Garbage Images Dataset (6 classes)

I used this dataset for my CNN Python project, you can use it by the way 😀

I've collected data from these two datasets into one: - https://www.kaggle.com/datasets/asdasdasasdas/garbage-classification - https://www.kaggle.com/datasets/mostafaabla/garbage-classification

And balanced it by removing unnecessary images and making each class's size equal to 775 images.

"'https://www.nature.com/articles/s41597-022-01721-8'">MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification https://www.nature.com/articles/s41597-022-01721-8

A large-scale MNIST-like collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into 28x28 (2D) or 28x28x28 (3D) with the corresponding classification labels, so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST is designed to perform classification on lightweight 2D and 3D images with various data scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression and multi-label). The resulting dataset, consisting of approximately 708K 2D images and 10K 3D images in total, could support numerous research and educational purposes in biomedical image analysis, computer vision and machine learning.Providers benchmark several baseline methods on MedMNIST, including 2D / 3D neural networks and open-source / commercial AutoML tools.

MedMNIST Landscape :

https://storage.googleapis.com/kagglesdsdata/datasets/4390240/7539891/medmnistlandscape.png?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=databundle-worker-v2%40kaggle-161607.iam.gserviceaccount.com%2F20240202%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240202T132716Z&X-Goog-Expires=345600&X-Goog-SignedHeaders=host&X-Goog-Signature=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" alt="medmnistlandscape">

About MedMNIST Landscape figure: The horizontal axis denotes the base-10 logarithm of the dataset scale, and the vertical axis denotes base-10 logarithm of imaging resolution. The upward and downward triangles are used to distinguish between 2D datasets and 3D datasets, and the 4 different colors represent different tasks

Key Features

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Diverse: It covers diverse data modalities, dataset scales (from 100 to 100,000), and tasks (binary/multi-class, multi-label, and ordinal regression). It is as diverse as the VDD and MSD to fairly evaluate the generalizable performance of machine learning algorithms in different settings, but both 2D and 3D biomedical images are provided.

Standardized: Each sub-dataset is pre-processed into the same format, which requires no background knowledge for users. As an MNIST-like dataset collection to perform classification tasks on small images, it primarily focuses on the machine learning part rather than the end-to-end system. Furthermore, we provide standard train-validation-test splits for all datasets in MedMNIST, therefore algorithms could be easily compared.

User-Friendly: The small size of 28×28 (2D) or 28×28×28 (3D) is lightweight and ideal for evaluating machine learning algorithms. We also offer a larger-size version, MedMNIST+: 64x64 (2D), 128x128 (2D), 224x224 (2D), and 64x64x64 (3D). Serving as a complement to the 28-size MedMNIST, this could be a standardized resource for developing medical foundation models. All these datasets are accessible via the same API.

Educational: As an interdisciplinary research area, biomedical image analysis is difficult to hand on for researchers from other communities, as it requires background knowledge from computer vision, machine learning, biomedical imaging, and clinical science. Our data with the Creative Commons (CC) License is easy to use for educational purposes.

Refer to the paper to learn more about data : https://www.nature.com/articles/s41597-022-01721-8

Starter Code: download more data and training

Github Page: https://github.com/MedMNIST/MedMNIST

My Kaggle Starter Notebook: https://www.kaggle.com/code/arashnic/medmnist-download-and-use-data?scriptVersionId=161421937

Acknowledgements

Jiancheng Yang,Rui Shi,Donglai Wei,Zequan Liu,Lin Zhao,Bilian Ke,Hanspeter Pfister,Bingbing Ni Shanghai Jiao Tong University, Shanghai, China, Boston College, Chestnut Hill, MA RWTH Aachen University, Aachen, Germany, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University, Shanghai, China, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, Harvard University, Cambridge, MA

License and Citation

The code is under Apache-2.0 License.

The MedMNIST dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)...

