{ "_id": "6a169f21acfb0bcc41d80041", "Package": "torchvision", "Title": "Models, Datasets and Transformations for Images", "Version": "0.9.0.9000", "Authors@R": "c(\nperson(\"Tomasz\", \"Kalinowski\", role = c(\"ctb\", \"cre\"),\nemail = \"tomasz@posit.co\"),\nperson(\"Daniel\", \"Falbel\", role = c(\"aut\", \"cph\"),\nemail = \"dfalbel@gmail.com\"),\nperson(\"Christophe\", \"Regouby\", role = c(\"ctb\"),\nemail = \"christophe.regouby@free.fr\"),\nperson(\"Akanksha\", \"Koshti\", role = c(\"ctb\")),\nperson(\"Derrick\", \"Richard\", role = c(\"ctb\"),\nemail = \"derrickrds@gmail.com\"),\nperson(\"ANAMASGARD\", role = c(\"ctb\")),\nperson(\"Chandraveer\", \"Singh\", role = c(\"ctb\"),\nemail = \"chandraveersingh1717@gmail.com\"),\nperson(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"),\ncomment = c(ROR = \"03wc8by49\"))\n)", "Description": "Provides access to datasets, models and preprocessing\nfacilities for deep learning with images. Integrates seamlessly\nwith the 'torch' package and its API borrows heavily from the\n'PyTorch' vision package.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE, roclets = c(\"namespace\", \"rd\"))", "URL": "https://torchvision.mlverse.org,\nhttps://github.com/mlverse/torchvision", "RoxygenNote": "7.3.3", "BugReports": "https://github.com/mlverse/torchvision/issues", "Collate": "'collection-catalog.R' 'folder-dataset.R'\n'collection-rf100-doc.R' 'collection-rf100-biology.R'\n'collection-rf100-damage.R' 'collection-rf100-infrared.R'\n'collection-rf100-medical.R' 'collection-rf100-underwater.R'\n'conditions.R' 'dataset-caltech.R' 'dataset-cifar.R'\n'dataset-coco.R' 'dataset-eurosat.R' 'dataset-fer.R'\n'dataset-fgvc.R' 'dataset-flickr.R' 'dataset-flowers.R'\n'dataset-imagenet.R' 'dataset-lfw.R' 'dataset-mnist.R'\n'dataset-oxfordiiitpet.R' 'dataset-pascal.R'\n'dataset-places365.R' 'dataset-plankton.R'\n'dataset-rf100-peixos.R' 'dataset-vggface2.R' 'extension.R'\n'globals.R' 'models-alexnet.R' 'models-convnext.R'\n'models-convnext_detection.R' 'models-convnext_segmentation.R'\n'models-deeplabv3.R' 'models-efficientnet.R'\n'models-efficientnetv2.R' 'models-facenet.R'\n'models-faster_rcnn.R' 'models-fcn.R' 'models-inception.R'\n'models-mask_rcnn.R' 'models-maxvit.R' 'models-mobilenetv2.R'\n'models-mobilenetv3.R' 'models-mobilenetv3_large.R'\n'models-resnet.R' 'models-vgg.R' 'models-vit.R'\n'ops-box_convert.R' 'ops-boxes.R' 'transforms-array.R'\n'transforms-defaults.R' 'transforms-generics.R'\n'transforms-magick.R' 'transforms-segmentation.R'\n'transforms-tensor.R' 'utils.R' 'vision_utils.R'", "LazyData": "true", "VignetteBuilder": "knitr", "Config/pak/sysreqs": "cmake make libjpeg-dev libpng-dev libtiff-dev\nlibuv1-dev", "Repository": "https://mlverse.r-universe.dev", "Date/Publication": "2026-04-27 07:09:28 UTC", "RemoteUrl": "https://github.com/mlverse/torchvision", "RemoteRef": "HEAD", "RemoteSha": "057d27a7cbb8009e708ab5c471729b0fa6903511", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-27 07:27:48 UTC", "User": "root" }, "Author": "Tomasz Kalinowski [ctb, cre],\nDaniel Falbel [aut, cph],\nChristophe Regouby [ctb],\nAkanksha Koshti [ctb],\nDerrick Richard [ctb],\nANAMASGARD [ctb],\nChandraveer Singh [ctb],\nPosit Software, PBC [cph, fnd] (ROR: )", "Maintainer": "Tomasz Kalinowski ", "MD5sum": "6f6101e5653f06c86bfd7dc9b9f3c443", "_user": "mlverse", "_type": "src", "_file": "torchvision_0.9.0.9000.tar.gz", "_fileid": "7aa47ec1d70052c3f1fe12ff11164f60cd270a10f56ab173ed3dd7e714f3e637", "_filesize": 