GUI-net-1m: Large-Scale Image Dataset
GUI-net-1m is a vast open-source image dataset, with nearly 900,000 files divided into several parts. A script is provided to merge and extract these images, making it easy to use them in computer vision projects.
Approximately 895,000 images divided into several compressed archives, requiring a script for extraction and fusion
Apache 2.0
Description
The dataset Gui-net-1m contains nearly 895,000 images extracted from a large visual corpus. The images are stored in several compressed files, requiring a merge and decompression script to reconstruct the complete set. This dataset is designed for large-scale computer vision applications, including training recognition models, classification, or image generation.
What is this dataset for?
- Train deep learning models for large-scale computer vision.
- Test the robustness and generalization capacity of image recognition algorithms.
- Experiment with the management and processing of very large volumes of images in automated pipelines.
Can it be enriched or improved?
This dataset can be enriched by adding specific annotations (labels, bounding boxes), or by selecting subsets dedicated to specific tasks (detection, segmentation). Improving the extraction script can also make it easier to integrate into different environments.
🔎 In summary
🧠 Recommended for
- Computer vision researchers
- Deep learning engineers
- Large-scale AI projects
🔧 Compatible tools
- PyTorch
- TensorFlow
- Bash scripts for extraction
- Big data management tools
💡 Tip
Use the script provided to automate merging and extraction in order to avoid errors and optimize processing.
Frequently Asked Questions
How do I extract and use images spread across multiple archives?
A bash script is provided to merge the split files and unpack them into a single folder, making them easy to access and use.
Does this dataset contain annotations for images?
No, GUI-net-1m is only a set of raw images with no associated annotations.
Is it a dataset suitable for computer vision beginners?
No, it requires a good command of tools and scripts to extract and process large volumes of images.
