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\"trpakov/chest-xray-classification\"
### Dataset Labels ``` ['PNEUMONIA', 'NORMAL'] ``` ### Number of Images ```json {'test': 582, '…", "source_url": "https://huggingface.co/datasets/trpakov/chest-xray-classification" } ], "n_verified": 11, "n_mirrored": 0, "n_metrics": 1, "top": { "name": "lxyuan/vit-xray-pneumonia-classification", "score": 0.9673539518900344, "metric": "top_1_accuracy" }, "updated_at": "2026-06-09T20:39:24.105036" }, { "id": 139, "name": "COPA-validation_benchmark", "category": "NLP/Commonsense Reasoning", "url": "https://runbenchhub.com/leaderboard/139", "submit_url": "https://runbenchhub.com/leaderboard/139?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "COPA-validation", "description": "COPA (Roemmele et al., 2011; SuperGLUE) — Choice of Plausible Alternatives, 2-option causal commonsense reasoning (validation). 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[Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset…", "source_url": "https://huggingface.co/datasets/AI-Lab-Makerere/beans" } ], "n_verified": 15, "n_mirrored": 0, "n_metrics": 1, "top": { "name": "platzi/platzi-vit-base-beans-omar-espejel", "score": 0.984375, "metric": "top_1_accuracy" }, "updated_at": "2026-06-09T19:22:29.664290" }, { "id": 66, "name": "Bingsu__Cat_and_Dog_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/66", "submit_url": "https://runbenchhub.com/leaderboard/66?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "Bingsu__Cat_and_Dog", "description": "## Dataset Description - **Homepage:** [Cat and Dog](https://www.kaggle.com/datasets/tongpython/cat-and-dog) - **Download Size** 217.30 MiB - **Generated Size** 198.89 MiB - **Total Size** 416.20 MiB ### Dataset Summary A dataset from [kaggle](https://www.kaggle.com/datasets/tong…", "source_url": "https://huggingface.co/datasets/Bingsu/Cat_and_Dog" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 1, "top": { "name": "openai/clip-vit-large-patch14", "score": 0.9975, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T05:59:42.116936" }, { "id": 15, "name": "Bingsu__Human_Action_Recognition_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/15", "submit_url": "https://runbenchhub.com/leaderboard/15?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "Bingsu__Human_Action_Recognition", "description": "## Dataset Description - **Homepage:** [Human Action Recognition (HAR) Dataset](https://www.kaggle.com/datasets/meetnagadia/human-action-recognition-har-dataset) - **Repository:** N/A - **Paper:** N/A - **Leaderboard:** N/A - **Point of Contact:** N/A ## Dataset Summary A dataset…", "source_url": "https://huggingface.co/datasets/Bingsu/Human_Action_Recognition" } ], "n_verified": 7, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "rvv-karma/Human-Action-Recognition-VIT-Base-patch16-224", "score": 0.116, "metric": "top_1_accuracy" }, "updated_at": "2026-06-09T19:55:26.727735" }, { "id": 46, "name": "blanchon__EuroSAT_RGB_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/46", "submit_url": "https://runbenchhub.com/leaderboard/46?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "blanchon__EuroSAT_RGB", "description": "# EuroSAT RGB ![EuroSAT RGB](./thumbnail.jpg) EUROSAT RGB is the RGB version of the EUROSAT dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000…", "source_url": "https://huggingface.co/datasets/blanchon/EuroSAT_RGB" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "google/siglip-so400m-patch14-384", "score": 0.533, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T05:59:20.629427" }, { "id": 44, "name": "blanchon__PatternNet_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/44", "submit_url": "https://runbenchhub.com/leaderboard/44?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "blanchon__PatternNet", "description": "# PatternNet ![PatternNet](./thumbnail.jpg) The PatternNet dataset is a dataset for remote sensing scene classification and image retrieval. - 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[Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Dat…", "source_url": "https://huggingface.co/datasets/uoft-cs/cifar100" } ], "n_verified": 20, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "edadaltocg/vit_base_patch16_224_in21k_ft_cifar100", "score": 0.927, "metric": "top_1_accuracy" }, "updated_at": "2026-06-02T12:58:18.971424" }, { "id": 1, "name": "cifar10_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/1", "submit_url": "https://runbenchhub.com/leaderboard/1?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "cifar10", "description": "# Dataset Card for CIFAR-10 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - 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[Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Data…", "source_url": "https://huggingface.co/datasets/ethz/food101" } ], "n_verified": 24, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "google/siglip-so400m-patch14-384", "score": 0.9641980198019802, "metric": "top_1_accuracy" }, "updated_at": "2026-06-09T20:16:45.379044" }, { "id": 58, "name": "Hemg__Brain-Tumor-MRI-Dataset_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/58", "submit_url": "https://runbenchhub.com/leaderboard/58?