2813676, "_sha256": "7aa47ec1d70052c3f1fe12ff11164f60cd270a10f56ab173ed3dd7e714f3e637", "_created": "2026-05-27T07:27:48.000Z", "_published": "2026-05-27T07:37:04.975Z", "_distro": "noble", "_jobs": [ { "job": 78028447751, "time": 152, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7234442511" }, { "job": 78028447713, "time": 165, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7234447301" }, { "job": 78028447718, "time": 126, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7234440992" }, { "job": 78028447717, "time": 126, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7234554886" }, { "job": 78027868915, "time": 232, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7234394257" }, { "job": 78028447723, "time": 120, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7234432003" }, { "job": 78028447743, "time": 143, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7234439549" }, { "job": 78028447855, "time": 159, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7234445025" }, { "job": 78028447730, "time": 120, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7234432034" } ], "_buildurl": "https://github.com/r-universe/mlverse/actions/runs/26497078837", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/mlverse/torchvision", "_commit": { "id": "057d27a7cbb8009e708ab5c471729b0fa6903511", "author": "C. Regouby ", "committer": "C. Regouby ", "message": "Increment version number to 0.9.0.9000\n", "time": 1777273768 }, "_maintainer": { "name": "Tomasz Kalinowski", "email": "tomasz@posit.co", "login": "t-kalinowski", "mastodon": "@t_kalinowski@fosstodon.org", "bluesky": "@t-kalinowski.bsky.social", "description": "", "uuid": 8462255 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5", "role": "Depends" }, { "package": "torch", "version": ">= 0.5.0", "role": "Imports" }, { "package": "fs", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "rappdirs", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "jpeg", "role": "Imports" }, { "package": "tiff", "role": "Imports" }, { "package": "magrittr", "role": "Imports" }, { "package": "png", "role": "Imports" }, { "package": "abind", "role": "Imports" }, { "package": "jsonlite", "role": "Imports" }, { "package": "withr", "role": "Imports" }, { "package": "cli", "role": "Imports" }, { "package": "glue", "role": "Imports" }, { "package": "zeallot", "role": "Imports" }, { "package": "arrow", "role": "Suggests" }, { "package": "magick", "role": "Suggests" }, { "package": "prettyunits", "role": "Suggests" }, { "package": "testthat", "role": "Suggests" }, { "package": "coro", "role": "Suggests" }, { "package": "R.matlab", "role": "Suggests" }, { "package": "xml2", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "torchvisionlib", "role": "Suggests" } ], "_owner": "mlverse", "_selfowned": true, "_usedby": 3, "_updates": [ { "week": "2025-24", "n": 1 }, { "week": "2025-26", "n": 3 }, { "week": "2025-27", "n": 5 }, { "week": "2025-28", "n": 1 }, { "week": "2025-29", "n": 4 }, { "week": "2025-30", "n": 9 }, { "week": "2025-31", "n": 5 }, { "week": "2025-32", "n": 2 }, { "week": "2025-33", "n": 8 }, { "week": "2025-34", "n": 5 }, { "week": "2025-35", "n": 14 }, { "week": "2025-36", "n": 16 }, { "week": "2025-37", "n": 17 }, { "week": "2025-38", "n": 3 }, { "week": "2025-39", "n": 2 }, { "week": "2025-40", "n": 2 }, { "week": "2025-44", "n": 1 }, { "week": "2025-45", "n": 3 }, { "week": "2025-49", "n": 4 }, { "week": "2025-50", "n": 1 }, { "week": "2025-52", "n": 3 }, { "week": "2026-01", "n": 3 }, { "week": "2026-02", "n": 1 }, { "week": "2026-05", "n": 1 }, { "week": "2026-06", "n": 5 }, { "week": "2026-09", "n": 1 }, { "week": "2026-10", "n": 2 }, { "week": "2026-13", "n": 6 }, { "week": "2026-14", "n": 5 }, { "week": "2026-15", "n": 4 }, { "week": "2026-16", "n": 3 }, { "week": "2026-17", "n": 1 }, { "week": "2026-18", "n": 1 } ], "_tags": [ { "name": "v0.7.0", "date": "2025-07-16" }, { "name": "v0.8.0", "date": "2025-11-06" }, { "name": "v0.9.0", "date": "2026-04-23" } ], "_stars": 76, "_contributors": [ { "user": "dfalbel", "count": 255, "uuid": 4706822 }, { "user": "cregouby", "count": 124, "uuid": 10136115 }, { "user": "koshtiakanksha", "count": 47, "uuid": 97248384 }, { "user": "derrickunleashed", "count": 21, "uuid": 137819955 }, { "user": "skeydan", "count": 11, "uuid": 469371 }, { "user": "christophe-regouby", "count": 9, "uuid": 167972538 }, { "user": "sebffischer", "count": 6, "uuid": 20975557 }, { "user": "anamasgard", "count": 6, "uuid": 137998824 }, { "user": "chandraveersingh1717", "count": 5, "uuid": 183128110 }, { "user": "athospd", "count": 3, "uuid": 1925102 }, { "user": "statist-bhfz", "count": 2, "uuid": 7324186 }, { "user": "mohamed-180", "count": 2, "uuid": 39778149 }, { "user": "dirkschumacher", "count": 1, "uuid": 1569647 }, { "user": "srishtiii28", "count": 1, "uuid": 144199086 }, { "user": "sau1812", "count": 1, "uuid": 146443602 } ], "_userbio": { "uuid": 55406849, "type": "organization", "name": "mlverse", "description": "Open source libraries to scale Data Science" }, "_downloads": { "count": 6784, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/torchvision" }, "_mentions": 22, "_devurl": "https://github.com/mlverse/torchvision", "_pkgdown": "https://torchvision.mlverse.org", "_searchresults": 380, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "extra/torchvision.html", "manual.pdf" ], "_homeurl": "https://github.com/mlverse/torchvision", "_realowner": "mlverse", "_cranurl": true, "_releases": [ { "version": "0.1.0", "date": "2020-10-29" }, { "version": "0.2.0", "date": "2021-01-05" }, { "version": "0.3.0", "date": "2021-05-04" }, { "version": "0.4.0", "date": "2021-08-17" }, { "version": "0.4.1", "date": "2022-01-28" }, { "version": "0.5.0", "date": "2023-03-15" }, { "version": "0.5.1", "date": "2023-04-14" }, { "version": "0.6.0", "date": "2024-06-14" }, { "version": "0.7.0", "date": "2025-07-18" }, { "version": "0.8.0", "date": "2025-11-06" }, { "version": "0.9.0", "date": "2026-04-22" } ], "_exports": [ "base_loader", "batched_nms", "box_area", "box_convert", "box_iou", "caltech_classes", "caltech101_dataset", "caltech256_dataset", "cifar10_dataset", "cifar100_dataset", "clip_boxes_to_image", "coco_caption_dataset", "coco_classes", "coco_detection_dataset", "coco_segmentation_dataset", "draw_bounding_boxes", "draw_keypoints", "draw_segmentation_masks", "emnist_collection", "emnist_dataset", "eurosat_all_bands_dataset", "eurosat_dataset", "eurosat100_dataset", "fashion_mnist_dataset", "fer_dataset", "fgvc_aircraft_dataset", "flickr30k_caption_dataset", "flickr8k_caption_dataset", "flowers102_dataset", "generalized_box_iou", "get_collection_catalog", "image_folder_dataset", "imagenet_1k_classes", "imagenet_21k_classes", "imagenet_21k_df", "imagenet_classes", "kmnist_dataset", "lfw_pairs_dataset", "lfw_people_dataset", "list