---
title: "RF100 Dataset Catalog"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{RF100 Dataset Catalog}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Overview

The RoboFlow 100 (RF100) benchmark consists of 34 diverse object detection datasets organized into 6 collections. This vignette provides a comprehensive catalog to help you find the right dataset for your task.

The RF100 datasets cover a wide range of domains including:

- **Biology**: Microscopy, cells, bacteria, parasites (9 datasets)
- **Medical**: X-rays, MRI, pathology (8 datasets)  
- **Infrared**: Thermal imaging, FLIR cameras (4 datasets)
- **Damage**: Defect detection, infrastructure inspection (3 datasets)
- **Underwater**: Marine life, coral, infrastructure (4 datasets)
- **Document**: OCR, document parsing, diagrams (6 datasets)

## Quick Search

The easiest way to find datasets is using the search functions:

```{r eval=FALSE}
library(torchvision)

# Search for specific topics
search_rf100("cell")        # Find cell-related datasets
search_rf100("solar")       # Find solar panel datasets
search_rf100("x-ray")       # Find X-ray datasets

# List all datasets in a collection
search_rf100(collection = "biology")
search_rf100(collection = "medical")

# View complete catalog
catalog <- get_rf100_catalog()
View(catalog)
```

## Example: Finding a Photovoltaic Dataset

One of the motivations for this catalog was answering questions like: *"Is there a photovoltaic dataset in torchvision?"*

```{r eval=FALSE}
# Search for solar/photovoltaic datasets
search_rf100("solar")
search_rf100("photovoltaic")

# Result shows:
# - solar_panel in infrared collection
# - solar_panel in damage collection
```

## Complete Catalog

Here's the complete catalog of all RF100 datasets:

```{r eval=FALSE}
library(torchvision)
library(knitr)

catalog <- get_rf100_catalog()

# Display key columns
kable(catalog[, c("collection", "dataset", "description", "total_size_mb", "estimated_images")])
```

## Collections

### Biology Collection (9 datasets)

Microscopy and biological imaging datasets for research and diagnostics:

```{r eval=FALSE}
search_rf100(collection = "biology")
```

**Available datasets:**

- `stomata_cell`: Plant stomata cells for biology research
- `blood_cell`: Blood cell detection (RBC, WBC, platelets)
- `parasite`: Parasite detection in microscopy images
- `cell`: General cell detection in microscopy
- `bacteria`: Bacteria detection in microscopy images
- `cotton_disease`: Cotton plant disease detection
- `mitosis`: Mitosis phase detection in cell images
- `phage`: Bacteriophage detection in microscopy
- `liver_disease`: Liver disease pathology detection

### Medical Collection (8 datasets)

Medical imaging datasets for clinical and research applications:

```{r eval=FALSE}
search_rf100(collection = "medical")
```

**Available datasets:**

- `radio_signal`: Radio signal detection in medical imaging
- `rheumatology`: Rheumatology X-ray abnormality detection
- `knee`: ACL and knee X-ray analysis
- `abdomen_mri`: Abdomen MRI organ detection
- `brain_axial_mri`: Brain axial MRI structure detection
- `gynecology_mri`: Gynecology MRI structure detection
- `brain_tumor`: Brain tumor detection in MRI scans
- `fracture`: Bone fracture detection in X-rays

### Infrared Collection (4 datasets)

Thermal and infrared imaging datasets:

```{r eval=FALSE}
search_rf100(collection = "infrared")
```

**Available datasets:**

- `thermal_dog_and_people`: Thermal imaging of dogs and people
- `solar_panel`: Solar panel detection in infrared imagery
- `thermal_cheetah`: Thermal imaging of cheetahs
- `ir_object`: FLIR camera object detection

### Damage Collection (3 datasets)

Infrastructure damage and defect detection:

```{r eval=FALSE}
search_rf100(collection = "damage")
```

**Available datasets:**

- `liquid_crystals`: 4-fold defect detection in LCD displays
- `solar_panel`: Solar panel defect and damage detection
- `asbestos`: Asbestos detection for safety inspection

### Underwater Collection (4 datasets)

Marine and underwater imaging datasets:

```{r eval=FALSE}
search_rf100(collection = "underwater")
```

**Available datasets:**

- `pipes`: Underwater pipe detection for infrastructure
- `aquarium`: Aquarium fish and species detection
- `objects`: Underwater object detection
- `coral`: Coral reef detection and monitoring

### Document Collection (6 datasets)

Document analysis and OCR datasets:

```{r eval=FALSE}
search_rf100(collection = "document")
```

**Available datasets:**

- `tweeter_post`: Twitter post element detection
- `tweeter_profile`: Twitter profile element detection
- `document_part`: Document structure and part detection
- `activity_diagram`: Activity diagram element detection
- `signature`: Signature detection in documents
- `paper_part`: Academic paper structure detection

## Usage Example

Once you've found a dataset, loading it is straightforward:

```{r eval=FALSE}
library(torchvision)

# Search for blood cell dataset
search_rf100("blood")

# Load the dataset
ds <- rf100_biology_collection(
  dataset = "blood_cell",
  split = "train",
  download = TRUE
)

# Inspect a sample
item <- ds[1]
print(item$y$labels)  # Object classes
print(item$y$boxes)   # Bounding boxes

# Visualize with bounding boxes
boxed <- draw_bounding_boxes(item)
tensor_image_browse(boxed)
```

## Dataset Statistics

```{r eval=FALSE}
catalog <- get_rf100_catalog()

# Total size of all datasets
sum(catalog$total_size_mb) / 1024  # In GB

# Datasets by size
catalog[order(-catalog$total_size_mb), c("dataset", "collection", "total_size_mb")]

# Smallest and largest datasets
catalog[which.min(catalog$total_size_mb), ]
catalog[which.max(catalog$total_size_mb), ]

# Average size by collection
aggregate(total_size_mb ~ collection, data = catalog, FUN = mean)
```

## Filtering and Exploration

The catalog is a regular data frame, so you can use standard R operations:

```{r eval=FALSE}
# Find small datasets (< 20 MB total)
subset(catalog, total_size_mb < 20)

# Find large datasets (> 200 MB total)
subset(catalog, total_size_mb > 200)

# Find datasets with specific keywords
subset(catalog, grepl("tumor|cancer|disease", description, ignore.case = TRUE))

# Datasets with all three splits
subset(catalog, has_train & has_test & has_valid)
```

## Additional Resources

- **RoboFlow Universe**: Browse datasets at https://universe.roboflow.com/browse/
- **Collection Functions**: See `?rf100_biology_collection`, `?rf100_medical_collection`, etc.
- **Visualization**: See `?draw_bounding_boxes` for visualizing detections

## Citation

If you use RF100 datasets in your research, please cite:

```
@article{roboflow100,
  title={Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark},
  author={Roboflow},
  journal={arXiv preprint},
  year={2022}
}
```