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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

TikTok’s Depth Anything: Revolutionizing Monocular Depth Estimation with Massive Data

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 23 Jan, 2024
2 min read

TikTok has introduced a groundbreaking development in Monocular Depth Estimation (MDE) with the release of “Depth Anything.” This innovative model leverages a colossal dataset, consisting of 62 million images, to establish itself as a foundational model in the field. Unlike traditional approaches, Depth Anything focuses on simplicity and power, setting new standards for robust image-based depth estimation.

Also Read: How To Create 3D Images For Instagram Using Bing AI?

The Power of Large-scale Unlabeled Data

Depth Anything relies on a dataset comprising 1.5 million labeled images and an impressive 62 million unlabeled images. This extensive dataset expansion is achieved through a data engine designed for collecting and automatically annotating unlabeled data. The key to its success lies in the significant reduction of generalization errors, making it a practical solution for monocular depth estimation.

Depth Anything is based on a massive dataset of 62 million images.

Strategies for Success

The model employs two effective strategies to enhance its capabilities. Firstly, a more challenging optimization target is created using data augmentation tools, compelling the model to actively seek additional visual knowledge. Secondly, auxiliary supervision ensures the model inherits rich semantic priors from pre-trained encoders, enhancing its ability to interpret and understand diverse images.

Setting New Benchmarks

In zero-shot evaluations across six public datasets and random photos, Depth Anything outperforms its predecessors. Notably, it achieves superior zero-shot relative depth estimation and metric depth estimation compared to MiDaS v3.1 and ZoeDepth, respectively. Fine-tuning on NYUv2 and KITTI datasets establishes new State-of-the-Art benchmarks, showcasing its versatility and prowess in monocular depth estimation.

Also Read: RunwayML Introduces a Multi-Motion Brush to Turn Images into Videos

Beyond Depth: Enhancing ControlNet

The impact of Depth Anything extends beyond depth estimation. Researchers re-trained a depth-conditioned ControlNet based on this model, surpassing the previous version dependent on MiDaS. This enhancement indicates broader applicability in areas like autonomous driving, emphasizing the model’s role in understanding complex environments.

TikTok's new foundational model, Depth Anything

Visualizing Depth in Dynamic Scenarios

Despite being primarily image-based, Depth Anything’s capabilities extend to dynamic scenarios showcased through video demonstrations. These visualizations underscore the model’s superiority in real-world situations, offering a glimpse into its potential applications.

Also Read: Make 3D Object from Single Image using VAST AI New Technology

Our Say

TikTok’s Depth Anything marks a significant stride in AI-driven depth perception, revolutionizing monocular depth estimation. Its reliance on a massive dataset, coupled with effective strategies and impressive benchmarks, positions it as a robust foundational model. The model’s simplicity and power, combined with its applicability beyond depth estimation, make it a noteworthy advancement in the field. Depth Anything exemplifies the potential unlocked through scaled-up diverse training data, showcasing TikTok’s commitment to innovation in AI research.

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advanced visualizationAIAI researchArtificial IntelligenceControlNetdatasetDepth AnythingFoundation Modelsimagesmolecular depth estimationnewsTikTokvideo
K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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Building a Modern App with TikTok’s “Depth Anything” Depth Estimation Model
Building a Modern App with TikTok’s “Depth Anything” Depth Estimation Model
Image Depth Estimation Using Depth Prediction Transformers (DPTs)
Image Depth Estimation Using Depth Prediction Transformers (DPTs)

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