and GPflow

GPflux Documentation | Tutorials | API reference | Slack What does GPflux do? GPflux is a toolbox dedicated to Deep Gaussian processes (DGP), the hier

Secondmind Labs 107 Nov 2, 2022
tf2onnx - Convert TensorFlow, Keras and Tflite models to ONNX.

tf2onnx converts TensorFlow (tf-1.x or tf-2.x), tf.keras and tflite models to ONNX via command line or python api.

Open Neural Network Exchange 1.8k Jan 8, 2023
Build tensorflow keras model pipelines in a single line of code. Created by Ram Seshadri. Collaborators welcome. Permission granted upon request.
Build tensorflow keras model pipelines in a single line of code. Created by Ram Seshadri. Collaborators welcome. Permission granted upon request.

deep_autoviml Build keras pipelines and models in a single line of code! Table of Contents Motivation How it works Technology Install Usage API Image

AutoViz and Auto_ViML 102 Dec 17, 2022
Advanced Deep Learning with TensorFlow 2 and Keras (Updated for 2nd Edition)
Advanced Deep Learning with TensorFlow 2 and Keras (Updated for 2nd Edition)

Advanced Deep Learning with TensorFlow 2 and Keras (Updated for 2nd Edition)

Packt 1.5k Jan 3, 2023
Releases(models_db2021_5500_2022_10_21)

  • models_db2021_5500_2022_10_21(Oct 21, 2022)

    ConvNext B, ViT B16
    Trained on Danbooru2021 512px SFW subset, modulos 0000-0899
    top 5500 tags (2021_0000_0899_5500/selected_tags.csv)
    alpha to white
    padding to make the image square is white
    channel order is BGR, input is 0...255, scaled to -1...1 within the model

    | run_name | definition_name | params_human | image_size | thres | F1 | |:---------------------------------|:------------------|:---------------|-------------:|--------:|-------:| | ConvNextBV1_09_25_2022_05h13m55s | B | 93.2M | 448 | 0.3673 | 0.6941 | | ViTB16_09_25_2022_04h53m38s | B16 | 90.5M | 448 | 0.3663 | 0.6918 |

    Source code(tar.gz)
    Source code(zip)
    ConvNextBV1_09_25_2022_05h13m55s.7z(322.58 MB)
    ViTB16_09_25_2022_04h53m38s.7z(312.96 MB)

  • convnexts_db2021_2022_03_22(Mar 22, 2022)

    ConvNext, T/S/B
    Trained on Danbooru2021 512px SFW subset, modulos 0000-0899
    alpha to white
    padding to make the image square is white
    channel order is BGR, input is 0...255, scaled to -1...1 within the model

    | run_name | definition_name | params_human | image_size | thres | F1 | |:---------------------------------|:------------------|:---------------|-------------:|--------:|-------:| | ConvNextBV1_03_10_2022_21h41m23s | B | 90.01M | 448 | 0.3372 | 0.6892 | | ConvNextSV1_03_11_2022_17h49m56s | S | 51.28M | 384 | 0.3301 | 0.6798 | | ConvNextTV1_03_05_2022_15h56m42s | T | 29.65M | 320 | 0.3259 | 0.6595 |

    Source code(tar.gz)
    Source code(zip)
    ConvNextBV1_03_10_2022_21h41m23s.7z(311.29 MB)
    ConvNextSV1_03_11_2022_17h49m56s.7z(177.36 MB)
    ConvNextTV1_03_05_2022_15h56m42s.7z(102.96 MB)

  • nfnets_db2021_2022_03_04(Mar 4, 2022)

    NFNet, L0/L1/L2 (based on timm Lx model definitions) Trained on Danbooru2021 512px SFW subset, modulos 0000-0899 alpha to white padding to make the image square is white channel order is BGR, input is 0...255, scaled to -1...1 within the model

    | run_name | definition_name | params_human | image_size | thres | F1 | |:---------------------------------|:------------------|:---------------|-------------:|--------:|-------:| | NFNetL2V1_02_20_2022_10h27m08s | L2 | 60.96M | 448 | 0.3231 | 0.6785 | | NFNetL1V1_02_17_2022_20h18m38s | L1 | 45.65M | 384 | 0.3259 | 0.6691 | | NFNetL0V1_02_10_2022_17h50m14s | L0 | 27.32M | 320 | 0.3190 | 0.6509 |

    Source code(tar.gz)
    Source code(zip)
    NFNetL0V1_02_10_2022_17h50m14s.7z(94.98 MB)
    NFNetL1V1_02_17_2022_20h18m38s.7z(157.97 MB)
    NFNetL2V1_02_20_2022_10h27m08s.7z(210.49 MB)

  • nfnet_tpu_training(Feb 6, 2022)

  • NFNetL1V1-100-0.57141(Dec 31, 2021)

    • NFNet, L1 (based on timm Lx model definitions), 100 epochs, F1 @ 0.4 at the end of the 100th epoch was 0.57141
    • Trained on Danbooru2020 512px SFW subset, modulos 0000-0599
    • 320px per side
    • alpha to white
    • padding to make the image square is white
    • channel order is BGR, scaled to 0-1
    • mixup alpha = 0.2 during epochs 76-100
    • analyze_metrics on Danbooru2020 original set, modulos 0984-0999: {'thres': 0.3485, 'F1': 0.6133, 'F2': 0.6133, 'MCC': 0.6094, 'A': 0.9923, 'R': 0.6133, 'P': 0.6133}
    • analyze_metrics on image IDs 4970000-5000000: {'thres': 0.3148, 'F1': 0.5942, 'F2': 0.5941, 'MCC': 0.5892, 'A': 0.9901, 'R': 0.5940, 'P': 0.5943}
    Source code(tar.gz)
    Source code(zip)
    NFNetL1V1-100-0.57141.7z(158.09 MB)
Owner
GitHub Repository
This is the official github repository of the Met dataset

The Met dataset This is the official github repository of the Met dataset. The official webpage of the dataset can be found here. What is it? This cod

Nikolaos-Antonios Ypsilantis 35 Dec 17, 2022
The repository offers the official implementation of our BMVC 2021 paper in PyTorch.

