Daniel Torres

Key Skills

Software

CVAT

Labelbox

Scale AI

SuperAnnotate

V7 Labs

Top Subject Matter

Satelite image classification

Biomedical Imaging

Sentiment - context analysis

Top Data Types

Geospatial Tiled Imagery

Text

Video

Top Task Types

Bounding Box

Point Key Point

Polygon

Text Generation

Tracking

Freelancer Overview

Throughout my academic and professional journey, I have worked on several projects that span various domains in AI and computer vision. My experience includes contributing to hyperspectral imaging projects aimed at enhancing data quality for remote sensing and biomedical applications, where I applied advanced annotation techniques to generate high-quality training datasets. I have also been involved in biomedical imaging projects, focusing on the precise labeling and segmentation of medical images to assist in diagnostics and research. Additionally, I have worked on forgery detection projects, where I played a key role in annotating visual cues for identifying tampered media, improving the model’s ability to detect subtle signs of forgery. In the realm of hidden object recognition, I have contributed to projects that required meticulous labeling of challenging datasets, enabling AI systems to locate and identify obscured or partially visible objects within complex environments. These projects have allowed me to develop a wide range of labeling expertise, reinforcing my ability to work with diverse and intricate datasets.

Labeling Experience

Sentiment analysis

LabelboxTextText Summarization

In this project, I focused on labeling textual data for sentiment analysis, where I annotated text samples from various sources, such as product reviews and social media posts, with their corresponding sentiment, positive, negative, or neutral. The goal was to train machine learning models to automatically detect the emotional tone in textual data. I was responsible for ensuring accurate and consistent sentiment labeling, which involved understanding the context and nuances of the text.

In this project, I focused on labeling textual data for sentiment analysis, where I annotated text samples from various sources, such as product reviews and social media posts, with their corresponding sentiment, positive, negative, or neutral. The goal was to train machine learning models to automatically detect the emotional tone in textual data. I was responsible for ensuring accurate and consistent sentiment labeling, which involved understanding the context and nuances of the text.

2024 - 2024

Forgery detection

LabelboxImagePoint Key Point

In this multimedia forgery detection project, I contributed to the labeling of tampered images and videos to train AI models that detect manipulations, such as deepfakes or image alterations. The task involved identifying subtle inconsistencies in lighting, shadows, textures, and other visual clues that indicated potential forgery. By providing high-quality annotations, I helped improve the model’s accuracy in detecting even the smallest signs of media manipulation.

In this multimedia forgery detection project, I contributed to the labeling of tampered images and videos to train AI models that detect manipulations, such as deepfakes or image alterations. The task involved identifying subtle inconsistencies in lighting, shadows, textures, and other visual clues that indicated potential forgery. By providing high-quality annotations, I helped improve the model’s accuracy in detecting even the smallest signs of media manipulation.

2023 - 2023

Biomedical Imaging

CVATMedical DicomPolygon

This project involved labeling and segmenting medical imaging data such as MRI, CT scans, and histopathological images. The goal was to assist in the development of AI models that could support medical diagnostics by identifying regions of interest, such as tumors or other anomalies, with high accuracy. I used advanced annotation tools to ensure precise labeling of both normal and pathological areas, helping the AI models to distinguish between healthy and abnormal tissues effectively.

This project involved labeling and segmenting medical imaging data such as MRI, CT scans, and histopathological images. The goal was to assist in the development of AI models that could support medical diagnostics by identifying regions of interest, such as tumors or other anomalies, with high accuracy. I used advanced annotation tools to ensure precise labeling of both normal and pathological areas, helping the AI models to distinguish between healthy and abnormal tissues effectively.

2023 - 2023

Hyperspectral Imaging

V7 LabsGeospatial Tiled ImageryPoint Key Point

The project involved labeling distinct spectral signatures across various materials or tissues, allowing machine learning models to identify subtle differences that would be invisible in standard imaging. My role was to apply precise segmentation techniques, ensuring that each pixel in the dataset was accurately labeled to improve the classification and analysis of hyperspectral data.

The project involved labeling distinct spectral signatures across various materials or tissues, allowing machine learning models to identify subtle differences that would be invisible in standard imaging. My role was to apply precise segmentation techniques, ensuring that each pixel in the dataset was accurately labeled to improve the classification and analysis of hyperspectral data.

2023 - 2023

Hidden Object recognition

LabelboxImagePolygon

This project focused on developing AI systems capable of recognizing obscured or partially visible objects in complex environments, such as in security footage or challenging real-world scenarios. My work involved labeling objects that were partially hidden or camouflaged within the scene, requiring meticulous attention to detail. I applied object tracking and segmentation techniques to ensure consistent and accurate labeling, allowing the AI to improve its ability to recognize hidden objects with greater accuracy.

This project focused on developing AI systems capable of recognizing obscured or partially visible objects in complex environments, such as in security footage or challenging real-world scenarios. My work involved labeling objects that were partially hidden or camouflaged within the scene, requiring meticulous attention to detail. I applied object tracking and segmentation techniques to ensure consistent and accurate labeling, allowing the AI to improve its ability to recognize hidden objects with greater accuracy.

2023 - 2023

Education

Work History