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "Hemg__Brain-Tumor-MRI-Dataset", "description": "# Dataset Card for \"Brain-Tumor-MRI-Dataset\" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)", "source_url": "https://huggingface.co/datasets/Hemg/Brain-Tumor-MRI-Dataset" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 1, "top": { "name": "openai/clip-vit-large-patch14", "score": 0.294, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T05:59:31.876357" }, { "id": 62, "name": "Joker5800__Tobacco_Leaf_Diseases_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/62", "submit_url": "https://runbenchhub.com/leaderboard/62?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "Joker5800__Tobacco_Leaf_Diseases", "description": null, "source_url": "https://huggingface.co/datasets/Joker5800/Tobacco_Leaf_Diseases" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "google/siglip-so400m-patch14-384", "score": 0.15804597701149425, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T05:59:35.870534" }, { "id": 25, "name": "jonathan-roberts1__GID_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/25", "submit_url": "https://runbenchhub.com/leaderboard/25?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "jonathan-roberts1__GID", "description": "# Dataset Card for \"GID\" ## Dataset Description - **Paper** [Land-cover classification with high-resolution remote sensing images using transferable deep models](https://www.sciencedirect.com/science/article/pii/S0034425719303414) ### Licensing Information Public domain. ## Citat…", "source_url": "https://huggingface.co/datasets/jonathan-roberts1/GID" } ], "n_verified": 6, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "prithivMLmods/GiD-Land-Cover-Classification", "score": 0.88, "metric": "top_1_accuracy" }, "updated_at": "2026-06-09T20:07:11.601753" }, { "id": 24, "name": "jonathan-roberts1__NWPU-RESISC45_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/24", "submit_url": "https://runbenchhub.com/leaderboard/24?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "jonathan-roberts1__NWPU-RESISC45", "description": "# Dataset Card for \"NWPU-RESISC45\" ## Dataset Description - **Paper** [Remote sensing image scene classification: Benchmark and state of the art](https://ieeexplore.ieee.org/iel7/5/8045830/07891544.pdf) ### Licensing Information [CC-BY-SA] ## Citation Information [Remote sensing…", "source_url": "https://huggingface.co/datasets/jonathan-roberts1/NWPU-RESISC45" } ], "n_verified": 6, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "prithivMLmods/RESISC45-SigLIP2", "score": 0.95, "metric": "top_1_accuracy" }, "updated_at": "2026-06-09T20:04:44.827313" }, { "id": 56, "name": "jonathan-roberts1__Optimal-31_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/56", "submit_url": "https://runbenchhub.com/leaderboard/56?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "jonathan-roberts1__Optimal-31", "description": "# Dataset Card for \"Optimal-31\" ## Dataset Description - **Paper** [Scene classification with recurrent attention of VHR remote sensing images](https://ieeexplore.ieee.org/iel7/5/8045830/07891544.pdf) ### Licensing Information [No license for now, cite the paper below.] ## Citati…", "source_url": "https://huggingface.co/datasets/jonathan-roberts1/Optimal-31" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "google/siglip-so400m-patch14-384", "score": 0.7844086021505376, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T05:59:30.237066" }, { "id": 50, "name": "jonathan-roberts1__Satellite-Images-of-Hurricane-Damage_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/50", "submit_url": "https://runbenchhub.com/leaderboard/50?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "jonathan-roberts1__Satellite-Images-of-Hurricane-Damage", "description": "# Dataset Card for \"Satellite-Images-of-Hurricane-Damage\" ## Dataset Description - **Paper** [Deep learning based damage detection on post-hurricane satellite imagery](https://arxiv.org/pdf/1807.01688.pdf) - 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It is designed for training machine learning models to perform **age classification** from facial features. ## Dataset Details * **Total Images**: 9,165 * **I…", "source_url": "https://huggingface.co/datasets/prithivMLmods/Face-Age-10K" } ], "n_verified": 6, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "openai/clip-vit-base-patch16", "score": 0.5105, "metric": "top_1_accuracy" }, "updated_at": "2026-06-09T19:54:29.378486" }, { "id": 60, "name": "qingyuyang__Fetal_Planes_DB_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/60", "submit_url": "https://runbenchhub.com/leaderboard/60?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "qingyuyang__Fetal_Planes_DB", "description": null, "source_url": "https://huggingface.co/datasets/qingyuyang/Fetal_Planes_DB" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 1, "top": { "name": "openai/clip-vit-large-patch14", "score": 0.242, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T05:59:33.489140" }, { "id": 69, "name": "Rishi210904__Crop_Weed_classification_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/69", "submit_url": "https://runbenchhub.com/leaderboard/69?