# Condensed: AutoMM Detection - Prepare COCO2017 Dataset

Summary: This tutorial provides implementation guidance for preparing the COCO2017 dataset specifically for AutoMM Detection tasks. It covers two main implementation approaches: using Python CLI commands or bash scripts for dataset installation, with options for custom output paths. The tutorial helps with dataset setup tasks, detailing the required 42.7GB storage, installation commands, and resulting directory structure. Key features include handling COCO2017's 80 classes and 886,284 instances, maintaining COCO format compatibility with AutoGluon MultiModalPredictor, and best practices for storage and data organization. It's particularly useful for setting up object detection datasets in a standardized format.

*This is a condensed version that preserves essential implementation details and context.*

Here's the condensed tutorial focusing on essential implementation details:

# COCO2017 Dataset Preparation for AutoMM Detection

## Key Requirements
- Disk space: 42.7 GB
- Recommended: SSD over HDD
- Typical setup time: ~20 min on AWS EC2 with EBS

## Installation Methods

### 1. Python Script Method
```bash
# Basic installation in current directory
python3 -m autogluon.multimodal.cli.prepare_detection_dataset --dataset_name coco2017

# Installation with custom output path
python3 -m autogluon.multimodal.cli.prepare_detection_dataset --dataset_name coco2017 --output_path ~/data

# Short form
python3 -m autogluon.multimodal.cli.prepare_detection_dataset -d coco17 -o ~/data
```

### 2. Bash Script Method
```bash
# Basic installation
bash download_coco17.sh

# Installation with custom path
bash download_coco17.sh ~/data
```

## Dataset Structure
After installation, the `coco17` folder contains:
```
annotations/
test2017/
train2017/
unlabeled2017/
val2017/
```

## Important Notes
- COCO2017 contains:
  - 80 classes
  - 123,287 images
  - 886,284 instances
  - Median image ratio: 640 x 480
- The COCO format (.json) is recommended for AutoGluon MultiModalPredictor
- For format conversion:
  - Refer to "Convert Data to COCO Format" tutorial
  - See "AutoMM Detection - Convert VOC Format Dataset to COCO Format"

## Best Practices
1. Use SSD for better performance
2. Follow COCO format for data organization
3. Check available disk space before installation
4. Consider using bash script on Unix systems for progress tracking

For customization details, refer to the "Customize AutoMM" documentation.