Small Home Objects (SHO) Dataset This dataset is consisted of 4160 different color and depth images belonging to 13 categories or classes or objects. Each object has 320 samples (160 color and 160 depth). The dataset is recorded with Microsoft Kinect sensor V.2. Categories are: 1. Ball 2. Console 3. Earmuff 4. Gamepad 5. Hand watch 6. Microphone 7. Quadcopter 8. Sharpener 9. Statue 10. Sunglass 11. Tripod 12. Vitamin 13. Webcam Please cite below after use: Mousavi, S. M. H., & Mosavi, S. M. H. (2022, March). A New Edge and Pixel-Based Image Quality Assessment Metric for Colour and Depth Images. In 2022 9th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS) (pp. 1-11). IEEE.

Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for DeepGlobe/7-class segmentation of RGB 512x512 high-res. images

These Residual-UNet model data are based on the DeepGlobe dataset

Models have been created using Segmentation Gym* using the following dataset**: https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset

Image size used by model: 512 x 512 x 3 pixels

classes: 1. urban 2. agricultural 3. rangeland 4. forest 5. water 6. bare 7. unknown

File descriptions

For each model, there are 5 files with the same root name:

1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.

2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function train_model.py. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function seg_images_in_folder.py. Models may be ensembled.

3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the config file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model

4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function train_model.py

5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function train_model.py

Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU

References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

**Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D. and Raskar, R., 2018. Deepglobe 2018: A challenge to parse the earth through satellite images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 172-181).

Fruits-360 dataset: A dataset of images containing fruits, vegetables, nuts and seeds Version: 2025.03.24.0 Content The following fruits, vegetables and nuts and are included: Apples (different varieties: Crimson Snow, Golden, Golden-Red, Granny Smith, Pink Lady, Red, Red Delicious), Apricot, Avocado, Avocado ripe, Banana (Yellow, Red, Lady Finger), Beans, Beetroot Red, Blackberry, Blueberry, Cabbage, Caju seed, Cactus fruit, Cantaloupe (2 varieties), Carambula, Carrot, Cauliflower, Cherimoya, Cherry (different varieties, Rainier), Cherry Wax (Yellow, Red, Black), Chestnut, Clementine, Cocos, Corn (with husk), Cucumber (ripened, regular), Dates, Eggplant, Fig, Ginger Root, Goosberry, Granadilla, Grape (Blue, Pink, White (different varieties)), Grapefruit (Pink, White), Guava, Hazelnut, Huckleberry, Kiwi, Kaki, Kohlrabi, Kumsquats, Lemon (normal, Meyer), Lime, Lychee, Mandarine, Mango (Green, Red), Mangostan, Maracuja, Melon Piel de Sapo, Mulberry, Nectarine (Regular, Flat), Nut (Forest, Pecan), Onion (Red, White), Orange, Papaya, Passion fruit, Peach (different varieties), Pepino, Pear (different varieties, Abate, Forelle, Kaiser, Monster, Red, Stone, Williams), Pepper (Red, Green, Orange, Yellow), Physalis (normal, with Husk), Pineapple (normal, Mini), Pistachio, Pitahaya Red, Plum (different varieties), Pomegranate, Pomelo Sweetie, Potato (Red, Sweet, White), Quince, Rambutan, Raspberry, Redcurrant, Salak, Strawberry (normal, Wedge), Tamarillo, Tangelo, Tomato (different varieties, Maroon, Cherry Red, Yellow, not ripened, Heart), Walnut, Watermelon, Zucchini (green and dark).

Branches The dataset has four major branches:

-The 100x100 branch, where all images have 100x100 pixels. See fruits-360_100x100 folder.

-The original-size branch, where all images are at their original (captured) size. See fruits-360_original-size folder.

-The meta branch, which contains additional information about the objects in the Fruits-360 dataset. See fruits-360_dataset_meta folder.

-The multi branch, which contains images with multiple fruits, vegetables, nuts and seeds. These images are not labeled. See fruits-360_multi folder.

How to cite Mihai Oltean, Fruits-360 dataset, 2017-

Dataset properties For the 100x100 branch Total number of images: 111589.

Training set size: 83616 images.

Test set size: 27973 images.

Number of classes: 166 (fruits, vegetables, nuts and seeds).

Image size: 100x100 pixels.

For the original-size branch Total number of images: 29440.

Training set size: 14731 images.

