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Can you turn $100 into $5,000 in just 7 days?
Jay can. And she’s challenging YOU to do the same. 👇
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❤2
🟢 Name Of Dataset: ICDAR 2013
🟢 Description Of Dataset:
TheICDAR 2013dataset consists of 229 training images and 233 testing images, with word-level annotations provided. It is the standard benchmark dataset for evaluating near-horizontal text detection.Source:Single Shot Text Detector with Regional Attention
🟢 Official Homepage: https://rrc.cvc.uab.es/?ch=2
🟢 Number of articles that used this dataset: Unknown
🟢 Dataset Loaders:
activeloopai/Hub:
https://docs.activeloop.ai/datasets/icdar-2013-dataset
mindee/doctr:
https://mindee.github.io/doctr/latest/datasets.html#doctr.datasets.IC13
tanglang96/DataLoaders_DALI:
https://github.com/tanglang96/DataLoaders_DALI
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
TheICDAR 2013dataset consists of 229 training images and 233 testing images, with word-level annotations provided. It is the standard benchmark dataset for evaluating near-horizontal text detection.Source:Single Shot Text Detector with Regional Attention
🟢 Official Homepage: https://rrc.cvc.uab.es/?ch=2
🟢 Number of articles that used this dataset: Unknown
🟢 Dataset Loaders:
activeloopai/Hub:
https://docs.activeloop.ai/datasets/icdar-2013-dataset
mindee/doctr:
https://mindee.github.io/doctr/latest/datasets.html#doctr.datasets.IC13
tanglang96/DataLoaders_DALI:
https://github.com/tanglang96/DataLoaders_DALI
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤3
🟢 Name Of Dataset: UFPR-ALPR
🟢 Description Of Dataset:
This dataset includes 4,500 fully annotated images (over 30,000 license plate characters) from 150 vehicles in real-world scenarios where both the vehicle and the camera (inside another vehicle) are moving.The images were acquired with three different cameras and are available in the Portable Network Graphics (PNG) format with a size of 1,920 × 1,080 pixels. The cameras used were: GoPro Hero4 Silver, Huawei P9 Lite, and iPhone 7 Plus.We collected 1,500 images with each camera, divided as follows:- 900 of cars with gray license plates;- 300 of cars with red license plates;- 300 of motorcycles with gray license plates.The dataset is split as follows: 40% for training, 40% for testing and 20% for validation. Every image has the following annotations available in a text file: the camera in which the image was taken, the vehicle’s position and information such as type (car or motorcycle), manufacturer, model and year; the identification and position of the license plate, as well as the position of its characters.Source:A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
🟢 Official Homepage: https://web.inf.ufpr.br/vri/databases/ufpr-alpr/
🟢 Number of articles that used this dataset: Unknown
🟢 Dataset Loaders:
ultralytics/yolov5:
https://github.com/ultralytics/yolov5
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
This dataset includes 4,500 fully annotated images (over 30,000 license plate characters) from 150 vehicles in real-world scenarios where both the vehicle and the camera (inside another vehicle) are moving.The images were acquired with three different cameras and are available in the Portable Network Graphics (PNG) format with a size of 1,920 × 1,080 pixels. The cameras used were: GoPro Hero4 Silver, Huawei P9 Lite, and iPhone 7 Plus.We collected 1,500 images with each camera, divided as follows:- 900 of cars with gray license plates;- 300 of cars with red license plates;- 300 of motorcycles with gray license plates.The dataset is split as follows: 40% for training, 40% for testing and 20% for validation. Every image has the following annotations available in a text file: the camera in which the image was taken, the vehicle’s position and information such as type (car or motorcycle), manufacturer, model and year; the identification and position of the license plate, as well as the position of its characters.Source:A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
🟢 Official Homepage: https://web.inf.ufpr.br/vri/databases/ufpr-alpr/
🟢 Number of articles that used this dataset: Unknown
🟢 Dataset Loaders:
ultralytics/yolov5:
https://github.com/ultralytics/yolov5
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤2
🟢 Name Of Dataset: PHM2017
🟢 Description Of Dataset:
PHM2017 is a new dataset consisting of 7,192 English tweets across six diseases and conditions: Alzheimer’s Disease, heart attack (any severity), Parkinson’s disease, cancer (any type), Depression (any severity), and Stroke. The Twitter search API was used to retrieve the data using the colloquial disease names as search keywords, with the expectation of retrieving a high-recall, low precision dataset. After removing the re-tweets and replies, the tweets were manually annotated. The labels are:self-mention. The tweet contains a health mention with a health self-report of the Twitter account owner, e.g., "However, I worked hard and ran for Tokyo Mayer Election Campaign in January through February, 2014, without publicizing the cancer."other-mention. The tweet contains a health mention of a health report about someone other than the account owner, e.g., "Designer with Parkinson’s couldn’t work then engineer invents bracelet + changes her world"awareness. The tweet contains the disease name, but does not mention a specific person, e.g., "A Month Before a Heart Attack, Your Body Will Warn You With These 8 Signals"non-health. The tweet contains the disease name, but the tweet topic is not about health. "Now I can have cancer on my wall for all to see <3"Source:Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
🟢 Official Homepage: https://github.com/emory-irlab/PHM2017
🟢 Number of articles that used this dataset: 7
🟢 Dataset Loaders:
emory-irlab/PHM2017:
https://github.com/emory-irlab/PHM2017
🟢 Articles related to the dataset:
📝 PHMD: An easy data access tool for prognosis and health management datasets
📝 Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
📝 Incorporating Emotions into Health Mention Classification Task on Social Media
📝 A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets
📝 Neural Architecture Search For Fault Diagnosis
📝 Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training
📝 Multi-task Learning for Personal Health Mention Detection on Social Media
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
PHM2017 is a new dataset consisting of 7,192 English tweets across six diseases and conditions: Alzheimer’s Disease, heart attack (any severity), Parkinson’s disease, cancer (any type), Depression (any severity), and Stroke. The Twitter search API was used to retrieve the data using the colloquial disease names as search keywords, with the expectation of retrieving a high-recall, low precision dataset. After removing the re-tweets and replies, the tweets were manually annotated. The labels are:self-mention. The tweet contains a health mention with a health self-report of the Twitter account owner, e.g., "However, I worked hard and ran for Tokyo Mayer Election Campaign in January through February, 2014, without publicizing the cancer."other-mention. The tweet contains a health mention of a health report about someone other than the account owner, e.g., "Designer with Parkinson’s couldn’t work then engineer invents bracelet + changes her world"awareness. The tweet contains the disease name, but does not mention a specific person, e.g., "A Month Before a Heart Attack, Your Body Will Warn You With These 8 Signals"non-health. The tweet contains the disease name, but the tweet topic is not about health. "Now I can have cancer on my wall for all to see <3"Source:Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
🟢 Official Homepage: https://github.com/emory-irlab/PHM2017
🟢 Number of articles that used this dataset: 7
🟢 Dataset Loaders:
emory-irlab/PHM2017:
https://github.com/emory-irlab/PHM2017
🟢 Articles related to the dataset:
📝 PHMD: An easy data access tool for prognosis and health management datasets
📝 Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
📝 Incorporating Emotions into Health Mention Classification Task on Social Media
📝 A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets
📝 Neural Architecture Search For Fault Diagnosis
📝 Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training
📝 Multi-task Learning for Personal Health Mention Detection on Social Media
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤1
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This channels is for Programmers, Coders, Software Engineers.