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "Rishi210904__Crop_Weed_classification", "description": "# Crop & Weed Classification Dataset © 2026 Rishi. All rights reserved. This dataset is provided under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. ### Usage You are free to share and adapt this dataset under the following terms: * **At…", "source_url": "https://huggingface.co/datasets/Rishi210904/Crop_Weed_classification" } ], "n_verified": 1, "n_mirrored": 0, "n_metrics": 1, "top": { "name": "openai/clip-vit-large-patch14", "score": 0.6378640776699029, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T05:59:44.534242" }, { "id": 64, "name": "rotemvahava__yoga-poses-107_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/64", "submit_url": "https://runbenchhub.com/leaderboard/64?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "rotemvahava__yoga-poses-107", "description": null, "source_url": "https://huggingface.co/datasets/rotemvahava/yoga-poses-107" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "google/siglip-so400m-patch14-384", "score": 0.574, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T05:59:39.737860" }, { "id": 8, "name": "sartajbhuvaji__Brain-Tumor-Classification_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/8", "submit_url": "https://runbenchhub.com/leaderboard/8?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "sartajbhuvaji__Brain-Tumor-Classification", "description": null, "source_url": "https://huggingface.co/datasets/sartajbhuvaji/Brain-Tumor-Classification" } ], "n_verified": 7, "n_mirrored": 0, "n_metrics": 1, "top": { "name": "NeuronZero/MRI-Reader", "score": 0.7690355329949239, "metric": "top_1_accuracy" }, "updated_at": "2026-06-09T19:53:06.967546" }, { "id": 67, "name": "Spitblaze__Corn_or_Maize_Leaf_Disease_Dataset_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/67", "submit_url": "https://runbenchhub.com/leaderboard/67?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "Spitblaze__Corn_or_Maize_Leaf_Disease_Dataset", "description": null, "source_url": "https://huggingface.co/datasets/Spitblaze/Corn_or_Maize_Leaf_Disease_Dataset" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 1, "top": { "name": "google/siglip-so400m-patch14-384", "score": 0.254, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T05:59:42.916572" }, { "id": 42, "name": "tanganke__dtd_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/42", "submit_url": "https://runbenchhub.com/leaderboard/42?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "tanganke__dtd", "description": "# [DTD: Describable Textures Dataset](https://www.robots.ox.ac.uk/~vgg/data/dtd/) The Describable Textures Dataset (DTD) is an evolving collection of textural images in the wild, annotated with a series of human-centric attributes, inspired by the perceptual properties of texture…", "source_url": "https://huggingface.co/datasets/tanganke/dtd" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "google/siglip-so400m-patch14-384", "score": 0.6861702127659575, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T05:59:16.694707" }, { "id": 6, "name": "tanganke__eurosat_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/6", "submit_url": "https://runbenchhub.com/leaderboard/6?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "tanganke__eurosat", "description": "# Dataset Card for EuroSAT # Dataset Source - [Paper with code](https://paperswithcode.com/dataset/eurosat) # Usage ```python from datasets import load_dataset dataset = load_dataset('tranganke/eurosat') ``` ### Data Fields The dataset contains the following fields: - `image`: An…", "source_url": "https://huggingface.co/datasets/tanganke/eurosat" } ], "n_verified": 6, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "openai/clip-vit-base-patch16", "score": 0.43555555555555553, "metric": "top_1_accuracy" }, "updated_at": "2026-06-09T19:52:47.968073" }, { "id": 79, "name": "tanganke__gtsrb_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/79", "submit_url": "https://runbenchhub.com/leaderboard/79?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "tanganke__gtsrb", "description": "# Dataset Card for German Traffic Sign Recognition Benchmark This dataset contains images of 43 classes of traffic signs. It is intended for developing and benchmarking traffic sign recognition systems. ## Dataset Details ### Dataset Description The German Traffic Sign Recognitio…", "source_url": "https://huggingface.co/datasets/tanganke/gtsrb" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "google/siglip-so400m-patch14-384", "score": 0.5435471100554236, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T17:46:43.349003" }, { "id": 57, "name": "tanganke__kmnist_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/57", "submit_url": "https://runbenchhub.com/leaderboard/57?