Validation set size: 7370 images

Test set size: 7339 images.

Number of classes: 48 (fruits, vegetables, nuts and seeds).

Image size: various (original, captured, size) pixels.

For the meta branch Number of classes: 26 (fruits, vegetables, nuts and seeds).

For the multi branch Number of images: 150.

Filename format: For the 100x100 branch image_index_100.jpg (e.g. 31_100.jpg) or

r_image_index_100.jpg (e.g. r_31_100.jpg) or

r?_image_index_100.jpg (e.g. r2_31_100.jpg)

where "r" stands for rotated fruit. "r2" means that the fruit was rotated around the 3rd axis. "100" comes from image size (100x100 pixels).

Different varieties of the same fruit (apple, for instance) are stored as belonging to different classes.

For the original-size branch r?_image_index.jpg (e.g. r2_31.jpg)

where "r" stands for rotated fruit. "r2" means that the fruit was rotated around the 3rd axis.

The name of the image files in the new version does NOT contain the "_100" suffix anymore. This will help you to make the distinction between the original-size branch and the 100x100 branch.

For the multi branch The file's name is the concatenation of the names of the fruits inside that picture.

Alternate download The Fruits-360 dataset can be downloaded from:

Kaggle https://www.kaggle.com/moltean/fruits

GitHub https://github.com/fruits-360

How fruits were filmed Fruits and vegetables were planted in the shaft of a low-speed motor (3 rpm) and a short movie of 20 seconds was recorded.

A Logitech C920 camera was used for filming the fruits. This is one of the best webcams available.

Behind the fruits, we placed a white sheet of paper as a background.

Here is a movie showing how the fruits and vegetables are filmed: https://youtu.be/_HFKJ144JuU

How fruits were extracted from the background However, due to the variations in the lighting conditions, the background was not uniform and we wrote a dedicated algorithm that extracts the fruit from the background. This algorithm is of flood fill type: we start from each edge of the image and we mark all pixels there, then we mark all pixels found in the neighborhood of the already marked pixels for which the distance between colors is less than a prescribed value. We repeat the previous step until no more pixels can be marked.

All marked pixels are considered as being background (which is then filled with white) and the rest of the pixels are considered as belonging to the object.

The maximum value for the distance between 2 neighbor pixels is a parameter of the algorithm and is set (by trial and error) for each movie.

Pictures from the test-multiple_fruits folder were taken with a Nexus 5X phone or an iPhone 11.

History Fruits were filmed at the dates given below (YYYY.MM.DD):

2017.02.25 - Apple (golden).

2017.02.28 - Apple (red-yellow, red, golden2), Kiwi, Pear, Grapefruit, Lemon, Orange, Strawberry, Banana.

2017.03.05 - Apple (golden3, Braeburn, Granny Smith, red2).

2017.03.07 - Apple (red3).

2017.05.10 - Plum, Peach, Peach flat, Apricot, Nectarine, Pomegranate.

2017.05.27 - Avocado, Papaya, Grape, Cherrie.

2017.12.25 - Carambula, Cactus fruit, Granadilla, Kaki, Kumsquats, Passion fruit, Avocado ripe, Quince.

2017.12.28 - Clementine, Cocos, Mango, Lime, Lychee.

2017.12.31 - Apple Red Delicious, Pear Monster, Grape White.

2018.01.14 - Ananas, Grapefruit Pink, Mandarine, Pineapple, Tangelo.

2018.01.19 - Huckleberry, Raspberry.

2018.01.26 - Dates, Maracuja, Plum 2, Salak, Tamarillo.

2018.02.05 - Guava, Grape White 2, Lemon Meyer

2018.02.07 - Banana Red, Pepino, Pitahaya Red.

2018.02.08 - Pear Abate, Pear Williams.

2018.05.22 - Lemon rotated, Pomegranate rotated.

2018.05.24 - Cherry Rainier, Cherry 2, Strawberry Wedge.

2018.05.26 - Cantaloupe (2 varieties).

2018.05.31 - Melon Piel de Sapo.

2018.06.05 - Pineapple Mini, Physalis, Physalis with Husk, Rambutan.

2018.06.08 - Mulberry, Redcurrant.