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==================================================
📝 1. BASIC DATASET INFO
----------------------------------------
Title: E. coli Resistance Dataset
Basic Description: Antibiotic resistance profiles in E. coli clinical isolates
📖 2. FULL DATASET DESCRIPTION
----------------------------------------
Full Description:
This dataset contains 195,000+ raw records of Escherichia coli clinical isolates and their antimicrobial susceptibility test results. The data was extracted from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), a public repository funded by NIAID.
Each entry captures how a specific E. coli genome responds to a given antibiotic, along with phenotypic interpretation, lab methods, measurement values (e.g., MIC), and supporting publication links.
📥 3. API DOWNLOAD INFORMATION
----------------------------------------
API Download Link: https://www.kaggle.com/api/v1/datasets/download/valeriamaciel/e-coli-resistance-dataset
Dataset Size: Download dataset as zip (3 MB)
📊 4. FILE COUNT
----------------------------------------
File count not found
📈 5. VIEWS & DOWNLOADS
----------------------------------------
Views: 418
Downloads: 72
📚 6. RELATED NOTEBOOKS
----------------------------------------
1. Antibiotic Dataset
Upvotes: 38
URL: https://www.kaggle.com/datasets/kanchana1990/antibiotic-dataset
2. Malaria Dataset
Upvotes: 35
URL: https://www.kaggle.com/datasets/miracle9to9/files1
3. SARS-CoV-2 Genetics
Upvotes: 13
URL: https://www.kaggle.com/datasets/rtwillett/sarscov2-genetics
4. E.coli_Data_cleaning
Upvotes: 7
URL: https://www.kaggle.com/code/valeriamaciel/e-coli-data-cleaning
5. E.coli_data_analysis
Upvotes: 4
URL: https://www.kaggle.com/code/valeriamaciel/e-coli-data-analysis
📝 1. BASIC DATASET INFO
----------------------------------------
Title: E. coli Resistance Dataset
Basic Description: Antibiotic resistance profiles in E. coli clinical isolates
📖 2. FULL DATASET DESCRIPTION
----------------------------------------
Full Description:
This dataset contains 195,000+ raw records of Escherichia coli clinical isolates and their antimicrobial susceptibility test results. The data was extracted from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), a public repository funded by NIAID.
Each entry captures how a specific E. coli genome responds to a given antibiotic, along with phenotypic interpretation, lab methods, measurement values (e.g., MIC), and supporting publication links.
📥 3. API DOWNLOAD INFORMATION
----------------------------------------
API Download Link: https://www.kaggle.com/api/v1/datasets/download/valeriamaciel/e-coli-resistance-dataset
Dataset Size: Download dataset as zip (3 MB)
📊 4. FILE COUNT
----------------------------------------
File count not found
📈 5. VIEWS & DOWNLOADS
----------------------------------------
Views: 418
Downloads: 72
📚 6. RELATED NOTEBOOKS
----------------------------------------
1. Antibiotic Dataset
Upvotes: 38
URL: https://www.kaggle.com/datasets/kanchana1990/antibiotic-dataset
2. Malaria Dataset
Upvotes: 35
URL: https://www.kaggle.com/datasets/miracle9to9/files1
3. SARS-CoV-2 Genetics
Upvotes: 13
URL: https://www.kaggle.com/datasets/rtwillett/sarscov2-genetics
4. E.coli_Data_cleaning
Upvotes: 7
URL: https://www.kaggle.com/code/valeriamaciel/e-coli-data-cleaning
5. E.coli_data_analysis
Upvotes: 4
URL: https://www.kaggle.com/code/valeriamaciel/e-coli-data-analysis
❤2
Dataset Name: 1.88 Million US Wildfires
Basic Description: 24 years of geo-referenced wildfire records
📖 FULL DATASET DESCRIPTION:
==================================
This data publication contains a spatial database of wildfires that occurred in the United States from 1992 to 2015. It is the third update of a publication originally generated to support the national Fire Program Analysis (FPA) system. The wildfire records were acquired from the reporting systems of federal, state, and local fire organizations. The following core data elements were required for records to be included in this data publication: discovery date, final fire size, and a point location at least as precise as Public Land Survey System (PLSS) section (1-square mile grid). The data were transformed to conform, when possible, to the data standards of the National Wildfire Coordinating Group (NWCG). Basic error-checking was performed and redundant records were identified and removed, to the degree possible. The resulting product, referred to as the Fire Program Analysis fire-occurrence database (FPA FOD), includes 1.88 million geo-referenced wildfire records, representing a total of 140 million acres burned during the 24-year period.
This dataset is an SQLite database that contains the following information:
📥 DATASET DOWNLOAD INFORMATION
==================================
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/rtatman/188-million-us-wildfires
🔴 Dataset Size: Download dataset as zip (176 MB)
📊 Additional information:
==================================
File count not found
Views: 411,000
Downloads: 38,600
📚 RELATED NOTEBOOKS:
==================================
1. Exercise: Creating, Reading and Writing | Upvotes: 453,001
URL: https://www.kaggle.com/code/residentmario/exercise-creating-reading-and-writing
2. Exercise: Indexing, Selecting & Assigning | Upvotes: 319,639
URL: https://www.kaggle.com/code/residentmario/exercise-indexing-selecting-assigning
3. Exercise: Summary Functions and Maps | Upvotes: 269,410
URL: https://www.kaggle.com/code/residentmario/exercise-summary-functions-and-maps
4. Next Day Wildfire Spread | Upvotes: 40
URL: https://www.kaggle.com/datasets/fantineh/next-day-wildfire-spread
5. Fire statistics dataset | Upvotes: 8
URL: https://www.kaggle.com/datasets/sujaykapadnis/fire-statistics-dataset
Basic Description: 24 years of geo-referenced wildfire records
📖 FULL DATASET DESCRIPTION:
==================================
This data publication contains a spatial database of wildfires that occurred in the United States from 1992 to 2015. It is the third update of a publication originally generated to support the national Fire Program Analysis (FPA) system. The wildfire records were acquired from the reporting systems of federal, state, and local fire organizations. The following core data elements were required for records to be included in this data publication: discovery date, final fire size, and a point location at least as precise as Public Land Survey System (PLSS) section (1-square mile grid). The data were transformed to conform, when possible, to the data standards of the National Wildfire Coordinating Group (NWCG). Basic error-checking was performed and redundant records were identified and removed, to the degree possible. The resulting product, referred to as the Fire Program Analysis fire-occurrence database (FPA FOD), includes 1.88 million geo-referenced wildfire records, representing a total of 140 million acres burned during the 24-year period.
This dataset is an SQLite database that contains the following information:
📥 DATASET DOWNLOAD INFORMATION
==================================
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/rtatman/188-million-us-wildfires
🔴 Dataset Size: Download dataset as zip (176 MB)
📊 Additional information:
==================================
File count not found
Views: 411,000
Downloads: 38,600
📚 RELATED NOTEBOOKS:
==================================
1. Exercise: Creating, Reading and Writing | Upvotes: 453,001
URL: https://www.kaggle.com/code/residentmario/exercise-creating-reading-and-writing
2. Exercise: Indexing, Selecting & Assigning | Upvotes: 319,639
URL: https://www.kaggle.com/code/residentmario/exercise-indexing-selecting-assigning
3. Exercise: Summary Functions and Maps | Upvotes: 269,410
URL: https://www.kaggle.com/code/residentmario/exercise-summary-functions-and-maps
4. Next Day Wildfire Spread | Upvotes: 40
URL: https://www.kaggle.com/datasets/fantineh/next-day-wildfire-spread
5. Fire statistics dataset | Upvotes: 8
URL: https://www.kaggle.com/datasets/sujaykapadnis/fire-statistics-dataset
❤1
Dataset Name: Yahoo NSFW as Mobile Net V2 Bottlenecks
Basic Description: Yahoo NSFW as MobileNetV2 Bottlenecks
📖 FULL DATASET DESCRIPTION:
==================================
This dataset is meant to aid development of effective and computationally light NSFW filtering that can be run on low powered devices. To understand why I'm posting this dataset, see this article.
