Update README.md

README.md

@@ -4,10 +4,10 @@ language:
 - en
 library_name: nv-tesseract
 ---
-# NV-Tesseract-AD 2.0 Overview
+# NV-Tesseract-AD Overview
 
 ## Description: <br>
-NVIDIA NV-Tesseract-AD-2.0 provides anomaly detection functionality. Rather than relying only on transformers, it introduces diffusion modeling, stabilized through curriculum learning, and pairs it with adaptive thresholding methods in a model purpose-built for anomaly detection. Together, these elements address some of the most challenging issues in the field: noisy, high-dimensional signals that drift over time and contain rare, irregular events.
+NVIDIA NV-Tesseract-AD provides anomaly detection functionality. Rather than relying only on transformers, it introduces diffusion modeling, stabilized through curriculum learning, and pairs it with adaptive thresholding methods in a model purpose-built for anomaly detection. Together, these elements address some of the most challenging issues in the field: noisy, high-dimensional signals that drift over time and contain rare, irregular events.
 
 This model is for research and development only.
 
@@ -33,9 +33,9 @@ Companies, organizations, research hubs looking to do anomaly detection on tempo
 
 **Network Architecture:** ResNet34 <br>
 
-**Number of model parameters:** 2 million <br>
+**Number of model parameters:** 8 million <br>
 
-The NV-Tesseract-AD-2.0 is a diffusion-based model for time-series imputation and anomaly detection. <br>
+The NV-Tesseract-AD is a diffusion-based model for time-series imputation and anomaly detection. <br>
 
 ## Input: <br>
 **Input Type(s):** Tabular numeric <br>
@@ -67,7 +67,8 @@ Linux<br>
 The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
 
 ## Model Version(s):
-NV-Tesseract-AD-2.0<br>
+NV-Tesseract-AD<br>
+
 
 # Training & Testing Datasets: <br>
 80/20 split per dataset. <br />
@@ -77,52 +78,52 @@ NV-Tesseract-AD-2.0<br>
 **Training Data Size:** 3 million data points
 
 ### <a href="https://thedatumorg.github.io/TSB-AD/">TSB-AD-M</a><br />
-**Data Collection Method by dataset**
-Hybrid: Synthetic, Automatic/Sensors, Human<br />
-**Labeling Method by dataset**
-Hybrid: Synthetic, Automatic/Sensors, Human<br/>
-**Properties:** The TSB-AD-M benchmark includes a public category containing 18 previously proposed datasets with a total of 1980 multi-variate time series. These time series span different domains and exhibit a high variability of anomaly types, ratios, and sizes.
+**Data Collection Method by dataset**  
+Hybrid: Automatic/Sensors, Human, Synthetic<br />
+**Labeling Method by dataset**  
+Hybrid: Human, Automatic, Synthetic<br/>
+**Properties:** The TSB-AD-M benchmark consists of **17 public multivariate datasets** containing **198 curated multivariate time series** spanning diverse application domains. The datasets include both point and sequence anomalies with varying dimensionalities, anomaly ratios, anomaly lengths, and time-series lengths, providing a comprehensive benchmark for multivariate time-series anomaly detection.
 <br>
 
 # Evaluation Datasets: <br>
 
 ### <a href="https://www.kaggle.com/datasets/arbazkhan971/anomaly-detection">Detecting Anomalies in Wafer Manufacturing</a><br />
-**Data Collection Method by dataset**
-Hybrid: Synthetic, Automatic/Sensors, Human<br />
-**Labeling Method by dataset**
-Hybrid: Synthetic, Automatic/Sensors, Human<br/>
-**Properties:** 151 inline process-control traces recorded by semiconductor sensors during wafer fabrication.
+**Data Collection Method by dataset**  
+Automatic/Sensors<br />
+**Labeling Method by dataset**  
+Human, Automatic<br/>
+**Properties:** High-dimensional tabular anomaly detection dataset collected from semiconductor wafer manufacturing. Each sample represents a single wafer described by **1,558 anonymized process features** and a binary anomaly label indicating normal or anomalous production.
 <br>
 
 ### <a href="https://archive.ics.uci.edu/dataset/156/calit2+building+people+counts">CalIt2 Building People Counts</a><br />
-**Data Collection Method by dataset**
-Hybrid: Synthetic, Automatic/Sensors, Human<br />
-**Labeling Method by dataset**
-Hybrid: Synthetic, Automatic/Sensors, Human<br/>
-**Properties:** People-count sensor at the main entrance of UC-Irvine's CalIt2 building (15 weeks, 48 half-hour slots per day)
+**Data Collection Method by dataset**  
+Automatic/Sensors<br />
+**Labeling Method by dataset**  
+Human<br/>
+**Properties:** Time-series dataset containing people counts collected from sensors at the main entrance of the Calit2 building at the University of California, Irvine. Measurements were recorded every 30 minutes over approximately 15 weeks, producing 48 observations per day.
 <br>
 
 ### <a href="https://www.kaggle.com/datasets/ziya07/network-traffic-anomaly-detection-dataset">Network Traffic</a><br />
-**Data Collection Method by dataset**
-Hybrid: Synthetic, Automatic/Sensors, Human<br />
-**Labeling Method by dataset**
-Hybrid: Synthetic, Automatic/Sensors, Human<br/>
-**Properties:** This dataset contains network traffic data generated for the purpose of anomaly detection in embedded systems, specifically targeting security threats such as malicious activities. It includes both normal and anomalous (malicious) behavior, which are labeled accordingly for supervised learning tasks.
+**Data Collection Method by dataset**  
+Synthetic, Automatic/Sensors<br />
+**Labeling Method by dataset**  
+Automatic, Human<br/>
+**Properties:** Labeled network traffic dataset for anomaly detection containing normal and malicious traffic records represented by multiple network features. The dataset is intended for evaluating supervised anomaly detection methods on network intrusion scenarios.
 <br>
 
 ### <a href="https://www.kaggle.com/datasets/inIT-OWL/genesis-demonstrator-data-for-machine-learning">Genesis Demonstrator</a><br />
-**Data Collection Method by dataset**
-Hybrid: Synthetic, Automatic/Sensors, Human<br />
-**Labeling Method by dataset**
-Hybrid: Synthetic, Automatic/Sensors, Human<br/>
-**Properties:** The Genesis Demonstrator was created during the OPAK Project and further revised during the European IMPROVE project. It is a portable pick-and-place demonstrator which uses an air tank to supply all the gripping and storage units. It records 5(+4) continuous signals, 13 discrete signals and 1 Unix Timestamp.
+**Data Collection Method by dataset**  
+Automatic/Sensors<br />
+**Labeling Method by dataset**  
+Human<br/>
+**Properties:** Multivariate industrial time-series dataset collected from the Genesis portable pick-and-place demonstrator developed as part of the OPAK and IMPROVE projects. The dataset contains continuous and discrete sensor signals together with timestamps and labeled anomalous operating conditions.
 <br>
 
 ### <a href="https://www.kaggle.com/datasets/jorekai/anomaly-detection-falling-people-events?select=data">Falling People</a><br />
-**Data Collection Method by dataset**
-Hybrid: Synthetic, Automatic/Sensors, Human<br />
-**Labeling Method by dataset**
-Hybrid: Synthetic, Automatic/Sensors, Human<br/>
+**Data Collection Method by dataset**  
+Human, Automatic/Sensors<br />
+**Labeling Method by dataset**  
+Human<br/>
 **Properties:** This data set was used during a thesis to develop safer smart environments. The origin is a care independent smart home environment to detect the falling of elderly people.
 <br>