2018.06.16 - Hazelnut, Walnut, Tomato, Cherry Red.

2018.06.17 - Cherry Wax (Yellow, Red, Black).

2018.08.19 - Apple Red Yellow 2, Grape Blue, Grape White 3-4, Peach 2, Plum 3, Tomato Maroon, Tomato 1-4 .

2018.12.20 - Nut Pecan, Pear Kaiser, Tomato Yellow.

2018.12.21 - Banana Lady Finger, Chesnut, Mangostan.

2018.12.22 - Pomelo Sweetie.

2019.04.21 - Apple Crimson Snow, Apple Pink Lady, Blueberry, Kohlrabi, Mango Red, Pear Red, Pepper (Red, Yellow, Green).

2019.06.18 - Beetroot Red, Corn, Ginger Root, Nectarine Flat, Nut Forest, Onion Red, Onion Red Peeled, Onion White, Potato Red, Potato Red Washed, Potato Sweet, Potato White.

2019.07.07 - Cauliflower, Eggplant, Pear Forelle, Pepper Orange, Tomato Heart.

2019.09.22 - Corn Husk, Cucumber Ripe, Fig, Pear 2, Pear Stone, Tomato not Ripened, Watermelon.

2021.06.07 - Eggplant long 1.

2021.08.09 - Apple hit 1, Cucumber 1.

2021.09.03 - Pear 3.

2021.09.22 - Apple 6, Cucumber 3.

2023.12.30 - Official Github repository is now https://github.com/fruits-360

License CC BY-SA 4.0

Copyright (c) 2017-, Mihai Oltean

You are free to:

Share — copy and redistribute the material in any medium or format for any purpose, even commercially.

Adapt — remix, transform, and build upon the material for any purpose, even commercially.

Under the following terms:

Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

Description:

👉 Download the dataset here

This dataset contains a collection of 15,150 images, categorized into 12 distinct classes of common household waste. The classes include paper, cardboard, biological waste, metal, plastic, green glass, brown glass, white glass, clothing, shoes, batteries, and general trash. Each category represents a different type of material, contributing to more effective recycling and waste management strategies. Garbage Classification Dataset.

Objective

The purpose of this dataset is to aid in the development of machine learning models designed to automatically classify household waste into its appropriate categories, thus promoting more efficient recycling processes. Proper waste sorting is crucial for maximizing the amount of material that can be recycled, and this dataset is aimed at enhancing automation in this area. The classification of garbage into a broader range of categories, as opposed to the limited classes found in most available datasets (2-6 classes), allows for a more precise recycling process and could significantly improve recycling rates.

Download Dataset

Dataset Composition and Collection Process

The dataset was primarily collected through web scraping, as simulating a real-world garbage collection scenario (such as placing a camera above a conveyor belt) was not feasible at the time of collection. The goal was to obtain images that closely resemble actual garbage. For example, images in the biological waste category include rotten fruits, vegetables, and food remnants. Similarly, categories such as glass and metal consist of images of bottles, cans, and containers typically found in household trash. While the images for some categories, like clothes or shoes, were harder to find specifically as garbage, they still represent the items that may end up in waste streams.

In an ideal setting, a conveyor system could be used to gather real-time data by capturing images of waste in a continuous flow. Such a setup would enhance the dataset by providing authentic waste images for all categories. However, until that setup is available, this dataset serves as a significant step toward automating garbage classification and improving recycling technologies.

Potential for Future Improvements

While this dataset provides a strong foundation for household waste classification, there is potential for further improvements. For example, real-time data collection using conveyor systems or garbage processing plants could provide higher accuracy and more contextual images. Additionally, future datasets could expand to include more specialized categories, such as electronic waste, hazardous materials, or specific types of plastic.

Conclusion

The Garbage Classification dataset offers a broad and diverse collection of household waste images, making it a valuable resource for researchers and developers working in environmental sustainability, machine learning, and recycling automation. By improving the accuracy of waste classification systems, we can contribute to a cleaner, more sustainable future.

This dataset is sourced from Kaggle.