NSFW machine learning requires NSFW images, which are best not distributed on public sites (and usually against Terms of Service). Instead, this dataset contains the model outputs of 200K mostly pornographic images having been sent through the first layers of MobileNetV2. Additionally, the output of the Yahoo NSFW model are included.
Transfer learning principles can then be applied to this dataset. Using the MobileNetV2 outputs as bottlenecks, and the Yahoo NSFW outputs as target values, one can build a model which tries to mimic the Yahoo NSFW model.
The files are in several archives (it was the only way to upload this much data with a 2GB limit per file). Inside the archives are npz files (compressed numpy arrays), containing 2000 input and target tensors.
Keras was used to create the MobileNetV2 output, and you can see in the tutorial kernel how it can be utilized.
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (6 GB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/nmurray1234/yahoo-nsfw-as-mobilenetv2-bottlenecks
📊 Additional information:
==================================
File count not found
Views: 13,600
Downloads: 416
📚 RELATED NOTEBOOKS:
==================================
1. Tutorial: Yahoo NSFW as MobileNetV2 | Upvotes: 16
URL: https://www.kaggle.com/code/nmurray1234/tutorial-yahoo-nsfw-as-mobilenetv2
2. EfficientNet Keras Full Weights | Upvotes: 11
URL: https://www.kaggle.com/datasets/xhlulu/efficientnet-keras-weights
3. Starter: Yahoo NSFW as MobileNetV2 bbf75635-2 | Upvotes: 6
URL: https://www.kaggle.com/code/kerneler/starter-yahoo-nsfw-as-mobilenetv2-bbf75635-2
4. CNN Models from Yahoo NSFW | Upvotes: 5
URL: https://www.kaggle.com/code/jimojung/cnn-models-from-yahoo-nsfw
5. EfficientnetV1-V2 no-top Keras models | Upvotes: 2
URL: https://www.kaggle.com/datasets/hamzaboulahia/efficientnetsv2-keras-notop-models
Basic Description: Yahoo NSFW as MobileNetV2 Bottlenecks
📖 FULL DATASET DESCRIPTION:
==================================
This dataset is meant to aid development of effective and computationally light NSFW filtering that can be run on low powered devices. To understand why I'm posting this dataset, see this article.
NSFW machine learning requires NSFW images, which are best not distributed on public sites (and usually against Terms of Service). Instead, this dataset contains the model outputs of 200K mostly pornographic images having been sent through the first layers of MobileNetV2. Additionally, the output of the Yahoo NSFW model are included.
Transfer learning principles can then be applied to this dataset. Using the MobileNetV2 outputs as bottlenecks, and the Yahoo NSFW outputs as target values, one can build a model which tries to mimic the Yahoo NSFW model.
The files are in several archives (it was the only way to upload this much data with a 2GB limit per file). Inside the archives are npz files (compressed numpy arrays), containing 2000 input and target tensors.
Keras was used to create the MobileNetV2 output, and you can see in the tutorial kernel how it can be utilized.
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (6 GB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/nmurray1234/yahoo-nsfw-as-mobilenetv2-bottlenecks
📊 Additional information:
==================================
File count not found
Views: 13,600
Downloads: 416
📚 RELATED NOTEBOOKS:
==================================
1. Tutorial: Yahoo NSFW as MobileNetV2 | Upvotes: 16
URL: https://www.kaggle.com/code/nmurray1234/tutorial-yahoo-nsfw-as-mobilenetv2
2. EfficientNet Keras Full Weights | Upvotes: 11
URL: https://www.kaggle.com/datasets/xhlulu/efficientnet-keras-weights
3. Starter: Yahoo NSFW as MobileNetV2 bbf75635-2 | Upvotes: 6
URL: https://www.kaggle.com/code/kerneler/starter-yahoo-nsfw-as-mobilenetv2-bbf75635-2
4. CNN Models from Yahoo NSFW | Upvotes: 5
URL: https://www.kaggle.com/code/jimojung/cnn-models-from-yahoo-nsfw
5. EfficientnetV1-V2 no-top Keras models | Upvotes: 2
URL: https://www.kaggle.com/datasets/hamzaboulahia/efficientnetsv2-keras-notop-models
Dataset Name: AI vs. Human-Generated Images
Basic Description: A Curated Dataset of AI-Generated and Authentic Images
📖 FULL DATASET DESCRIPTION:
==================================
Official dataset for the 2025 Women in AI Kaggle Competition: https://www.kaggle.com/competitions/detect-ai-vs-human-generated-images
The dataset consists of authentic images sampled from the Shutterstock platform across various categories, including a balanced selection where one-third of the images feature humans. These authentic images are paired with their equivalents generated using state-of-the-art generative models. This structured pairing enables a direct comparison between real and AI-generated content, providing a robust foundation for developing and evaluating image authenticity detection systems.
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (10 GB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/alessandrasala79/ai-vs-human-generated-dataset
📊 Additional information:
==================================
Total files: 85,500
Views: 29,800
Downloads: 9,793
📚 RELATED NOTEBOOKS:
==================================
1. [99.97% LB ] Baseline with timm | Upvotes: 229
URL: https://www.kaggle.com/code/vyacheslavshen/99-97-lb-baseline-with-timm
2. [0.73298] ConvNeXT Classifier | Upvotes: 116
URL: https://www.kaggle.com/code/madhavdhage201/0-73298-convnext-classifier
3. 2025WAI ViT Image Classification | Upvotes: 64
URL: https://www.kaggle.com/code/ucas0v0zhuoqunli/2025wai-vit-image-classification
4. ShutterStock Dataset for AI vs Human-Gen. Image | Upvotes: 16
URL: https://www.kaggle.com/datasets/shreyasraghav/shutterstock-dataset-for-ai-vs-human-gen-image
5. Flickr-Face-HQ and GenAI Dataset (FF-GenAI) | Upvotes: 1
URL: https://www.kaggle.com/datasets/argonautex/flickr-face-hq-and-genai-dataset-ff-genai
==================================
⭐️ By: https://t.me/datasets1
Basic Description: A Curated Dataset of AI-Generated and Authentic Images
📖 FULL DATASET DESCRIPTION:
==================================
Official dataset for the 2025 Women in AI Kaggle Competition: https://www.kaggle.com/competitions/detect-ai-vs-human-generated-images
The dataset consists of authentic images sampled from the Shutterstock platform across various categories, including a balanced selection where one-third of the images feature humans. These authentic images are paired with their equivalents generated using state-of-the-art generative models. This structured pairing enables a direct comparison between real and AI-generated content, providing a robust foundation for developing and evaluating image authenticity detection systems.