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "tanganke__kmnist", "description": "# KMNIST Dataset lassify images from the KMNIST dataset into one of the 10 classes, representing different Japanese characters.", "source_url": "https://huggingface.co/datasets/tanganke/kmnist" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "openai/clip-vit-base-patch16", "score": 0.111, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T05:59:31.054044" }, { "id": 48, "name": "tanganke__resisc45_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/48", "submit_url": "https://runbenchhub.com/leaderboard/48?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "tanganke__resisc45", "description": "# [RESISC45](https://www.tensorflow.org/datasets/catalog/resisc45) ## Overview ## Usage ```python from datasets import load_dataset # Load the dataset dataset = load_dataset('tanganke/resisc45') ``` ## Dataset Information The dataset is divided into the following splits: - **Trai…", "source_url": "https://huggingface.co/datasets/tanganke/resisc45" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "google/siglip-so400m-patch14-384", "score": 0.676, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T05:59:23.726039" }, { "id": 38, "name": "tanganke__stanford_cars_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/38", "submit_url": "https://runbenchhub.com/leaderboard/38?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "tanganke__stanford_cars", "description": "# Stanford Cars Dataset ## Dataset Overview - **Splits**: - **Training**: 8144 images used for model training. - **Test**: 8041 images used for evaluation. - **Contrast**: 8041 images with high contrast for robustness testing. - **Gaussian Noise**: 8041 images corrupted by Gaussi…", "source_url": "https://huggingface.co/datasets/tanganke/stanford_cars" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "google/siglip-so400m-patch14-384", "score": 0.9482651411515981, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T04:55:20.853846" }, { "id": 45, "name": "tanganke__stl10_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/45", "submit_url": "https://runbenchhub.com/leaderboard/45?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "tanganke__stl10", "description": null, "source_url": "https://huggingface.co/datasets/tanganke/stl10" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "openai/clip-vit-large-patch14", "score": 0.994, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T05:59:19.097881" }, { "id": 63, "name": "TheKernel01__140k-Real-and-Fake-Faces_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/63", "submit_url": "https://runbenchhub.com/leaderboard/63?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "TheKernel01__140k-Real-and-Fake-Faces", "description": "# 140k Real and Fake Faces Dataset ## 📝 Dataset Description ### Dataset Summary The **140k Real and Fake Faces Dataset** is a balanced face image collection designed to benchmark models for detecting StyleGAN-generated faces. The dataset contains **70,000 real** human face photog…", "source_url": "https://huggingface.co/datasets/TheKernel01/140k-Real-and-Fake-Faces" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 1, "top": { "name": "openai/clip-vit-base-patch32", "score": 0.5805, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T05:59:38.192942" }, { "id": 78, "name": "timm__mini-imagenet_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/78", "submit_url": "https://runbenchhub.com/leaderboard/78?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "timm__mini-imagenet", "description": "## Dataset Description A mini version of ImageNet-1k with 100 of 1000 classes present. Unlike some 'mini' variants this one includes the original images at their original sizes. Many such subsets downsample to 84x84 or other smaller resolutions. ### Data Splits #### Train * 50000…", "source_url": "https://huggingface.co/datasets/timm/mini-imagenet" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "google/siglip-base-patch16-224", "score": 0.0124, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T17:39:22.147668" }, { "id": 33, "name": "timm__oxford-iiit-pet_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/33", "submit_url": "https://runbenchhub.com/leaderboard/33?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "timm__oxford-iiit-pet", "description": "# The Oxford-IIIT Pet Dataset ## Description A 37 category pet dataset with roughly 200 images for each class. The images have a large variations in scale, pose and lighting. This instance of the dataset uses standard label ordering and includes the standard train/test splits. Tr…", "source_url": "https://huggingface.co/datasets/timm/oxford-iiit-pet" } ], "n_verified": 6, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "google/siglip-so400m-patch14-384", "score": 0.9536658490051785, "metric": "top_1_accuracy" }, "updated_at": "2026-06-09T20:09:52.707459" }, { "id": 41, "name": "timm__resisc45_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/41", "submit_url": "https://runbenchhub.com/leaderboard/41?