This dataset contains images of 7 waste materials, and the goal is to classify them into different categories. Here’s a brief description of each class:

Cardboard: Images of cardboard materials, such as packaging boxes, cartons, and paperboard. Compost: Images of organic waste that can be composted, including food scraps, plant matter, and biodegradable materials. Glass: Images of glass containers, bottles, and other glass waste. Metal: Images of metallic waste, such as aluminum cans, steel containers, and other metal objects. Paper: Images of paper waste, including newspapers, magazines, office paper, and cardboard. Plastic: Images of plastic materials, such as plastic bottles, bags, and containers. Trash: Images of miscellaneous waste that doesn’t fit into the other categories.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12145656%2F7096297ab054f505f4d788b86545ce5f%2F42979_2023_1706_Fig1_HTML.png?generation=1709547095589432&alt=media" alt=""> The dataset provides an opportunity to build a deep learning model that can automatically classify waste materials, contributing to better waste management and recycling efforts. You can explore the dataset, preprocess the images, and train a neural network to achieve accurate classification results.

This dataset was created by cropping and resizing images from FloodNet Challenge Aerial Imagery Dataset. The dataset is for Multiclass Semantic Segmentation. There are 398 images, which can be divided into train, validation and testing by the user. The original image size was 4000x3000. The last 1000 pixels along width were cropped away and then the square images were resized to 512x512. The dataset has following 10 classes: 0=Background 1=Building Flooded 2=Building Non-Flooded 3=Road Flooded 4=Road Non-Flooded 5=Water 6=Tree 7=Vehicle 8=Pool 9=Grass The images and masks are RGB .png images. In the masks, the pixel value (5,5,5) will correspond to label 5.

This dataset was developed during experimentation for doing semantic segmentation on partially annotated training data. So, along with normal masks, it also includes two other types of masks in two folders that could be used for those experiments. mask_partial folder includes partial masks created by randomly sampling 3% pixels and marking others as unlabeled. The unlabeled pixels have value of 255. The mask_sam folder includes pseudo label masks generated by using SAM 2.1 model to segment image, and then for each segmented region, assigning the label of most common class in labelled pixels inside it, to the whole region.

Overview

The object detection process required an annotated pizza dataset sourced from a Kaggle repository [1], comprising approximately 9000 pizza images capturing diverse visual conditions and angles. Around 1500 images were randomly selected and meticulously annotated with 16 ingredient labels, encompassing common pizza components like Cheese, Pepperoni, and Basil. Utilizing RoboFlow, a versatile dataset creation tool, facilitated label management, bounding box creation, and image sorting, streamlining the annotation process. The dataset was split into training, validation, and testing subsets (60%, 20%, and 20% respectively), ensuring a comprehensive evaluation. Augmentations like rotation and blur, applied exclusively to the training set, increased its size to 2544 images, while the validation and testing sets contained 284 and 283 images respectively. This dataset underwent extensive preparation and augmentation, laying the groundwork for subsequent model training and evaluation phases. RoboFlow's visual aids provided valuable insights into dataset characteristics, including label representation and object placement within images.

The following is a list of classes used for annotation:

1. Arugula
2. Bacon
3. Basil
4. Broccoli
5. Cheese
6. Chicken
7. Corn
8. Ham
9. Mushroom
10. Olives
11. Onion
12. Pepperoni
13. Peppers
14. Pineapple
15. Pizza
16. Tomatoes

[1] M. Bryant, Pizza images with topping labels, https://www.kaggle.com/datasets/michaelbryantds/pizza-images-with-topping- labels/, Jun. 2019.

ENSeg Dataset Overview This dataset represents an enhanced subset of the ENS dataset. The ENS dataset comprises image samples extracted from the enteric nervous system (ENS) of male adult Wistar rats (Rattus norvegicus, albius variety), specifically from the jejunum, the second segment of the small intestine.

The original dataset consists of two classes: - Control (C): Healthy animals. - Walker-256 Tumor (WT): Animals with cancer induced by the Walker-256 Tumor.