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (10 GB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/alessandrasala79/ai-vs-human-generated-dataset
📊 Additional information:
==================================
Total files: 85,500
Views: 29,800
Downloads: 9,793
📚 RELATED NOTEBOOKS:
==================================
1. [99.97% LB ] Baseline with timm | Upvotes: 229
URL: https://www.kaggle.com/code/vyacheslavshen/99-97-lb-baseline-with-timm
2. [0.73298] ConvNeXT Classifier | Upvotes: 116
URL: https://www.kaggle.com/code/madhavdhage201/0-73298-convnext-classifier
3. 2025WAI ViT Image Classification | Upvotes: 64
URL: https://www.kaggle.com/code/ucas0v0zhuoqunli/2025wai-vit-image-classification
4. ShutterStock Dataset for AI vs Human-Gen. Image | Upvotes: 16
URL: https://www.kaggle.com/datasets/shreyasraghav/shutterstock-dataset-for-ai-vs-human-gen-image
5. Flickr-Face-HQ and GenAI Dataset (FF-GenAI) | Upvotes: 1
URL: https://www.kaggle.com/datasets/argonautex/flickr-face-hq-and-genai-dataset-ff-genai
==================================
⭐️ By: https://t.me/datasets1
❤3
Dataset Name: Arabic Handwritten Characters Dataset
Basic Description: Arabic Handwritten Characters Data-set
📖 FULL DATASET DESCRIPTION:
==================================
• A. El-Sawy, M. Loey, and H. EL-Bakry, “Arabic handwritten characters recognition using convolutional neural network,” WSEAS Transactions on Computer Research, vol. 5, pp. 11–19, 2017.
https://doi.org/10.1007/978-3-319-48308-5_54
https://link.springer.com/chapter/10.1007/978-3-319-48308-5_54
• A. El-Sawy, H. EL-Bakry, and M. Loey, “CNN for handwritten arabic digits recognition based on lenet-5,” in Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016, vol. 533, pp. 566–575, Springer International Publishing, 2016.
https://www.wseas.org/multimedia/journals/computerresearch/2017/a045818-075.php
https://arxiv.org/abs/1706.06720
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (25 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/mloey1/ahcd1
📊 Additional information:
==================================
Total files: 16,800
Views: 128,000
Downloads: 17,100
📚 RELATED NOTEBOOKS:
==================================
1. Automated feature selection with sklearn | Upvotes: 198
URL: https://www.kaggle.com/code/residentmario/automated-feature-selection-with-sklearn
2. Arabic MNIST with detection | Upvotes: 152
URL: https://www.kaggle.com/code/yehyachali/arabic-mnist-with-detection
3. Understanding Convolutional Neural Network | Upvotes: 101
URL: https://www.kaggle.com/code/bbloggsbott/understanding-convolutional-neural-network
4. Arabic-Handwritten-Chars | Upvotes: 12
URL: https://www.kaggle.com/datasets/rashwan/arabic-chars-mnist
5. Handwritten Isolated English Character Dataset | Upvotes: 3
URL: https://www.kaggle.com/datasets/fayed02/handwritten-isolated-english-character-dataset
==================================
⭐️ By: https://t.me/datasets1
Basic Description: Arabic Handwritten Characters Data-set
📖 FULL DATASET DESCRIPTION:
==================================
• A. El-Sawy, M. Loey, and H. EL-Bakry, “Arabic handwritten characters recognition using convolutional neural network,” WSEAS Transactions on Computer Research, vol. 5, pp. 11–19, 2017.
https://doi.org/10.1007/978-3-319-48308-5_54
https://link.springer.com/chapter/10.1007/978-3-319-48308-5_54
• A. El-Sawy, H. EL-Bakry, and M. Loey, “CNN for handwritten arabic digits recognition based on lenet-5,” in Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016, vol. 533, pp. 566–575, Springer International Publishing, 2016.
https://www.wseas.org/multimedia/journals/computerresearch/2017/a045818-075.php
https://arxiv.org/abs/1706.06720
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (25 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/mloey1/ahcd1
📊 Additional information:
==================================
Total files: 16,800
Views: 128,000
Downloads: 17,100
📚 RELATED NOTEBOOKS:
==================================
1. Automated feature selection with sklearn | Upvotes: 198
URL: https://www.kaggle.com/code/residentmario/automated-feature-selection-with-sklearn
2. Arabic MNIST with detection | Upvotes: 152
URL: https://www.kaggle.com/code/yehyachali/arabic-mnist-with-detection
3. Understanding Convolutional Neural Network | Upvotes: 101
URL: https://www.kaggle.com/code/bbloggsbott/understanding-convolutional-neural-network
4. Arabic-Handwritten-Chars | Upvotes: 12
URL: https://www.kaggle.com/datasets/rashwan/arabic-chars-mnist
5. Handwritten Isolated English Character Dataset | Upvotes: 3
URL: https://www.kaggle.com/datasets/fayed02/handwritten-isolated-english-character-dataset
==================================
⭐️ By: https://t.me/datasets1
❤5
Dataset Name: Turkey and Syria Earthquake Tweets
Basic Description: Tweets about the recent earthquake in Turkey and Syria
📖 FULL DATASET DESCRIPTION:
==================================
This dataset contains tweets related to the earthquake that struck Turkey and Syria on Feb 6, 2023. The dataset includes the text of each tweet, the user profile information, the time and location of each tweet, and the number of likes, retweets, and replies for each tweet. The dataset also includes any hashtags, mentions, and links used in the tweets. This dataset provides a snapshot of the conversation about the natural disaster and its impact on the region in real-time. By analyzing the content of the tweets, researchers can gain a better understanding of the public's reaction to the event and the way it was reported and discussed on social media.
DATASET ARCHIVED
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (53 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/swaptr/turkey-earthquake-tweets
📊 Additional information:
==================================
File count not found
Views: 14,400
Downloads: 1,748
📚 RELATED NOTEBOOKS:
==================================
1. Turkey Earthquake Tweets | NLP + DTC | Upvotes: 64
URL: https://www.kaggle.com/code/sujithmandala/turkey-earthquake-tweets-nlp-dtc
2. Tweet Classification(Machine Learning) | Upvotes: 63
URL: https://www.kaggle.com/code/sachinpatil1280/tweet-classification-machine-learning
3. Turkey Earthquake Tweets | Upvotes: 36
URL: https://www.kaggle.com/datasets/gpreda/turkey-earthquake-tweets
4. EDA_Turkey_and_Syria_Earthquake 📌 | Upvotes: 34
URL: https://www.kaggle.com/code/yassinabdulmahdi/eda-turkey-and-syria-earthquake
5. Turkey-Syria Earthquake Tweets | Upvotes: 4
URL: https://www.kaggle.com/datasets/mrrahulroy/turkey-syria-earthquake-tweets
==================================
⭐️ By: https://t.me/datasets1
Basic Description: Tweets about the recent earthquake in Turkey and Syria
📖 FULL DATASET DESCRIPTION:
==================================
This dataset contains tweets related to the earthquake that struck Turkey and Syria on Feb 6, 2023. The dataset includes the text of each tweet, the user profile information, the time and location of each tweet, and the number of likes, retweets, and replies for each tweet. The dataset also includes any hashtags, mentions, and links used in the tweets. This dataset provides a snapshot of the conversation about the natural disaster and its impact on the region in real-time. By analyzing the content of the tweets, researchers can gain a better understanding of the public's reaction to the event and the way it was reported and discussed on social media.
