datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
LuminBench-Weakly-Supervised-CellSeg
LuminBench: Weakly Supervised Cell Segmentation
A checksum-bound training and validation package for agent-driven research with two labelled images per group. The scientific protocol and runnable code are maintained in LuminBench-Weakly-Supervised-CellSeg. The project uses the LB-Template organization for ordinary experiments and agent research.
Split
Images
Masks
Role
training_labelled
24
24
Two human-labelled examples in each of 12 groups
training_unlabelled
967
0… See the full description on the dataset page: https://huggingface.co/datasets/LuminScience/LuminBench-Weakly-Supervised-CellSeg.supervised-and-blurred-rotated-shrunk-NoRevsSupervised-Fog-Removal-DatasetSupervised Fog Removal Dataset
Overview
This dataset contains 80,000 paired images designed for supervised image dehazing / fog removal tasks.
Each sample consists of:
a clean image (ground truth)
a synthetically fogged version of that image
The fog is generated using a physics-inspired atmospheric scattering model combined with depth estimation, allowing the fog to behave realistically with respect to scene geometry.
Unlike simple uniform haze overlays, this dataset simulates depth-aware fog… See the full description on the dataset page: https://huggingface.co/datasets/Aeye-coder/Supervised-Fog-Removal-Dataset.bowman_prospects_supervised_imagesBowman Prospects Baseball Card Dataset labeled by rarity.
All Images were fetched from ebay.
Example setup
from datasets import load_dataset
dataset = load_dataset("hazelbestt/bowman_prospects_supervised_images")["train"]
# Split the data manually
split = dataset.train_test_split(test_size=0.2, seed=42)
train_ds = split["train"]
val_ds = split["test"]
supervised-training-revssupervised-and-blurred-rotated