CoolFace
18 results

hardness

Kimhi /hardness_data_mix Hardness Data Mix - Resolution Sufficiency Dataset A large-scale dataset of document images with labels indicating the minimum resolution required to accurately answer questions about those documents. Dataset Description This dataset contains 81,924 document image-question pairs labeled with resolution sufficiency information. Each sample is annotated with a "hardness" label indicating the minimum resolution level needed to answer questions about that document accurately.… See the full description on the dataset page: https://huggingface.co/datasets/Kimhi/hardness_data_mix.tabularimage-classification10K<n<100K2 likes116 downloads6mo agoHugging FaceAI4Manufacturing /AM11_DED316L_texture_hardness_agent_onlygated Dataset Card Dataset Description [Placeholder] Dataset Structure [Placeholder] Uses [Placeholder] Limitations [Placeholder] License [Placeholder] Citation [Placeholder] textn<1K0 likes58 downloads12h agoHugging FaceAI4Manufacturing /AM11_DED316L_texture_hardness_agent_only_deprecatedgated Dataset Card Dataset Description [Placeholder] Dataset Structure [Placeholder] Uses [Placeholder] Limitations [Placeholder] License [Placeholder] Citation [Placeholder] textn<1K0 likes54 downloads12h agoHugging Faceduyle2408 /levir-oacp-r2-hardness-adaptive-seed42-runs0 likes31 downloads11d agoHugging Facetapwaterdata /us-water-hardness US Water Hardness Dataset Measured drinking-water hardness for 16,561 US cities — mg/L as calcium carbonate and grains per gallon (gpg), each value carrying its source, tier, and sample date. Canonical, continuously-updated source: https://www.tapwaterdata.com/water-hardness GitHub mirror: https://github.com/Tapwaterdata/water-hardness-dataset DOI: 10.5281/zenodo.21315903 Files water-hardness.csv — 16,561 rows, 17-column header. water-hardness.json — same 17… See the full description on the dataset page: https://huggingface.co/datasets/tapwaterdata/us-water-hardness.textn<1K0 likes17 downloads3mo agoHugging Facequinnlue /audioset24k-mae-hardness AudioSet-Opus MAE reconstruction hardness Per-clip MAE reconstruction loss over danjacobellis/audioset_opus_24kbps, computed with a frozen dasheng-base encoder + trained MAE decoder. Loss is the per-patch normalised reconstruction MSE on masked, in-span patches, averaged over 4 fixed-seed masking passes. Higher loss = harder example. Columns: id, path, loss, loss_all_patches, n_valid_patches, duration_s, labels, rank (1 = hardest). tabular1M<n<10M0 likes10 downloads3mo agoHugging Face