DATASET ARCHIVED
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (53 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/swaptr/turkey-earthquake-tweets
📊 Additional information:
==================================
File count not found
Views: 14,400
Downloads: 1,748
📚 RELATED NOTEBOOKS:
==================================
1. Turkey Earthquake Tweets | NLP + DTC | Upvotes: 64
URL: https://www.kaggle.com/code/sujithmandala/turkey-earthquake-tweets-nlp-dtc
2. Tweet Classification(Machine Learning) | Upvotes: 63
URL: https://www.kaggle.com/code/sachinpatil1280/tweet-classification-machine-learning
3. Turkey Earthquake Tweets | Upvotes: 36
URL: https://www.kaggle.com/datasets/gpreda/turkey-earthquake-tweets
4. EDA_Turkey_and_Syria_Earthquake 📌 | Upvotes: 34
URL: https://www.kaggle.com/code/yassinabdulmahdi/eda-turkey-and-syria-earthquake
5. Turkey-Syria Earthquake Tweets | Upvotes: 4
URL: https://www.kaggle.com/datasets/mrrahulroy/turkey-syria-earthquake-tweets
==================================
⭐️ By: https://t.me/datasets1
❤2
Dataset Name: Hand Gesture Recognition Database
Basic Description: Acquired by Leap Motion
📖 FULL DATASET DESCRIPTION:
==================================
Hand gesture recognition database is presented, composed by a set of near infrared images acquired by the Leap Motion sensor.
The database is composed by 10 different hand-gestures (showed above) that were performed by 10 different subjects (5 men and 5 women).
The database is structured in different folders as:
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (2 GB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/gti-upm/leapgestrecog
📊 Additional information:
==================================
Total files: 20,000
Views: 255,000
Downloads: 35,200
📚 RELATED NOTEBOOKS:
==================================
1. Hand Gesture Recognition Database with CNN | Upvotes: 1,022
URL: https://www.kaggle.com/code/benenharrington/hand-gesture-recognition-database-with-cnn
2. [keras] Hand Gesture Recognition CNN | Upvotes: 492
URL: https://www.kaggle.com/code/kageyama/keras-hand-gesture-recognition-cnn
3. 100% in Hand Gesture Recognition | Upvotes: 245
URL: https://www.kaggle.com/code/mohamedgobara/100-in-hand-gesture-recognition
4. Multi-Modal Dataset for Hand Gesture Recognition | Upvotes: 49
URL: https://www.kaggle.com/datasets/gti-upm/multimodhandgestrec
5. Hand Gesture Recognition Dataset | Upvotes: 8
URL: https://www.kaggle.com/datasets/tapakah68/hand-gesture-recognition-dataset
==================================
⭐️ By: https://t.me/datasets1
Basic Description: Acquired by Leap Motion
📖 FULL DATASET DESCRIPTION:
==================================
Hand gesture recognition database is presented, composed by a set of near infrared images acquired by the Leap Motion sensor.
The database is composed by 10 different hand-gestures (showed above) that were performed by 10 different subjects (5 men and 5 women).
The database is structured in different folders as:
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (2 GB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/gti-upm/leapgestrecog
📊 Additional information:
==================================
Total files: 20,000
Views: 255,000
Downloads: 35,200
📚 RELATED NOTEBOOKS:
==================================
1. Hand Gesture Recognition Database with CNN | Upvotes: 1,022
URL: https://www.kaggle.com/code/benenharrington/hand-gesture-recognition-database-with-cnn
2. [keras] Hand Gesture Recognition CNN | Upvotes: 492
URL: https://www.kaggle.com/code/kageyama/keras-hand-gesture-recognition-cnn
3. 100% in Hand Gesture Recognition | Upvotes: 245
URL: https://www.kaggle.com/code/mohamedgobara/100-in-hand-gesture-recognition
4. Multi-Modal Dataset for Hand Gesture Recognition | Upvotes: 49
URL: https://www.kaggle.com/datasets/gti-upm/multimodhandgestrec
5. Hand Gesture Recognition Dataset | Upvotes: 8
URL: https://www.kaggle.com/datasets/tapakah68/hand-gesture-recognition-dataset
==================================
⭐️ By: https://t.me/datasets1
❤5
Dataset Name: E-commerce Product Images
Basic Description: Over 2900 apparel and footwear product images with meta data
📖 FULL DATASET DESCRIPTION:
==================================
Product images provide a better first impression. According to a survey, more than 63 percent of consumers say that good product images are more important than product descriptions. For an e-commerce platform, good quality product images are instrumental in convincing shoppers to buy. Product images can help shoppers to get a better virtual “feel” about the product and engage on a deeper level.
Collection of over 2900 product images under Apparel and Footwear category. Two gender types Boys and Girls under Apparel, similarly Men and Women under Footwear. Each image is identified by an unique ID(ProductId) like 10054. fashion.csv contains additional details about the products like title, description, category, gender etc.
The dataset can be used for multiple purpose like -
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (351 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/vikashrajluhaniwal/fashion-images
📊 Additional information:
==================================
File count not found
Views: 55,500
Downloads: 7,149
📚 RELATED NOTEBOOKS:
==================================
1. Fashion Product Images (Small) | Upvotes: 414
URL: https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-small
2. Building Visual Similarity based Recommendation | Upvotes: 118
URL: https://www.kaggle.com/code/vikashrajluhaniwal/building-visual-similarity-based-recommendation
3. Myntra Fashion Product Dataset | Upvotes: 47
URL: https://www.kaggle.com/datasets/hiteshsuthar101/myntra-fashion-product-dataset
4. Computer vision with PyTorch | Upvotes: 35
URL: https://www.kaggle.com/code/aleksandrmorozov123/computer-vision-with-pytorch
5. Image Representations for Similarity Search | Upvotes: 29
URL: https://www.kaggle.com/code/palealex/image-representations-for-similarity-search
==================================
⭐️ By: https://t.me/datasets1
Basic Description: Over 2900 apparel and footwear product images with meta data
📖 FULL DATASET DESCRIPTION:
==================================
Product images provide a better first impression. According to a survey, more than 63 percent of consumers say that good product images are more important than product descriptions. For an e-commerce platform, good quality product images are instrumental in convincing shoppers to buy. Product images can help shoppers to get a better virtual “feel” about the product and engage on a deeper level.
Collection of over 2900 product images under Apparel and Footwear category. Two gender types Boys and Girls under Apparel, similarly Men and Women under Footwear. Each image is identified by an unique ID(ProductId) like 10054. fashion.csv contains additional details about the products like title, description, category, gender etc.
The dataset can be used for multiple purpose like -
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (351 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/vikashrajluhaniwal/fashion-images
📊 Additional information:
==================================
File count not found
Views: 55,500
Downloads: 7,149
📚 RELATED NOTEBOOKS:
==================================
1. Fashion Product Images (Small) | Upvotes: 414
URL: https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-small
2. Building Visual Similarity based Recommendation | Upvotes: 118
URL: https://www.kaggle.com/code/vikashrajluhaniwal/building-visual-similarity-based-recommendation
3. Myntra Fashion Product Dataset | Upvotes: 47
URL: https://www.kaggle.com/datasets/hiteshsuthar101/myntra-fashion-product-dataset
4. Computer vision with PyTorch | Upvotes: 35
URL: https://www.kaggle.com/code/aleksandrmorozov123/computer-vision-with-pytorch
5. Image Representations for Similarity Search | Upvotes: 29
URL: https://www.kaggle.com/code/palealex/image-representations-for-similarity-search
==================================
⭐️ By: https://t.me/datasets1
❤3
Dataset Name: Fish Detection (Labelled)
Basic Description: Description not found
📖 FULL DATASET DESCRIPTION:
==================================
The Fish Species Detection Dataset is an expertly curated collection designed for developing and testing object detection models focused on identifying various fish species. With this dataset, researchers and developers can leverage advanced computer vision techniques to classify fish in diverse aquatic environments.
