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Aerial Semantic Segmentation Drone Dataset
aerial semantic Segmentation
Table 1: Semanic classes of the Drone Dataset
tree, gras, other vegetation, dirt, gravel, rocks, water, paved area, pool, person, dog, car, bicycle, roof, wall, fence, fence-pole, window, door, obstacle
https://t.me/datasets1🎁
aerial semantic Segmentation
Table 1: Semanic classes of the Drone Dataset
tree, gras, other vegetation, dirt, gravel, rocks, water, paved area, pool, person, dog, car, bicycle, roof, wall, fence, fence-pole, window, door, obstacle
https://t.me/datasets1
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Kaggle Data Hub
Aerial Semantic Segmentation Drone Dataset.zip.002
1.9 GB
Aerial Semantic Segmentation Drone Dataset
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Forwarded from Tomas
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Average earnings from 100$ a day
Lisa is looking for people who want to earn money. If you are responsible, motivated and want to change your life. Welcome to her channel.
WHAT YOU NEED TO WORK:
1. phone or computer
2. Free 15-20 minutes a day
3. desire to earn
❗️ Requires 20 people ❗️
Access is available at the link below
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Fruits-360 dataset
A dataset with 94110 images of 141 fruits, vegetables and nuts
https://t.me/datasets1✈️
A dataset with 94110 images of 141 fruits, vegetables and nuts
https://t.me/datasets1
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Fruits-360 dataset.zip
963.8 MB
Fruits-360 dataset
A dataset with 94110 images of 141 fruits, vegetables and nuts
https://t.me/datasets1✈️
A dataset with 94110 images of 141 fruits, vegetables and nuts
https://t.me/datasets1
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Amazon Books Reviews.zip
1.1 GB
Amazon Books Reviews 🛒
Goodreads-books reviews and descriptions of each book
https://t.me/datasets1⭐️
Goodreads-books reviews and descriptions of each book
https://t.me/datasets1
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US Accidents (2016 - 2023)
A Countrywide Traffic Accident Dataset (2016 - 2023)
About Dataset
Description
This is a countrywide car accident dataset that covers 49 states of the USA. The accident data were collected from February 2016 to March 2023, using multiple APIs that provide streaming traffic incident (or event) data. These APIs broadcast traffic data captured by various entities, including the US and state departments of transportation, law enforcement agencies, traffic cameras, and traffic sensors within the road networks. The dataset currently contains approximately 7.7 million accident records
US Accidents (2016 - 2023)
A Countrywide Traffic Accident Dataset (2016 - 2023)
https://t.me/datasets1✅
A Countrywide Traffic Accident Dataset (2016 - 2023)
About Dataset
Description
This is a countrywide car accident dataset that covers 49 states of the USA. The accident data were collected from February 2016 to March 2023, using multiple APIs that provide streaming traffic incident (or event) data. These APIs broadcast traffic data captured by various entities, including the US and state departments of transportation, law enforcement agencies, traffic cameras, and traffic sensors within the road networks. The dataset currently contains approximately 7.7 million accident records
US Accidents (2016 - 2023)
A Countrywide Traffic Accident Dataset (2016 - 2023)
https://t.me/datasets1
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US Accidents (2016 - 2023).zip
653.1 MB
US Accidents (2016 - 2023)
A Countrywide Traffic Accident Dataset (2016 - 2023)
https://t.me/datasets1🇺🇸
A Countrywide Traffic Accident Dataset (2016 - 2023)
https://t.me/datasets1
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Ocular Disease Recognition
Right and left eye fundus photographs of 5000 patients
Ocular Disease Intelligent Recognition (ODIR) is a structured ophthalmic database of 5,000 patients with age, color fundus photographs from left and right eyes and doctors' diagnostic keywords from doctors.
This dataset is meant to represent ‘‘real-life’’ set of patient information collected by Shanggong Medical Technology Co., Ltd. from different hospitals/medical centers in China. In these institutions, fundus images are captured by various cameras in the market, such as Canon, Zeiss and Kowa, resulting into varied image resolutions.
Annotations were labeled by trained human readers with quality control management. They classify patient into eight labels including:
Normal (N),
Diabetes (D),
Glaucoma (G),
Cataract (C),
Age related Macular Degeneration (A),
Hypertension (H),
Pathological Myopia (M),
Other diseases/abnormalities (O)
https://t.me/datasets1👩💻
Right and left eye fundus photographs of 5000 patients
Ocular Disease Intelligent Recognition (ODIR) is a structured ophthalmic database of 5,000 patients with age, color fundus photographs from left and right eyes and doctors' diagnostic keywords from doctors.
This dataset is meant to represent ‘‘real-life’’ set of patient information collected by Shanggong Medical Technology Co., Ltd. from different hospitals/medical centers in China. In these institutions, fundus images are captured by various cameras in the market, such as Canon, Zeiss and Kowa, resulting into varied image resolutions.
Annotations were labeled by trained human readers with quality control management. They classify patient into eight labels including:
Normal (N),
Diabetes (D),
Glaucoma (G),
Cataract (C),
Age related Macular Degeneration (A),
Hypertension (H),
Pathological Myopia (M),
Other diseases/abnormalities (O)
https://t.me/datasets1
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Diabetic Retinopathy Diagnosis Dataset
A Comprehensive Dataset for Medical Image Analysis
https://t.me/datasets1☠️
A Comprehensive Dataset for Medical Image Analysis
https://t.me/datasets1
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Diabetic Retinopathy Diagnosis Dataset.zip
1.3 GB
Diabetic Retinopathy Diagnosis Dataset
A Comprehensive Dataset for Medical Image Analysis
https://t.me/datasets1❤️
A Comprehensive Dataset for Medical Image Analysis
https://t.me/datasets1
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Forwarded from Machine Learning with Python
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