rf100vl

library1.1.2pypypi✓ verified 85d ago

`rf100vl` is a Python library that provides a convenient interface for the RF100-VL dataset, specifically designed for research in multi-modal learning and understanding. It handles the downloading, caching, and access of the dataset's image-caption pairs, allowing users to easily integrate it into their machine learning pipelines. The current stable version is 1.1.0, and the project appears to be in maintenance with occasional minor updates.

ai-ml data

pip install rf100vl

package ↗

INSTALL

IMPORT

SIG · RF100VL

rf100vl

ai-mlpythonv1.1.2

Install

11.5s avg

Import

—

Disk

350MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.1.2 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.910 runs

build_error

glibc

py 3.10–3.910 runs

installs and imports cleanly · install 11.5s · import 0.000s · 346MB

350MB installed

● package 350MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

RF100VL

✓ from rf100vl import roboflow100vl

✗ from rf100vl.rf100vl import RF100VL

download_rf100vl

✓ from rf100vl import download_rf100vl

get_rf100vl_projects

✓ from rf100vl import get_rf100vl_projects

This quickstart demonstrates how to initialize the `RF100VL` dataset, automatically downloading it to a specified `root_dir` if it's not already present. It then shows how to access an individual item, which provides a PIL Image and its corresponding text caption.

import os from rf100vl.rf100vl import RF100VL # Define a directory for the dataset; it will be created if it doesn't exist. # Using an environment variable or a default path for flexibility. data_root = os.environ.get('RF100VL_DATA_ROOT', './rf100vl_data') os.makedirs(data_root, exist_ok=True) try: # Initialize the dataset. Set download=True to fetch if not present. # This can take significant time and disk space. dataset = RF100VL(root_dir=data_root, split='train', download=True) print(f"\nSuccessfully loaded RF100VL dataset with {len(dataset)} items in '{data_root}'.") # Access a sample item (e.g., the first one) sample_item = dataset[0] image = sample_item['image'] # A PIL Image object caption = sample_item['caption'] # A string caption print(f"\nFirst item details:") print(f" Caption: '{caption[:100]}...' ") print(f" Image type: {type(image)}, size: {image.size}, mode: {image.mode}") # Further processing (e.g., transforming image, tokenizing caption) would go here. except Exception as e: print(f"\nAn error occurred during dataset initialization or access: {e}") print("Please ensure you have network access, sufficient disk space, and correct permissions for the data_root directory.")

Debug

Known issues

gotchaThe RF100-VL dataset is substantial in size (multiple gigabytes). Ensure your system has sufficient free disk space and a stable, high-bandwidth internet connection before attempting the initial download. The download process can be lengthy.

fix

Verify available disk space and network stability. The `tqdm` progress bar will indicate download status, but be prepared for a long wait.

affects: All

gotchaThe primary class `RF100VL` is located within the `rf100vl.rf100vl` module, not directly under the `rf100vl` package. A common mistake is to omit the inner `rf100vl` in the import path, leading to an `ImportError`.

fix

Always use `from rf100vl.rf100vl import RF100VL` for correct importation.

affects: All

gotchaThe `root_dir` parameter for `RF100VL` specifies where the dataset files are stored. If this directory does not exist, the library might raise a `FileNotFoundError` or attempt to create it without proper permissions, leading to issues. Subsequent file access will also fail if the path is invalid.

fix

Always ensure the `root_dir` exists by calling `os.makedirs(root_dir, exist_ok=True)` before initializing the `RF100VL` class.

affects: All

Upgrade

Version history

1.1.2latest on PyPI · released May 23, 2026

Audit

Dependencies

requestsrequiredHandles secure downloading of dataset files from remote servers.

numpyrequiredUsed for numerical operations, common in data processing tasks.

tqdmrequiredProvides progress bars for dataset download and processing, enhancing user experience.

Agent activity

5 hits · last 30 days

node

OpenAI (training)

Resources

packagehttps://pypi.org/project/rf100vl/ ↗