The dataset consists of a total of 8,242 annotated images categorized into thirteen distinct fish species:
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (359 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/zehraatlgan/fish-detection
📊 Additional information:
==================================
Total files: 16,500
Views: 1,562
Downloads: 302
📚 RELATED NOTEBOOKS:
==================================
1. Fish Detection with YOLO11 | Upvotes: 108
URL: https://www.kaggle.com/code/zehraatlgan/fish-detection-with-yolo11
2. 🐟🐟🐟Fish Species Image Data | Upvotes: 85
URL: https://www.kaggle.com/datasets/sripaadsrinivasan/fish-species-image-data
3. Deep Fish Object Detection | Upvotes: 81
URL: https://www.kaggle.com/datasets/vencerlanz09/deep-fish-object-detection
4. Fish Dataset | Upvotes: 7
URL: https://www.kaggle.com/datasets/mahmoodyousaf/fish-dataset
5. fish detection yolov8 | Upvotes: 4
URL: https://www.kaggle.com/code/myriamgam62/fish-detection-yolov8
==================================
⭐️ By: https://t.me/datasets1
Basic Description: Description not found
📖 FULL DATASET DESCRIPTION:
==================================
The Fish Species Detection Dataset is an expertly curated collection designed for developing and testing object detection models focused on identifying various fish species. With this dataset, researchers and developers can leverage advanced computer vision techniques to classify fish in diverse aquatic environments.
The dataset consists of a total of 8,242 annotated images categorized into thirteen distinct fish species:
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (359 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/zehraatlgan/fish-detection
📊 Additional information:
==================================
Total files: 16,500
Views: 1,562
Downloads: 302
📚 RELATED NOTEBOOKS:
==================================
1. Fish Detection with YOLO11 | Upvotes: 108
URL: https://www.kaggle.com/code/zehraatlgan/fish-detection-with-yolo11
2. 🐟🐟🐟Fish Species Image Data | Upvotes: 85
URL: https://www.kaggle.com/datasets/sripaadsrinivasan/fish-species-image-data
3. Deep Fish Object Detection | Upvotes: 81
URL: https://www.kaggle.com/datasets/vencerlanz09/deep-fish-object-detection
4. Fish Dataset | Upvotes: 7
URL: https://www.kaggle.com/datasets/mahmoodyousaf/fish-dataset
5. fish detection yolov8 | Upvotes: 4
URL: https://www.kaggle.com/code/myriamgam62/fish-detection-yolov8
==================================
⭐️ By: https://t.me/datasets1
❤2
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Opened access to the Jay Welcome Club where the AI bot does all the work itself💻
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Opened access to the Jay Welcome Club where the AI bot does all the work itself💻
Usually you pay crazy money to get into this club, but today access is free for everyone!
23,432% on deposit earned by club members in the last 6 months📈
Just follow Jay's trades and earn! 👇
https://t.me/+mONXtEgVxtU5NmZl
🔥2❤1
Dataset Name: +7000 Dress Style Image [Resized 640 * 640]
Basic Description: The dataset includes 7238 images.
📖 FULL DATASET DESCRIPTION:
==================================
The following pre-processing was applied to each image:
The following augmentation was applied to create 3 versions of each source image:
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (468 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/ahmadrafiee/7000-dress-style-image-resized-640-640
📊 Additional information:
==================================
File count not found
Views: 1,272
Downloads: 167
📚 RELATED NOTEBOOKS:
==================================
1. MobileNetV2.0: Dress Style Image | Upvotes: 17
URL: https://www.kaggle.com/code/saswattulo/mobilenetv2-0-dress-style-image
2. F1 > 0.98 Dress Style Image Classification PyTorch | Upvotes: 10
URL: https://www.kaggle.com/code/killa92/f1-0-98-dress-style-image-classification-pytorch
3. Fashion-Class-dataset | Upvotes: 6
URL: https://www.kaggle.com/datasets/marvellouscreations/fashion-class-dataset
4. stylish_wheat | Upvotes: 1
URL: https://www.kaggle.com/datasets/dvorobiev/stylish-wheat
5. Licence Plate Dataset | Upvotes: 1
URL: https://www.kaggle.com/datasets/channingfisher/licence-plate-dataset
==================================
⭐️ By: https://t.me/datasets1
Basic Description: The dataset includes 7238 images.
📖 FULL DATASET DESCRIPTION:
==================================
The following pre-processing was applied to each image:
The following augmentation was applied to create 3 versions of each source image:
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (468 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/ahmadrafiee/7000-dress-style-image-resized-640-640
📊 Additional information:
==================================
File count not found
Views: 1,272
Downloads: 167
📚 RELATED NOTEBOOKS:
==================================
1. MobileNetV2.0: Dress Style Image | Upvotes: 17
URL: https://www.kaggle.com/code/saswattulo/mobilenetv2-0-dress-style-image
2. F1 > 0.98 Dress Style Image Classification PyTorch | Upvotes: 10
URL: https://www.kaggle.com/code/killa92/f1-0-98-dress-style-image-classification-pytorch
3. Fashion-Class-dataset | Upvotes: 6
URL: https://www.kaggle.com/datasets/marvellouscreations/fashion-class-dataset
4. stylish_wheat | Upvotes: 1
URL: https://www.kaggle.com/datasets/dvorobiev/stylish-wheat
5. Licence Plate Dataset | Upvotes: 1
URL: https://www.kaggle.com/datasets/channingfisher/licence-plate-dataset
==================================
⭐️ By: https://t.me/datasets1
❤5
Dataset Name: Brain MRI segmentation
Basic Description: Brain MRI images together with manual FLAIR abnormality segmentation masks
📖 FULL DATASET DESCRIPTION:
==================================
Dataset used in:
Mateusz Buda, AshirbaniSaha, Maciej A. Mazurowski "Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm." Computers in Biology and Medicine, 2019.
and
Maciej A. Mazurowski, Kal Clark, Nicholas M. Czarnek, Parisa Shamsesfandabadi, Katherine B. Peters, Ashirbani Saha "Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data." Journal of Neuro-Oncology, 2017.