Image acquisition involved 13 different animals, with 7 belonging to the C class and 6 to the WT class. Each animal contributed 32 image samples obtained from the myenteric plexus. All images were captured using the same setup and configuration, stored in PNG format, with a spatial resolution of 1384 × 1036 pixels. The overall process of obtaining the images and performing the morphometric and quantitative analyses takes approximately 5 months.

Dataset Annotations Our dataset version includes expert-annotated labels for 6 animals, tagged as 2C, 4C, 5C, 22WT, 23WT, and 28WT. The image labels were created by members of the same laboratory where the images originated: researchers from the Enteric Neural Plasticity Laboratory of the State University of Maringá (UEM).

Annotations were generated using LabelMe and consist of polygons marking each neuron cell. To maintain labeling quality according to laboratory standards, only neuron cells with well-defined borders were included in the final label masks. The labeling process lasted 9 months (from November 2023 to July 2024) and was iteratively reviewed by the lead researcher of the lab.

Dataset Statistics After processing, the full dataset contains: - 187 images - 9,709 annotated neuron cells

The table below summarizes the number of images and annotated neurons per animal tag.

Recommended Training Methodology Due to the limited number of animal samples and the natural split of images, we recommend using a leave-one-out cross-validation (LOO-CV) method for training. This ensures more reliable results by performing six training sessions per experiment, where: - In each session, images from one subject are isolated for testing. - The remaining images are used for training.

Although cross-validation is recommended, it is not mandatory. Future works may introduce new training methodologies as the number of annotated subjects increases. However, it is crucial to maintain images from the same source (animal) within the same data split to prevent biased results. Even though samples are randomly selected from the animals' original tissues, this approach enhances the credibility of the findings.

This dataset provides a valuable resource for instance segmentation and biomedical image analysis, supporting research on ENS morphology and cancer effects. Contributions and feedback are welcome!

Project Citation If you want to cite our article, the dataset, or the source codes contained in this repository, please used the citation (bibtex format):

@Article{felipe25enseg, AUTHOR = { Felipe, Gustavo Zanoni and Nanni, Loris and Garcia, Isadora Goulart and Zanoni, Jacqueline Nelisis and Costa, Yandre Maldonado e Gomes da}, TITLE = {ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images}, JOURNAL = {Applied Sciences}, VOLUME = {15}, YEAR = {2025}, NUMBER = {3}, ARTICLE-NUMBER = {1046}, URL = {https://www.mdpi.com/2076-3417/15/3/1046}, ISSN = {2076-3417}, DOI = {10.3390/app15031046} }

Additional Notes Please check out our previous datasets if you are interest into developing projects with Enteric Nervous System images: 1. EGC-Z: three datasets of Enteric Glial cells images, composed of three different chronic degenerative diseases: Cancer, Diabetes Mellitus, and Rheumatoid Arthritis. Each dataset represent binary classification task, with the classes: control (healthy) and sick; 2. ENS: the ENS image datasets comprises 1248 images taken from thirteen rats distributed in two classes: control/healthy or sick. The images were created with three distinct contrast settings targeting different Enteric Nervous System cells: Enteric Neuron cells, Enteric Glial cells, or both.

For more details, please contact the main author of this project or create an issue on the project.

SIMAS Dataset

Taxonomy for MMS Visual Spam

The following table presents the definitions of categories used for classifying MMS images.

Table 1: Category definitions

Note: Categories marked with an asterisk are regulated in some jurisdictions and may not be universally restricted.

Dataset Collection and Annotation

Data Sources

The SIMAS dataset combines publicly available images from multiple sources, selected to reflect the categories defined in our content taxonomy. Each image was manually reviewed by three independent annotators, with final labels assigned when at least two annotators agreed.

The largest portion of the dataset (30.4%) originates from LAION-400M, a large-scale image-text dataset. To identify relevant content, we first selected a list of ImageNet labels that semantically matched our taxonomy. These labels were generated using GPT-4o in a zero-shot setting, using separate prompts per category. This resulted in 194 candidate labels, of which 88.7% were retained after manual review. The structure of the prompts used in this process is shown in the file gpt4o_imagenet_prompting_scheme.png, which illustrates a shared base prompt template applied across all categories. The fields category_definition, file_examples, and exceptions are specified per category. Definitions align with the taxonomy, while the file_examples column includes sample labels retrieved from the ImageNet label list. The exceptions field contains category-specific filtering instructions; a dash indicates no exceptions were specified.