CrossMLP Cascaded Cross MLP-Mixer GANs for Cross-View Image Translation Bin Ren1, Hao Tang2, Nicu Sebe1. 1University of Trento, Italy, 2ETH, Switzerla

Bingoren 16 Jul 27, 2022
Transformer in Computer Vision

Transformer-in-Vision A paper list of some recent Transformer-based CV works. If you find some ignored papers, please open issues or pull requests. **

506 Dec 26, 2022
An OpenAI-Gym Package for Training and Testing Reinforcement Learning algorithms with OpenSim Models

Authors: Utkarsh A. Mishra and Dr. Dimitar Stanev Advisors: Dr. Dimitar Stanev and Prof. Auke Ijspeert, Biorobotics Laboratory (BioRob), EPFL Video Pl

Utkarsh Mishra 16 Dec 13, 2022
An algorithmic trading bot that learns and adapts to new data and evolving markets using Financial Python Programming and Machine Learning.

ALgorithmic_Trading_with_ML An algorithmic trading bot that learns and adapts to new data and evolving markets using Financial Python Programming and

1 Mar 14, 2022
PyTorch Implementation of Vector Quantized Variational AutoEncoders.

Pytorch implementation of VQVAE. This paper combines 2 tricks: Vector Quantization (check out this amazing blog for better understanding.) Straight-Th

Vrushank Changawala 2 Oct 06, 2021
AI-generated-characters for Learning and Wellbeing

AI-generated-characters for Learning and Wellbeing Click here for the full project page. This repository contains the source code for the paper AI-gen

MIT Media Lab 214 Jan 01, 2023
'Solving the sampling problem of the Sycamore quantum supremacy circuits

solve_sycamore This repo contains data, contraction code, and contraction order for the paper ''Solving the sampling problem of the Sycamore quantum s

Feng Pan 29 Nov 28, 2022
Implement slightly different caffe-segnet in tensorflow

Tensorflow-SegNet Implement slightly different (see below for detail) SegNet in tensorflow, successfully trained segnet-basic in CamVid dataset. Due t

Tseng Kuan Lun 364 Oct 27, 2022
Face recognition. Redefined.

FaceFinder Use a powerful CNN to identify faces in images! TABLE OF CONTENTS About The Project Built With Getting Started Prerequisites Installation U

BleepLogger 20 Jun 16, 2021
PyTorch implementation of EGVSR: Efficcient & Generic Video Super-Resolution (VSR)

This is a PyTorch implementation of EGVSR: Efficcient & Generic Video Super-Resolution (VSR), using subpixel convolution to optimize the inference speed of TecoGAN VSR model. Please refer to the offi

789 Jan 04, 2023
This is the official repository of the paper Stocastic bandits with groups of similar arms (NeurIPS 2021). It contains the code that was used to compute the figures and experiments of the paper.

Experiments How to reproduce experimental results of Stochastic bandits with groups of similar arms submitted paper ? Section 5 of the paper To reprod

Fabien 0 Oct 25, 2021
Official codebase for "B-Pref: Benchmarking Preference-BasedReinforcement Learning" contains scripts to reproduce experiments.

B-Pref Official codebase for B-Pref: Benchmarking Preference-BasedReinforcement Learning contains scripts to reproduce experiments. Install conda env

48 Dec 20, 2022
Language Used: Python . Made in Jupyter(Anaconda) notebook.

FACE-DETECTION-ATTENDENCE-SYSTEM Made in Jupyter(Anaconda) notebook. Language Used: Python Steps to perform before running the program : Install Anaco

1 Jan 12, 2022
A very short and easy implementation of Quantile Regression DQN

Quantile Regression DQN Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression (https://arx

Arsenii Senya Ashukha 80 Sep 17, 2022
PyTorch code for the paper "FIERY: Future Instance Segmentation in Bird's-Eye view from Surround Monocular Cameras"

FIERY This is the PyTorch implementation for inference and training of the future prediction bird's-eye view network as described in: FIERY: Future In

Wayve 406 Dec 24, 2022
RDA: Robust Domain Adaptation via Fourier Adversarial Attacking

RDA: Robust Domain Adaptation via Fourier Adversarial Attacking Updates 08/2021: check out our domain adaptation for video segmentation paper Domain A

17 Nov 30, 2022
Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis

Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis [Paper] [Online Demo] The following results are obtained by our SCUNet with purely syn

Kai Zhang 312 Jan 07, 2023
Applications using the GTN library and code to reproduce experiments in "Differentiable Weighted Finite-State Transducers"

gtn_applications An applications library using GTN. Current examples include: Offline handwriting recognition Automatic speech recognition Installing

Facebook Research 68 Dec 29, 2022
PyTorch implementation for ComboGAN

ComboGAN This is our ongoing PyTorch implementation for ComboGAN. Code was written by Asha Anoosheh (built upon CycleGAN) [ComboGAN Paper] If you use

Asha Anoosheh 139 Dec 20, 2022