_collection_datasets", "magick_loader", "mnist_dataset", "model_alexnet", "model_convnext_base_1k", "model_convnext_base_22k", "model_convnext_base_detection", "model_convnext_base_fcn", "model_convnext_base_upernet", "model_convnext_large_1k", "model_convnext_large_22k", "model_convnext_small_22k", "model_convnext_small_22k1k", "model_convnext_small_detection", "model_convnext_small_fcn", "model_convnext_small_upernet", "model_convnext_tiny_1k", "model_convnext_tiny_22k", "model_convnext_tiny_detection", "model_convnext_tiny_fcn", "model_convnext_tiny_upernet", "model_deeplabv3_resnet101", "model_deeplabv3_resnet50", "model_efficientnet_b0", "model_efficientnet_b1", "model_efficientnet_b2", "model_efficientnet_b3", "model_efficientnet_b4", "model_efficientnet_b5", "model_efficientnet_b6", "model_efficientnet_b7", "model_efficientnet_v2_l", "model_efficientnet_v2_m", "model_efficientnet_v2_s", "model_facenet_inception_resnet_v1", "model_facenet_onet", "model_facenet_pnet", "model_facenet_rnet", "model_fasterrcnn_mobilenet_v3_large_320_fpn", "model_fasterrcnn_mobilenet_v3_large_fpn", "model_fasterrcnn_resnet50_fpn", "model_fasterrcnn_resnet50_fpn_v2", "model_fcn_resnet101", "model_fcn_resnet50", "model_inception_v3", "model_maskrcnn_resnet50_fpn", "model_maskrcnn_resnet50_fpn_v2", "model_maxvit", "model_mobilenet_v2", "model_mobilenet_v3_large", "model_mobilenet_v3_large_quantized", "model_mobilenet_v3_small", "model_mtcnn", "model_resnet101", "model_resnet152", "model_resnet18", "model_resnet34", "model_resnet50", "model_resnext101_32x8d", "model_resnext50_32x4d", "model_vgg11", "model_vgg11_bn", "model_vgg13", "model_vgg13_bn", "model_vgg16", "model_vgg16_bn", "model_vgg19", "model_vgg19_bn", "model_vit_b_16", "model_vit_b_32", "model_vit_h_14", "model_vit_l_16", "model_vit_l_32", "model_wide_resnet101_2", "model_wide_resnet50_2", "nms", "oxfordiiitpet_binary_dataset", "oxfordiiitpet_dataset", "oxfordiiitpet_segmentation_dataset", "pascal_detection_dataset", "pascal_segmentation_dataset", "pascal_voc_classes", "places365_dataset", "places365_dataset_large", "qmnist_dataset", "remove_small_boxes", "rf100_biology_collection", "rf100_damage_collection", "rf100_document_collection", "rf100_infrared_collection", "rf100_medical_collection", "rf100_peixos_segmentation_dataset", "rf100_underwater_collection", "search_collection", "target_transform_coco_masks", "target_transform_trimap_masks", "tensor_image_browse", "tensor_image_display", "tiny_imagenet_dataset", "transform_adjust_brightness", "transform_adjust_contrast", "transform_adjust_gamma", "transform_adjust_hue", "transform_adjust_saturation", "transform_affine", "transform_center_crop", "transform_color_jitter", "transform_convert_image_dtype", "transform_crop", "transform_five_crop", "transform_grayscale", "transform_hflip", "transform_linear_transformation", "transform_normalize", "transform_pad", "transform_perspective", "transform_random_affine", "transform_random_apply", "transform_random_choice", "transform_random_crop", "transform_random_erasing", "transform_random_grayscale", "transform_random_horizontal_flip", "transform_random_order", "transform_random_perspective", "transform_random_resized_crop", "transform_random_rotation", "transform_random_vertical_flip", "transform_resize", "transform_resized_crop", "transform_rgb_to_grayscale", "transform_rotate", "transform_ten_crop", "transform_to_tensor", "transform_vflip", "vggface2_dataset", "vision_make_grid", "whoi_plankton_dataset", "whoi_small_coralnet_dataset", "whoi_small_plankton_dataset" ], "_datasets": [ { "name": "collection_catalog", "title": "Dataset Collection Catalog", "object": "collection_catalog", "class": [ "data.frame" ], "fields": [ "collection", "dataset", "description", "task", "num_classes", "num_images", "image_width", "image_height", "train_size_mb", "test_size_mb", "valid_size_mb", "total_size_mb", "has_train", "has_test", "has_valid", "function_name", "roboflow_url" ], "rows": 43, "table": true, "tojson": true } ], "_help": [ { "page": "base_loader", "title": "Base loader", "topics": [ "base_loader" ] }, { "page": "batched_nms", "title": "Batched Non-maximum Suppression (NMS)", "topics": [ "batched_nms" ] }, { "page": "box_area", "title": "Box Area", "topics": [ "box_area" ] }, { "page": "box_convert", "title": "Box Convert", "topics": [ "box_convert" ] }, { "page": "box_cxcywh_to_xyxy", "title": "box_cxcywh_to_xyxy", "topics": [ "box_cxcywh_to_xyxy" ] }, { "page": "box_iou", "title": "Box IoU", "topics": [ "box_iou" ] }, { "page": "box_xywh_to_xyxy", "title": "box_xywh_to_xyxy", "topics": [ "box_xywh_to_xyxy" ] }, { "page": "box_xyxy_to_cxcywh", "title": "box_xyxy_to_cxcywh", "topics": [ "box_xyxy_to_cxcywh" ] }, { "page": "box_xyxy_to_xywh", "title": "box_xyxy_to_xywh", "topics": [ "box_xyxy_to_xywh" ] }, { "page": "caltech_classes", "title": "Caltech Class Labels", "concept": [ "class_resolution" ], "topics": [ "caltech_classes" ] }, { "page": "caltech_dataset", "title": "Caltech Datasets", "concept": [ "classification_dataset" ], "topics": [ "caltech101_dataset", "caltech256_dataset", "caltech_dataset" ] }, { "page": "cifar_datasets", "title": "CIFAR datasets", "concept": [ "classification_dataset" ], "topics": [ "cifar100_dataset", "cifar10_dataset" ] }, { "page": "clip_boxes_to_image", "title": "Clip Boxes to Image", "topics": [ "clip_boxes_to_image" ] }, { "page": "coco_caption_dataset", "title": "COCO Caption Dataset", "concept": [ "caption_dataset" ], "topics": [ "coco_caption_dataset" ] }, { "page": "coco_classes", "title": "MS COCO Class Labels", "concept": [ "class_resolution" ], "topics": [ "coco_classes" ] }, { "page": "coco_detection_dataset", "title": "COCO Detection Dataset", "concept": [ "detection_dataset" ], "topics": [ "coco_detection_dataset" ] }, { "page": "coco_segmentation_dataset", "title": "COCO Segmentation Dataset", "concept": [ "segmentation_dataset" ], "topics": [ "coco_segmentation_dataset" ] }, { "page": "collection_catalog", "title": "Dataset Collection Catalog", "topics": [ "collection_catalog" ] }, { "page": "draw_bounding_boxes", "title": "Draws bounding boxes on image.", "concept": [ "image display" ], "topics": [ "draw_bounding_boxes", "draw_bounding_boxes.default", "draw_bounding_boxes.image_with_bounding_box", "draw_bounding_boxes.torch_tensor" ] }, { "page": "draw_keypoints", "title": "Draws Keypoints", "concept": [ "image display" ], "topics": [ "draw_keypoints" ] }, { "page": "draw_segmentation_masks", "title": "Draw segmentation masks", "concept": [ "image display" ], "topics": [ "draw_segmentation_masks", "draw_segmentation_masks.default", "draw_segmentation_masks.image_with_segmentation_mask", "draw_segmentation_masks.torch_tensor" ] }, { "page": "eurosat_dataset", "title": "EuroSAT datasets", "concept": [ "classification_dataset" ], "topics": [ "eurosat100_dataset", "eurosat_all_bands_dataset", "eurosat_dataset" ] }, { "page": "fer_dataset", "title": "FER-2013 Facial Expression Dataset", "concept": [ "classification_dataset" ], "topics": [ "fer_dataset" ] }, { "page": "fgvc_aircraft_dataset", "title": "FGVC Aircraft Dataset", "concept": [ "classification_dataset" ], "topics": [ "fgvc_aircraft_dataset" ] }, { "page": "flickr_caption_dataset", "title": "Flickr Caption Datasets", "concept": [ "caption_dataset" ], "topics": [ "flickr30k_caption_dataset", "flickr8k_caption_dataset", "flickr_caption_dataset" ] }, { "page": "flowers102_dataset", "title": "Oxford Flowers 102 Dataset", "concept": [ "classification_dataset" ], "topics": [ "flowers102_dataset" ] }, { "page": "generalized_box_iou", "title": "Generalized Box IoU", "topics": [ "generalized_box_iou" ] }, { "page": "get_collection_catalog", "title": "Get Complete Collection Catalog", "topics": [ "get_collection_catalog" ] }, { "page": "image_folder_dataset", "title": "Create an image folder dataset", "concept": [ "classification_dataset" ], "topics": [ "image_folder_dataset" ] }, { "page": "imagenet_classes", "title": "ImageNet Class Labels", "concept": [ "class_resolution" ], "topics": [ "imagenet_1k_classes", "imagenet_21k_classes", "imagenet_21k_df", "imagenet_classes" ] }, { "page": "lfw_dataset", "title": "LFW Datasets", "concept": [ "classification_dataset" ], "topics": [ "lfw_dataset", "lfw_pairs_dataset", "lfw_people_dataset" ] }, { "page": "list_collection_datasets", "title": "List Datasets in a Collection", "topics": [ "list_collection_datasets" ] }, { "page": "magick_loader", "title": "Load an Image using ImageMagick", "topics": [ "magick_loader" ] }, { "page": "mnist_dataset", "title": "MNIST and Derived Datasets", "concept": [ "classification_dataset" ], "topics": [ "emnist_collection", "emnist_dataset", "fashion_mnist_dataset", "kmnist_dataset", "mnist_dataset", "qmnist_dataset" ] }, { "page": "model_alexnet", "title": "AlexNet Model Architecture", "concept": [ "classification_model" ], "topics": [ "model_alexnet" ] }, { "page": "model_convnext", "title": "ConvNeXt Implementation", "concept": [ "classification_model" ], "topics": [ "model_convnext", "model_convnext_base_1k", "model_convnext_base_22k", "model_convnext_large_1k", "model_convnext_large_22k", "model_convnext_small_22k", "model_convnext_small_22k1k", "model_convnext_tiny_1k", "model_convnext_tiny_22k" ] }, { "page": "model_convnext_detection", "title": "ConvNeXt Detection Models (Faster R-CNN style)", "concept": [ "object_detection_model" ], "topics": [ "model_convnext_base_detection", "model_convnext_detection", "model_convnext_small_detection", "model_convnext_tiny_detection" ] }, { "page": "model_convnext_segmentation", "title": "ConvNeXt Segmentation Models", "concept": [ "semantic_segmentation_model" ], "topics": [ "model_convnext_base_fcn", "model_convnext_base_upernet", "model_convnext_segmentation", "model_convnext_small_fcn", "model_convnext_small_upernet", "model_convnext_tiny_fcn", "model_convnext_tiny_upernet" ] }, { "page": "model_deeplabv3", "title": "DeepLabV3 Semantic Segmentation Models", "concept": [ "semantic_segmentation_model" ], "topics": [ "model_deeplabv3", "model_deeplabv3_resnet101", "model_deeplabv3_resnet50" ] }, { "page": "model_efficientnet", "title": "EfficientNet Models", "concept": [ "classification_model" ], "topics": [ "model_efficientnet", "model_efficientnet_b0", "model_efficientnet_b1", "model_efficientnet_b2", "model_efficientnet_b3", "model_efficientnet_b4", "model_efficientnet_b5", "model_efficientnet_b6", "model_efficientnet_b7" ] }, { "page": "model_efficientnet_v2", "title": "EfficientNetV2 Models", "concept": [ "classification_model" ], "topics": [ "model_efficientnet_v2", "model_efficientnet_v2_l", "model_efficientnet_v2_m", "model_efficientnet_v2_s" ] }, { "page": "model_facenet", "title": "MTCNN Face Detection Networks", "concept": [ "classification_model", "object_detection_model" ], "topics": [ "model_facenet", "model_facenet_inception_resnet_v1", "model_facenet_onet", "model_facenet_pnet", "model_facenet_rnet", "model_mtcnn" ] }, { "page": "model_fasterrcnn", "title": "Faster R-CNN Models", "concept": [ "object_detection_model" ], "topics": [ "model_fasterrcnn", "model_fasterrcnn_mobilenet_v3_large_320_fpn", "model_fasterrcnn_mobilenet_v3_large_fpn", "model_fasterrcnn_resnet50_fpn", "model_fasterrcnn_resnet50_fpn_v2" ] }, { "page": "model_fcn_resnet", "title": "Fully Convolutional Network for Semantic Segmentation", "concept": [ "semantic_segmentation_model" ], "topics": [ "model_fcn_resnet", "model_fcn_resnet101", "model_fcn_resnet50" ] }, { "page": "model_inception_v3", "title": "Inception v3 model", "concept": [ "classification_model" ], "topics": [ "model_inception_v3" ] }, { "page": "model_maskrcnn", "title": "Mask R-CNN Models", "concept": [ "object_detection_model" ], "topics": [ "model_maskrcnn", "model_maskrcnn_resnet50_fpn", "model_maskrcnn_resnet50_fpn_v2" ] }, { "page": "model_maxvit", "title": "MaxViT Model", "concept": [ "classification_model" ], "topics": [ "model_maxvit" ] }, { "page": "model_mobilenet_v2", "title": "MobileNetV2 Model", "concept": [ "classification_model" ], "topics": [ "model_mobilenet_v2" ] }, { "page": "model_mobilenet_v3", "title": "MobileNetV3 Model", "concept": [ "classification_model" ], "topics": [ "model_mobilenet_v3", "model_mobilenet_v3_large", "model_mobilenet_v3_large_quantized", "model_mobilenet_v3_small" ] }, { "page": "model_resnet", "title": "ResNet implementation", "concept": [ "classification_model" ], "topics": [ "model_resnet", "model_resnet101", "model_resnet152", "model_resnet18", "model_resnet34", "model_resnet50", "model_resnext101_32x8d", "model_resnext50_32x4d", "model_wide_resnet101_2", "model_wide_resnet50_2" ] }, { "page": "model_vgg", "title": "VGG implementation", "concept": [ "classification_model" ], "topics": [ "model_vgg", "model_vgg11", "model_vgg11_bn", "model_vgg13", "model_vgg13_bn", "model_vgg16", "model_vgg16_bn", "model_vgg19", "model_vgg19_bn" ] }, { "page": "model_vit", "title": "Vision Transformer Implementation", "concept": [ "classification_model" ], "topics": [ "model_vit", "model_vit_b_16", "model_vit_b_32", "model_vit_h_14", "model_vit_l_16", "model_vit_l_32" ] }, { "page": "nms", "title": "Non-maximum Suppression (NMS)", "topics": [ "nms" ] }, { "page": "oxfordiiitpet_dataset", "title": "Oxford-IIIT Pet Classification Datasets", "concept": [ "classification_dataset" ], "topics": [ "oxfordiiitpet_binary_dataset", "oxfordiiitpet_dataset" ] }, { "page": "oxfordiiitpet_segmentation_dataset", "title": "Oxford-IIIT Pet Segmentation Dataset", "concept": [ "segmentation_dataset" ], "topics": [ "oxfordiiitpet_segmentation_dataset" ] }, { "page": "pascal_voc_classes", "title": "Pascal VOC Segmentation Dataset", "concept": [ "class_resolution" ], "topics": [ "pascal_voc_classes" ] }, { "page": "pascal_voc_datasets", "title": "Pascal