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "timm__resisc45", "description": "## Description RESISC45 dataset is a publicly available benchmark for Remote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class. The dat…", "source_url": "https://huggingface.co/datasets/timm/resisc45" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "google/siglip-so400m-patch14-384", "score": 0.6785, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T05:59:15.893025" }, { "id": 13, "name": "ylecun__mnist_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/13", "submit_url": "https://runbenchhub.com/leaderboard/13?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "ylecun__mnist", "description": "# Dataset Card for MNIST ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data In…", "source_url": "https://huggingface.co/datasets/ylecun/mnist" } ], "n_verified": 10, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "Mourad/thinqai_awesome_mnist_model", "score": 0.995, "metric": "top_1_accuracy" }, "updated_at": "2026-06-09T19:55:09.495210" }, { "id": 5, "name": "zalando-datasets__fashion_mnist_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/5", "submit_url": "https://runbenchhub.com/leaderboard/5?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "zalando-datasets__fashion_mnist", "description": "# Dataset Card for FashionMNIST ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - […", "source_url": "https://huggingface.co/datasets/zalando-datasets/fashion_mnist" } ], "n_verified": 8, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "prithivMLmods/Fashion-Mnist-SigLIP2", "score": 0.903, "metric": "top_1_accuracy" }, "updated_at": "2026-06-09T19:47:13.417789" }, { "id": 49, "name": "zh-plus__tiny-imagenet_benchmark", "category": "Vision/Image Classification", "url": "https://runbenchhub.com/leaderboard/49", "submit_url": "https://runbenchhub.com/leaderboard/49?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "zh-plus__tiny-imagenet", "description": "# Dataset Card for tiny-imagenet ## Dataset Description - **Homepage:** https://www.kaggle.com/c/tiny-imagenet - **Repository:** [Needs More Information] - **Paper:** http://cs231n.stanford.edu/reports/2017/pdfs/930.pdf - **Leaderboard:** https://paperswithcode.com/sota/image-cla…", "source_url": "https://huggingface.co/datasets/zh-plus/tiny-imagenet" } ], "n_verified": 5, "n_mirrored": 0, "n_metrics": 2, "top": { "name": "openai/clip-vit-base-patch32", "score": 0.0055, "metric": "top_1_accuracy" }, "updated_at": "2026-06-10T05:59:24.545369" }, { "id": 75, "name": "jagennath-hari__nyuv2_benchmark", "category": "Vision/Monocular Depth Estimation", "url": "https://runbenchhub.com/leaderboard/75", "submit_url": "https://runbenchhub.com/leaderboard/75?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "jagennath-hari__nyuv2", "description": "# NYUv2 This is an **unofficial and preprocessed version** of [NYU Depth Dataset V2](https://cs.nyu.edu/~fergus/datasets/nyu_depth_v2.html) made available for easier integration with modern ML workflows. The dataset was converted from the original `.mat` format into a split struc…", "source_url": "https://huggingface.co/datasets/jagennath-hari/nyuv2" } ], "n_verified": 9, "n_mirrored": 0, "n_metrics": 4, "top": { "name": "vinvino02/glpn-nyu", "score": 0.36983777343935853, "metric": "rmse_depth" }, "updated_at": "2026-06-10T10:28:49.386610" }, { "id": 85, "name": "naufalso__carla_hd_benchmark", "category": "Vision/Monocular Depth Estimation", "url": "https://runbenchhub.com/leaderboard/85", "submit_url": "https://runbenchhub.com/leaderboard/85?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "naufalso__carla_hd", "description": null, "source_url": "https://huggingface.co/datasets/naufalso/carla_hd" } ], "n_verified": 9, "n_mirrored": 0, "n_metrics": 4, "top": { "name": "Intel/zoedepth-nyu-kitti", "score": 462.22307459568225, "metric": "rmse_depth" }, "updated_at": "2026-06-10T22:45:42.671753" }, { "id": 86, "name": "POSE-Lab__IndustryShapes_benchmark", "category": "Vision/Monocular Depth Estimation", "url": "https://runbenchhub.com/leaderboard/86", "submit_url": "https://runbenchhub.com/leaderboard/86?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "POSE-Lab__IndustryShapes", "description": "# IndustryShapes [**Project Page**](https://pose-lab.github.io/IndustryShapes) | [**Paper**](https://arxiv.org/abs/2602.05555) IndustryShapes is a new benchmark dataset tailored for 6D object pose estimation in industrial settings. Targeting the challenges of textureless objects,…", "source_url": "https://huggingface.co/datasets/POSE-Lab/IndustryShapes" } ], "n_verified": 7, "n_mirrored": 0, "n_metrics": 4, "top": { "name": "depth-anything/Depth-Anything-V2-Large-hf", "score": 8202.357050887189, "metric": "rmse_depth" }, "updated_at": "2026-06-10T22:45:42.744741" }, { "id": 84, "name": "prs-eth__ZuriPano_benchmark", "category": "Vision/Monocular Depth Estimation", "url": "https://runbenchhub.com/leaderboard/84", "submit_url": "https://runbenchhub.com/leaderboard/84?utm_source=hf_space&utm_medium=submit_btn", "datasets": [ { "name": "prs-eth__ZuriPano", "description": "

🗃️ ZüriPano Dataset