Another 25.1% of images were sourced from Roboflow, using open datasets such as:

• Marijuana and Hemp 200
• Drug Detection
• Plants Classification
• Weapon Detection
• Suicide Detection
• Violence Detection
• Clasificacionimagenes
• Waste Recognition
• FYP

The NudeNet dataset contributes 11.4% of the dataset. We sampled 1,000 images from the “porn” category to provide visual coverage of explicit sexual content.

Another 11.0% of images were collected from Kaggle, including:

• National Flowers
• Weapon Dataset for YOLOv5
• GUIE Toys
• Alcohol Bottle Images
• Smoking & Drinking Dataset

An additional 9.9% of images were retrieved from Unsplash, using keyword-based search queries aligned with each category in our taxonomy.

Images from UnsafeBench make up 8.0% of the dataset. Since its original binary labels did not match our taxonomy, all samples were manually reassigned to the most appropriate category.

Finally, 4.2% of images were gathered from various publicly accessible websites. These were primarily used to improve category balance and model generalization, especially in safe classes.

All images collected from the listed sources have been manually reviewed by three independent annotators. Each image is then assigned to a category when at least two annotators reach consensus.

Table 2: Distribution of images per public source and category in SIMAS dataset

Balancing

To ensure semantic diversity and dataset balance, undersampling was performed on overrepresented categories using a CLIP-based embedding and k-means clustering strategy. This resulted in a final dataset containing 2,500 spam and 2,500 safe images, evenly distributed across all categories.

Table 3: Distribution of images per category in SIMAS

Overview

Frequent, and increasingly severe, natural disasters threaten human health, infrastructure, and natural systems. The provision of accurate, timely, and understandable information has the potential to revolutionize disaster management. For quick response and recovery on a large scale, after a natural disaster such as a hurricane, access to aerial images is critically important for the response team. The emergence of small unmanned aerial systems (UAS) along with inexpensive sensors presents the opportunity to collect thousands of images after each natural disaster with high flexibility and easy maneuverability for rapid response and recovery. Moreover, UAS can access hard-to-reach areas and perform data collection tasks that can be unsafe for humans if not impossible. Despite all these advancements and efforts to collect such large datasets, analyzing them and extracting meaningful information remains a significant challenge in scientific communities.

FloodNet provides high-resolution UAS imageries with detailed semantic annotation regarding the damages. To advance the damage assessment process for post-disaster scenarios, we present a unique challenge considering classification, semantic segmentation, visual question answering highlighting the UAS imagery-based FloodNet dataset.

Track 1: Image Classification and Semantic Segmentation Track 2: Visual Question Answering

Dataset Details

The data is collected with a small UAS platform, DJI Mavic Pro quadcopters, after Hurricane Harvey. The whole dataset has 2343 images, divided into training (~60%), validation (~20%), and test (~20%) sets.

For Track 1 ( Semi-supervised Classification and Semantic Segmentation), in the training set, we have around 400 labeled images (~25% of the training set) and around 1050 unlabeled images (~75% of the training set ).

For Track 2 ( Supervised VQA), in the training set we have around 1450 images and there are a total 4511 image-question pairs.

Track 1

In this track, participants are required to complete two semi-supervised tasks. The first task is image classification, and the second task is semantic segmentation.

Semi-Supervised Classification: Classification for FloodNet dataset requires classifying the images into ‘Flooded’ and ‘Non-Flooded’ classes. Only a few of the training images have their labels available, while most of the training images are unlabeled.

Semi-Supervised Semantic Segmentation: The semantic segmentation labels include:

0 - Background

1 - Building Flooded

2 - Building Non-Flooded

3 - Road Flooded

4 - Road Non-Flooded

5 - Water

6 - Tree

7 - Vehicle

8 - Pool

9 - Grass.

Only a small portion of the training images have their corresponding masks available.