This dataset contains brain MR images together with manual FLAIR abnormality segmentation masks. The images were obtained from The Cancer Imaging Archive (TCIA). They correspond to 110 patients included in The Cancer Genome Atlas (TCGA) lower-grade glioma collection with at least fluid-attenuated inversion recovery (FLAIR) sequence and genomic cluster data available. Tumor genomic clusters and patient data is provided in data.csv file. For more information on genomic data, refer to the publication "Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas" and supplementary material available at https://www.nejm.org/doi/full/10.1056/NEJMoa1402121
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (749 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/mateuszbuda/lgg-mri-segmentation
📊 Additional information:
==================================
File count not found
Views: 557,000
Downloads: 85,200
📚 RELATED NOTEBOOKS:
==================================
1. Brain MRI | Data Visualization | UNet | FPN | Upvotes: 878
URL: https://www.kaggle.com/code/bonhart/brain-mri-data-visualization-unet-fpn
2. Brain Tumor Segmentation | UNet | Dice coef: 89.6% | Upvotes: 748
URL: https://www.kaggle.com/code/abdallahwagih/brain-tumor-segmentation-unet-dice-coef-89-6
3. Brain MRI Segmentation| Using Unet | Keras | Upvotes: 725
URL: https://www.kaggle.com/code/monkira/brain-mri-segmentation-using-unet-keras
4. Brain Tumor Segmentation | Upvotes: 166
URL: https://www.kaggle.com/datasets/andrewmvd/brain-tumor-segmentation-in-mri-brats-2015
5. Figshare Brain Tumor Dataset | Upvotes: 19
URL: https://www.kaggle.com/datasets/ashkhagan/figshare-brain-tumor-dataset
==================================
⭐️ By: https://t.me/datasets1
Basic Description: Brain MRI images together with manual FLAIR abnormality segmentation masks
📖 FULL DATASET DESCRIPTION:
==================================
Dataset used in:
Mateusz Buda, AshirbaniSaha, Maciej A. Mazurowski "Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm." Computers in Biology and Medicine, 2019.
and
Maciej A. Mazurowski, Kal Clark, Nicholas M. Czarnek, Parisa Shamsesfandabadi, Katherine B. Peters, Ashirbani Saha "Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data." Journal of Neuro-Oncology, 2017.
This dataset contains brain MR images together with manual FLAIR abnormality segmentation masks. The images were obtained from The Cancer Imaging Archive (TCIA). They correspond to 110 patients included in The Cancer Genome Atlas (TCGA) lower-grade glioma collection with at least fluid-attenuated inversion recovery (FLAIR) sequence and genomic cluster data available. Tumor genomic clusters and patient data is provided in data.csv file. For more information on genomic data, refer to the publication "Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas" and supplementary material available at https://www.nejm.org/doi/full/10.1056/NEJMoa1402121
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (749 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/mateuszbuda/lgg-mri-segmentation
📊 Additional information:
==================================
File count not found
Views: 557,000
Downloads: 85,200
📚 RELATED NOTEBOOKS:
==================================
1. Brain MRI | Data Visualization | UNet | FPN | Upvotes: 878
URL: https://www.kaggle.com/code/bonhart/brain-mri-data-visualization-unet-fpn
2. Brain Tumor Segmentation | UNet | Dice coef: 89.6% | Upvotes: 748
URL: https://www.kaggle.com/code/abdallahwagih/brain-tumor-segmentation-unet-dice-coef-89-6
3. Brain MRI Segmentation| Using Unet | Keras | Upvotes: 725
URL: https://www.kaggle.com/code/monkira/brain-mri-segmentation-using-unet-keras
4. Brain Tumor Segmentation | Upvotes: 166
URL: https://www.kaggle.com/datasets/andrewmvd/brain-tumor-segmentation-in-mri-brats-2015
5. Figshare Brain Tumor Dataset | Upvotes: 19
URL: https://www.kaggle.com/datasets/ashkhagan/figshare-brain-tumor-dataset
==================================
⭐️ By: https://t.me/datasets1
❤1
Dataset Name: Annotated Skin
Basic Description: Annotated body parts of Men and Women
📖 FULL DATASET DESCRIPTION:
==================================
This dataset was built for a pet project. We tried to annotate it with different objects to make it as reusable as possible. Feel free to ask any questions.
Contains:
The Men and Women folder further contain:
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (729 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/muhammadmdurrani/annotated-skin
📊 Additional information:
==================================
File count not found
Views: 5,579
Downloads: 369
📚 RELATED NOTEBOOKS:
==================================
1. Human Images Dataset - Men and Women | Upvotes: 65
URL: https://www.kaggle.com/datasets/snmahsa/human-images-dataset-men-and-women
2. skin_parts_visualized | Upvotes: 8
URL: https://www.kaggle.com/code/peterfriedrich1/skin-parts-visualized
3. Starter: Annotated Skin cefa0707-5 | Upvotes: 1
URL: https://www.kaggle.com/code/kerneler/starter-annotated-skin-cefa0707-5
4. Multi-Races Human Body Semantic Segmentation Data | Upvotes: 0
URL: https://www.kaggle.com/datasets/nexdatafrank/multi-races-human-body-semantic-segmentation-data
5. Segmentation and Key Points of Human Body | Upvotes: 0
URL: https://www.kaggle.com/datasets/maadaaai/segmentation-and-key-points-of-human-body
==================================
⭐️ By: https://t.me/datasets1
Basic Description: Annotated body parts of Men and Women
📖 FULL DATASET DESCRIPTION:
==================================
This dataset was built for a pet project. We tried to annotate it with different objects to make it as reusable as possible. Feel free to ask any questions.
Contains:
The Men and Women folder further contain:
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (729 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/muhammadmdurrani/annotated-skin
📊 Additional information:
==================================
File count not found
Views: 5,579
Downloads: 369
📚 RELATED NOTEBOOKS:
==================================
1. Human Images Dataset - Men and Women | Upvotes: 65
URL: https://www.kaggle.com/datasets/snmahsa/human-images-dataset-men-and-women
2. skin_parts_visualized | Upvotes: 8
URL: https://www.kaggle.com/code/peterfriedrich1/skin-parts-visualized
3. Starter: Annotated Skin cefa0707-5 | Upvotes: 1
URL: https://www.kaggle.com/code/kerneler/starter-annotated-skin-cefa0707-5
4. Multi-Races Human Body Semantic Segmentation Data | Upvotes: 0
URL: https://www.kaggle.com/datasets/nexdatafrank/multi-races-human-body-semantic-segmentation-data
5. Segmentation and Key Points of Human Body | Upvotes: 0
URL: https://www.kaggle.com/datasets/maadaaai/segmentation-and-key-points-of-human-body
==================================
⭐️ By: https://t.me/datasets1
❤2
Dataset Name: Gender Recognizer
Basic Description: Gender Classification Dataset
📖 FULL DATASET DESCRIPTION:
==================================
This dataset contains a total of 2000 images, with an equal distribution of 1000 images of men and 1000 images of women. Each individual is depicted wearing a white t-shirt. The images are standardized to a resolution of 512x512 pixels.
This dataset is ideal for tasks such as:
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (670 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/anthonytherrien/gender-recognizer
📊 Additional information:
==================================
File count not found
Views: 933
Downloads: 157
📚 RELATED NOTEBOOKS:
==================================
1. Men/Women Classification | Upvotes: 132
URL: https://www.kaggle.com/datasets/playlist/men-women-classification
2. Biggest gender/face recognition dataset. | Upvotes: 77
URL: https://www.kaggle.com/datasets/maciejgronczynski/biggest-genderface-recognition-dataset
3. 🔍 Gender Classification with CNN 🔍 | Upvotes: 7
URL: https://www.kaggle.com/code/anthonytherrien/gender-classification-with-cnn
4. 🔍 Gender Classification with ResNet18 🔍 | Upvotes: 7
URL: https://www.kaggle.com/code/anthonytherrien/gender-classification-with-resnet18
5. Gender Recognizer Lightning 2024 | Upvotes: 5
URL: https://www.kaggle.com/code/stpeteishii/gender-recognizer-lightning-2024
==================================
⭐️ By: https://t.me/datasets1
Basic Description: Gender Classification Dataset
📖 FULL DATASET DESCRIPTION:
==================================
This dataset contains a total of 2000 images, with an equal distribution of 1000 images of men and 1000 images of women. Each individual is depicted wearing a white t-shirt. The images are standardized to a resolution of 512x512 pixels.
