🟢 Name Of Dataset: RVL-CDIP
🟢 Description Of Dataset:
TheRVL-CDIPdataset consists of scanned document images belonging to 16 classes such as letter, form, email, resume, memo, etc. The dataset has 320,000 training, 40,000 validation and 40,000 test images. The images are characterized by low quality, noise, and low resolution, typically 100 dpi.Source:Towards a Multi-modal, Multi-task Learning based Pre-training Framework for Document Representation Learning
🟢 Official Homepage: https://www.cs.cmu.edu/~aharley/rvl-cdip/
🟢 Number of articles that used this dataset: Unknown
🟢 Dataset Loaders:
huggingface/datasets (rvl_cdip):
https://huggingface.co/datasets/rvl_cdip
huggingface/datasets (rvl-cdip_easyOCR):
https://huggingface.co/datasets/jordyvl/rvl-cdip_easyOCR
huggingface/datasets (rvl_cdip):
https://huggingface.co/datasets/aharley/rvl_cdip
huggingface/datasets (rvl_cdip_easyocr):
https://huggingface.co/datasets/jordyvl/rvl_cdip_easyocr
huggingface/datasets (rvl_cdip_mini):
https://huggingface.co/datasets/dvgodoy/rvl_cdip_mini
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
TheRVL-CDIPdataset consists of scanned document images belonging to 16 classes such as letter, form, email, resume, memo, etc. The dataset has 320,000 training, 40,000 validation and 40,000 test images. The images are characterized by low quality, noise, and low resolution, typically 100 dpi.Source:Towards a Multi-modal, Multi-task Learning based Pre-training Framework for Document Representation Learning
🟢 Official Homepage: https://www.cs.cmu.edu/~aharley/rvl-cdip/
🟢 Number of articles that used this dataset: Unknown
🟢 Dataset Loaders:
huggingface/datasets (rvl_cdip):
https://huggingface.co/datasets/rvl_cdip
huggingface/datasets (rvl-cdip_easyOCR):
https://huggingface.co/datasets/jordyvl/rvl-cdip_easyOCR
huggingface/datasets (rvl_cdip):
https://huggingface.co/datasets/aharley/rvl_cdip
huggingface/datasets (rvl_cdip_easyocr):
https://huggingface.co/datasets/jordyvl/rvl_cdip_easyocr
huggingface/datasets (rvl_cdip_mini):
https://huggingface.co/datasets/dvgodoy/rvl_cdip_mini
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤5
🟢 Name Of Dataset: FUNSD (Form Understanding in Noisy Scanned Documents)
🟢 Description Of Dataset:
Form Understanding in Noisy Scanned Documents (FUNSD) comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task. The proposed dataset can be used for various tasks, including text detection, optical character recognition, spatial layout analysis, and entity labeling/linking.Source:FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents
🟢 Official Homepage: https://guillaumejaume.github.io/FUNSD/
🟢 Number of articles that used this dataset: Unknown
🟢 Dataset Loaders:
huggingface/datasets:
https://huggingface.co/datasets/nielsr/FUNSD_layoutlmv2
mindee/doctr:
https://mindee.github.io/doctr/latest/datasets.html#doctr.datasets.FUNSD
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
Form Understanding in Noisy Scanned Documents (FUNSD) comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task. The proposed dataset can be used for various tasks, including text detection, optical character recognition, spatial layout analysis, and entity labeling/linking.Source:FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents
🟢 Official Homepage: https://guillaumejaume.github.io/FUNSD/
🟢 Number of articles that used this dataset: Unknown
🟢 Dataset Loaders:
huggingface/datasets:
https://huggingface.co/datasets/nielsr/FUNSD_layoutlmv2
mindee/doctr:
https://mindee.github.io/doctr/latest/datasets.html#doctr.datasets.FUNSD
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤4
🟢 Name Of Dataset: IIIT-AR-13K
🟢 Description Of Dataset:
IIIT-AR-13K is created by manually annotating the bounding boxes of graphical or page objects in publicly available annual reports. This dataset contains a total of 13k annotated page images with objects in five different popular categories - table, figure, natural image, logo, and signature. It is the largest manually annotated dataset for graphical object detection.Source:IIIT-AR-13K: A New Dataset for Graphical Object Detection in Documents
🟢 Official Homepage: http://cvit.iiit.ac.in/usodi/iiitar13k.php
🟢 Number of articles that used this dataset: 6
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 Deep learning for table detection and structure recognition: A survey
📝 RanLayNet: A Dataset for Document Layout Detection used for Domain Adaptation and Generalization
📝 The YOLO model that still excels in document layout analysis
📝 IIIT-AR-13K: A New Dataset for Graphical Object Detection in Documents
📝 Document AI: Benchmarks, Models and Applications
📝 Robust Table Detection and Structure Recognition from Heterogeneous Document Images
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
IIIT-AR-13K is created by manually annotating the bounding boxes of graphical or page objects in publicly available annual reports. This dataset contains a total of 13k annotated page images with objects in five different popular categories - table, figure, natural image, logo, and signature. It is the largest manually annotated dataset for graphical object detection.Source:IIIT-AR-13K: A New Dataset for Graphical Object Detection in Documents
🟢 Official Homepage: http://cvit.iiit.ac.in/usodi/iiitar13k.php
🟢 Number of articles that used this dataset: 6
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 Deep learning for table detection and structure recognition: A survey
📝 RanLayNet: A Dataset for Document Layout Detection used for Domain Adaptation and Generalization
📝 The YOLO model that still excels in document layout analysis
📝 IIIT-AR-13K: A New Dataset for Graphical Object Detection in Documents
📝 Document AI: Benchmarks, Models and Applications
📝 Robust Table Detection and Structure Recognition from Heterogeneous Document Images
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
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Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
Admin: @HusseinSheikho || @Hussein_Sheikho
Admin: @HusseinSheikho || @Hussein_Sheikho
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🟢 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
Forwarded from Machine Learning with Python
5 minutes of work - 127,000$ profit!
Opened access to the Jay Welcome Club where the AI bot does all the work itself💻
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23,432% on deposit earned by club members in the last 6 months📈
Just follow Jay's trades and earn! 👇
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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