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Car Detection and Tracking Dataset

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Car Detection and Tracking Dataset 499 Images of Car Dataset with Text Annotation

About Dataset This dataset contains 499 images, each with bounding box annotations for cars. The annotations are provided in the YOLO text format, which includes class labels and bounding box coordinates. This dataset is useful for object detection tasks such as vehicle recognition and traffic analysis.

Hand Gesture Detection System

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Hand Gesture Detection System HandMimic - An Advanced Hand Gesture Recognition System

Problem Statement As a data scientist at a leading home electronics company, my goal is to create an innovative gesture control feature for smart televisions. By utilizing a webcam mounted on the TV, the system will recognize five specific gestures, enabling users to interact with the TV hands-free, without needing a remote control. The five gestures and their corresponding actions are: Thumbs Up: Increases the volume. Thumbs Down: Decreases the volume. Left Swipe: Rewinds the content by 10 seconds. Right Swipe: Jumps forward by 10 seconds. Stop: Pauses the content. Machine learning algorithms will train the system to recognize these gestures in real-time using the webcam, providing seamless interaction and enhancing the overall user experience. Objectives The primary objective is to develop a gesture-based control feature for smart TVs, enabling users to adjust volume, skip, rewind, and pause content using five distinct gestures detected by a webcam. Machine learning will be employed to train the model to recognize these gestures instantly, offering a hands-free and intuitive TV experience. Understanding the Dataset The training dataset consists of several hundred videos, each categorized into one of five gesture classes. Each video lasts 2-3 seconds, divided into 30 frames (images). Captured by various individuals performing the gestures in front of a webcam, these videos simulate real-world smart TV use. The gestures include thumbs up, thumbs down, left swipe, right swipe, and stop, and serve as individual training samples for the gesture recognition model. Generator The generator will preprocess the video data by cropping, resizing, and normalizing it to ensure proper formatting before passing it to the model. The generator should efficiently process batches of video data, ensuring smooth training without errors. Model The objective is to create a model that trains efficiently with minimal inference time. The model’s architecture should be optimized to balance performance and speed, with fewer parameters leading to faster predictions. The model will be evaluated based on accuracy in recognizing gestures, starting with a small dataset to assess initial performance before scaling up.

Thyroid Cancer Risk Dataset

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Thyroid Cancer Risk Dataset Assessing Thyroid Cancer Risk Through Key Health Indicators This dataset incorporates 212,691 statistics related to* thyroid cancer risk factors*. It includes demographic facts, clinical history, lifestyle factors, and key thyroid hormone degrees to assess the probability of thyroid most cancers. The dataset may be beneficial for system learning fashions aiming to predict thyroid most cancers risk based on numerous indicators. Column Descriptions: Patient_ID (int): Unique identifier for each patient. Age (int): Age of the patient. Gender (object): Patient’s gender (Male/Female). Country (object): Country of residence. Ethnicity (object): Patient’s ethnic background. Family_History (object): Whether the patient has a family history of thyroid cancer (Yes/No). Radiation_Exposure (object): History of radiation exposure (Yes/No). Iodine_Deficiency (object): Presence of iodine deficiency (Yes/No). Smoking (object): Whether the patient smokes (Yes/No). Obesity (object): Whether the patient is obese (Yes/No). Diabetes (object): Whether the patient has diabetes (Yes/No). TSH_Level (float): Thyroid-Stimulating Hormone level (µIU/mL). T3_Level (float): Triiodothyronine level (ng/dL). T4_Level (float): Thyroxine level (µg/dL). Nodule_Size (float): Size of thyroid nodules (cm). Thyroid_Cancer_Risk (object): Estimated risk of thyroid cancer (Low/Medium/High). Diagnosis (object): Final diagnosis (Benign/Malignant).

Thyroid Cancer Risk Dataset Assessing Thyroid Cancer Risk Through Key Health Indicators This dataset incorporates 212,691 statistics related to* thyroid cancer risk factors*. It includes demographic facts, clinical history, lifestyle factors, and key thyroid hormone degrees to assess the probability of thyroid most cancers. The dataset may be beneficial for system learning fashions aiming to predict thyroid most cancers risk based on numerous indicators.

Column Descriptions: Patient_ID (int): Unique identifier for each patient. Age (int): Age of the patient. Gender (object): Patient’s gender (Male/Female). Country (object): Country of residence. Ethnicity (object): Patient’s ethnic background. Family_History (object): Whether the patient has a family history of thyroid cancer (Yes/No). Radiation_Exposure (object): History of radiation exposure (Yes/No). Iodine_Deficiency (object): Presence of iodine deficiency (Yes/No). Smoking (object): Whether the patient smokes (Yes/No). Obesity (object): Whether the patient is obese (Yes/No). Diabetes (object): Whether the patient has diabetes (Yes/No). TSH_Level (float): Thyroid-Stimulating Hormone level (µIU/mL). T3_Level (float): Triiodothyronine level (ng/dL). T4_Level (float): Thyroxine level (µg/dL). Nodule_Size (float): Size of thyroid nodules (cm). Thyroid_Cancer_Risk (object): Estimated risk of thyroid cancer (Low/Medium/High). Diagnosis (object): Final diagnosis (Benign/Malignant).

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Oral Cancer Prediction Dataset – Top 30 Countries

#KaggleDatasets #DataScience #MachineLearning #DataAnalysis #DataVisualization #OpenData #DataCleaning #TextClassification #NLP #SentimentAnalysis #BigData #APIAutomation #DataLicensing #SocialMediaData #PythonIntegration #DataModeling #kaggle #ComputerVision #python #LLM #DeepLearning #Pytorch #HuggingFace #Dataset https://t.me/datasets1