This dataset is ideal for tasks such as:
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (670 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/anthonytherrien/gender-recognizer
📊 Additional information:
==================================
File count not found
Views: 933
Downloads: 157
📚 RELATED NOTEBOOKS:
==================================
1. Men/Women Classification | Upvotes: 132
URL: https://www.kaggle.com/datasets/playlist/men-women-classification
2. Biggest gender/face recognition dataset. | Upvotes: 77
URL: https://www.kaggle.com/datasets/maciejgronczynski/biggest-genderface-recognition-dataset
3. 🔍 Gender Classification with CNN 🔍 | Upvotes: 7
URL: https://www.kaggle.com/code/anthonytherrien/gender-classification-with-cnn
4. 🔍 Gender Classification with ResNet18 🔍 | Upvotes: 7
URL: https://www.kaggle.com/code/anthonytherrien/gender-classification-with-resnet18
5. Gender Recognizer Lightning 2024 | Upvotes: 5
URL: https://www.kaggle.com/code/stpeteishii/gender-recognizer-lightning-2024
==================================
⭐️ By: https://t.me/datasets1
❤4
Dataset Name: Preprocessed IXI MRI
Basic Description: Preprocessed T1 MRI with Skull-Stripping, Tissue Segmentation, and Jacobian Maps
📖 FULL DATASET DESCRIPTION:
==================================
This dataset consists of preprocessed T1-weighted MRI images from the IXI dataset, focused on gray matter (GM) and white matter (WM) segmentation. The images were processed using the CAT12 toolbox within the SPM software (MATLAB) for skull stripping, registration to the MNI standard space, and tissue segmentation. Additionally, Jacobian maps for each MRI scan are provided, which reflect local volume changes after the registration step.
Data Content: Gray Matter (GM): Segmented gray matter maps for each subject. Files: mwp1.nii (processed gray matter images). White Matter (WM): Segmented white matter maps for each subject. Files: mwp2.nii (processed white matter images). Jacobian Maps: A representation of local volume changes (deformation) after the MRI images have been registered to the MNI standard. Files: wj.nii (Jacobian maps for each MRI scan). Registered and Skull-Stripped MRI: The images have been skull-stripped and registered to the MNI standard space using CAT12. Files: wm.nii (registered and skull-stripped MRI images). Preprocessing Details: Skull Stripping: The brain was extracted from the surrounding skull and non-brain tissues. Registration to MNI Standard: Each subject's MRI was registered to the Montreal Neurological Institute (MNI) standard template for spatial normalization. Segmentation: Gray matter and white matter regions were segmented from the MRI scans. Jacobian Mapping: The Jacobian maps show the local volume differences between the original and the normalized images. Use Cases: This dataset is suitable for researchers working on neuroimaging analysis, particularly those focused on:
Brain tissue segmentation (gray matter and white matter). Structural MRI preprocessing workflows. Registration and skull stripping. Jacobian mapping for investigating structural deformations or changes. Source: The MRI images were sourced from the , a publicly available collection of T1-weighted MR scans.
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (5 GB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/hamedamin/preprocessed-oasis-and-epilepsy-and-ixi
📊 Additional information:
==================================
File count not found
Views: 634
Downloads: 97
📚 RELATED NOTEBOOKS:
==================================
1. Brain MRI Dataset for Tumor Detection and Analysis | Upvotes: 32
URL: https://www.kaggle.com/datasets/sudipde25/mri-dataset-for-detection-and-analysis
2. Brain Tumor MRI Classification Dataset | Upvotes: 9
URL: https://www.kaggle.com/datasets/theiturhs/brain-tumor-mri-classification-dataset
3. Load_preprocessed_IXI_MRI | Upvotes: 4
URL: https://www.kaggle.com/code/hamedamin/load-preprocessed-ixi-mri
4. ibsr - brain tissue segmentation dataset | Upvotes: 1
URL: https://www.kaggle.com/datasets/rishukumaroo7/ibsr-brain-tissue-segmentation-dataset
5. Dataset MR-MS | Upvotes: 0
URL: https://www.kaggle.com/datasets/blossom1994/dataset-mr-ms
==================================
⭐️ By: https://t.me/datasets1
Basic Description: Preprocessed T1 MRI with Skull-Stripping, Tissue Segmentation, and Jacobian Maps
📖 FULL DATASET DESCRIPTION:
==================================
This dataset consists of preprocessed T1-weighted MRI images from the IXI dataset, focused on gray matter (GM) and white matter (WM) segmentation. The images were processed using the CAT12 toolbox within the SPM software (MATLAB) for skull stripping, registration to the MNI standard space, and tissue segmentation. Additionally, Jacobian maps for each MRI scan are provided, which reflect local volume changes after the registration step.
Data Content: Gray Matter (GM): Segmented gray matter maps for each subject. Files: mwp1.nii (processed gray matter images). White Matter (WM): Segmented white matter maps for each subject. Files: mwp2.nii (processed white matter images). Jacobian Maps: A representation of local volume changes (deformation) after the MRI images have been registered to the MNI standard. Files: wj.nii (Jacobian maps for each MRI scan). Registered and Skull-Stripped MRI: The images have been skull-stripped and registered to the MNI standard space using CAT12. Files: wm.nii (registered and skull-stripped MRI images). Preprocessing Details: Skull Stripping: The brain was extracted from the surrounding skull and non-brain tissues. Registration to MNI Standard: Each subject's MRI was registered to the Montreal Neurological Institute (MNI) standard template for spatial normalization. Segmentation: Gray matter and white matter regions were segmented from the MRI scans. Jacobian Mapping: The Jacobian maps show the local volume differences between the original and the normalized images. Use Cases: This dataset is suitable for researchers working on neuroimaging analysis, particularly those focused on:
Brain tissue segmentation (gray matter and white matter). Structural MRI preprocessing workflows. Registration and skull stripping. Jacobian mapping for investigating structural deformations or changes. Source: The MRI images were sourced from the , a publicly available collection of T1-weighted MR scans.
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (5 GB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/hamedamin/preprocessed-oasis-and-epilepsy-and-ixi
📊 Additional information:
==================================
File count not found
Views: 634
Downloads: 97
📚 RELATED NOTEBOOKS:
==================================
1. Brain MRI Dataset for Tumor Detection and Analysis | Upvotes: 32
URL: https://www.kaggle.com/datasets/sudipde25/mri-dataset-for-detection-and-analysis
2. Brain Tumor MRI Classification Dataset | Upvotes: 9
URL: https://www.kaggle.com/datasets/theiturhs/brain-tumor-mri-classification-dataset
3. Load_preprocessed_IXI_MRI | Upvotes: 4
URL: https://www.kaggle.com/code/hamedamin/load-preprocessed-ixi-mri
4. ibsr - brain tissue segmentation dataset | Upvotes: 1
URL: https://www.kaggle.com/datasets/rishukumaroo7/ibsr-brain-tissue-segmentation-dataset
5. Dataset MR-MS | Upvotes: 0
URL: https://www.kaggle.com/datasets/blossom1994/dataset-mr-ms
==================================
⭐️ By: https://t.me/datasets1
❤3