Track 2

For the Visual Question Answering (VQA) task, we provide images associated with multiple questions. These questions will be divided into the following categories:

• Simple Counting: Questions will be designed to count the number of objects regardless of their attribute. For example: “how many buildings are there in the image?”.
• Complex Counting: Questions will be asked to count the number of objects belonging to a specific attribute. For example: “how many flooded buildings are there in the image?”.
• Condition Recognition: In this category, all the questions are mainly designed to ask questions regarding the condition of the object and the neighborhood. For example: “What is the condition of the road in the given image?”.
• Yes/No type of question: For this type of question, the answer will be either a ‘Yes’ or a ‘No’. For example: “Is there any flooded road?”.

Citation

@article{rahnemoonfar2020floodnet,
 title={FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding},
 author={Rahnemoonfar, Maryam and Chowdhury, Tashnim and Sarkar, Argho and Varshney, Debvrat and Yari, Masoud and Murphy, Robin},
 journal={arXiv preprint arXiv:2012.02951},
 year={2020}
}

The dataset contain a total of 28,000 RGB images belonging to a total of 28 classes. The data was collected from around 50 participants from Zagazig university. The ages of participants are ranging from 15 to 35 years. The images was captured by two mobile phones namely Realme 6, Realme 7, Realme 8. The images were resized into size of 224*224.

The inital version of data is available privately on our page: https://www.kaggle.com/datasets/deepologylab/esl-net

THE MNIST DATABASE of handwritten digits

Authors:

• Yann LeCun, Courant Institute, NYU
• Corinna Cortes, Google Labs, New York
• Christopher J.C. Burges, Microsoft Research, Redmond

Dataset Obtained From: http://yann.lecun.com/exdb/mnist/

All images were sized 28x28 in the original dataset

The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.

It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.

Version 1 (original-images_trainSetSplitBy80_20):

• Original, raw images, with the train set split to provide 80% of its images to the training set and 20% of its images to the validation set
• Trained from Roboflow Classification Model's ImageNet training checkpoint

Version 2 (original-images_ModifiedClasses_trainSetSplitBy80_20):

• Original, raw images, with the train set split to provide 80% of its images to the training set and 20% of its images to the validation set
• Modify Classes, a Roboflow preprocessing feature, was employed to change class names from 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 to one, two, three, four, five, six, seven, eight, nine
• Trained from the Roboflow Classification Model's ImageNet training checkpoint

Version 3 (original-images_Original-MNIST-Splits):

• Original images, with the original splits for MNIST: train (86% of images - 60,000 images) set and test (14% of images - 10,000 images) set only.
• This version was not trained

Citation:

@article{lecun2010mnist,
 title={MNIST handwritten digit database},
 author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
 journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
 volume={2},
 year={2010}
}

Fer2013 contains approximately 30,000 facial RGB images of different expressions with size restricted to 48×48, and the main labels of it can be divided into 7 types: 0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral. The Disgust expression has the minimal number of images – 600, while other labels have nearly 5,000 samples each.

HiXray is a High-quality X-ray security inspection image dataset, which contains 102,928 common prohibited items of 8 categories. It has been gathered from the real-world airport security inspection and annotated by professional security inspectors

The SD-198 dataset contains 198 different diseases from different types of eczema, acne and various cancerous conditions. There are 6,584 images in total. A subset include the classes with more than 20 image samples, namely SD-128."

The minimalist histopathology image analysis dataset (MHIST) is a binary classification dataset of 3,152 fixed-size images of colorectal polyps, each with a gold-standard label determined by the majority vote of seven board-certified gastrointestinal pathologists. MHIST also includes each image’s annotator agreement level. As a minimalist dataset, MHIST occupies less than 400 MB of disk space, and a ResNet-18 baseline can be trained to convergence on MHIST in just 6 minutes using approximately 3.5 GB of memory on a NVIDIA RTX 3090. As example use cases, the authors use MHIST to study natural questions that arise in histopathology image classification such as how dataset size, network depth, transfer learning, and high-disagreement examples affect model performance.

Dataset

This dataset was created by Timo Bozsolik

Released under MIT

Contents

This dataset is collected from Kaggle ( https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset ). This dataset is a combination of the following three datasets :figshareSARTAJ datasetBr35H