VOC Datasets", "concept": [ "detection_dataset", "segmentation_dataset" ], "topics": [ "pascal_detection_dataset", "pascal_segmentation_dataset", "pascal_voc_datasets" ] }, { "page": "places365_dataset", "title": "Places365 Dataset", "concept": [ "classification_dataset" ], "topics": [ "places365_dataset", "places365_dataset_large" ] }, { "page": "remove_small_boxes", "title": "Remove Small Boxes", "topics": [ "remove_small_boxes" ] }, { "page": "rf100_biology_collection", "title": "RoboFlow 100 Biology dataset Collection", "concept": [ "detection_dataset" ], "topics": [ "rf100_biology_collection" ] }, { "page": "rf100_damage_collection", "title": "RoboFlow 100 Damages dataset Collection", "concept": [ "detection_dataset" ], "topics": [ "rf100_damage_collection" ] }, { "page": "rf100_document_collection", "title": "RF100 Document Collection Datasets", "concept": [ "detection_dataset" ], "topics": [ "rf100_document_collection" ] }, { "page": "rf100_infrared_collection", "title": "RoboFlow 100 Infrared dataset Collection", "concept": [ "detection_dataset" ], "topics": [ "rf100_infrared_collection" ] }, { "page": "rf100_medical_collection", "title": "RoboFlow 100 Medical dataset Collection", "concept": [ "detection_dataset" ], "topics": [ "rf100_medical_collection" ] }, { "page": "rf100_peixos_segmentation_dataset", "title": "RF100 Peixos Segmentation Dataset", "concept": [ "segmentation_dataset" ], "topics": [ "rf100_peixos_segmentation_dataset" ] }, { "page": "rf100_underwater_collection", "title": "RoboFlow 100 Underwater dataset Collection", "concept": [ "detection_dataset" ], "topics": [ "rf100_underwater_collection" ] }, { "page": "search_collection", "title": "Search Collection Catalog", "topics": [ "search_collection" ] }, { "page": "target_transform_coco_masks", "title": "Target Transform: COCO Polygon Segmentation to Masks", "concept": [ "target_transforms" ], "topics": [ "target_transform_coco_masks" ] }, { "page": "target_transform_trimap_masks", "title": "Target Transform: Trimap to Boolean Masks", "concept": [ "target_transforms" ], "topics": [ "target_transform_trimap_masks" ] }, { "page": "tensor_image_browse", "title": "Display image tensor", "concept": [ "image display" ], "topics": [ "tensor_image_browse" ] }, { "page": "tensor_image_display", "title": "Display image tensor", "concept": [ "image display" ], "topics": [ "tensor_image_display" ] }, { "page": "tiny_imagenet_dataset", "title": "Tiny ImageNet dataset", "concept": [ "classification_dataset" ], "topics": [ "tiny_imagenet_dataset" ] }, { "page": "transform_adjust_brightness", "title": "Adjust the brightness of an image", "concept": [ "unitary_transforms" ], "topics": [ "transform_adjust_brightness" ] }, { "page": "transform_adjust_contrast", "title": "Adjust the contrast of an image", "concept": [ "unitary_transforms" ], "topics": [ "transform_adjust_contrast" ] }, { "page": "transform_adjust_gamma", "title": "Adjust the gamma of an RGB image", "concept": [ "unitary_transforms" ], "topics": [ "transform_adjust_gamma" ] }, { "page": "transform_adjust_hue", "title": "Adjust the hue of an image", "concept": [ "unitary_transforms" ], "topics": [ "transform_adjust_hue" ] }, { "page": "transform_adjust_saturation", "title": "Adjust the color saturation of an image", "concept": [ "unitary_transforms" ], "topics": [ "transform_adjust_saturation" ] }, { "page": "transform_affine", "title": "Apply affine transformation on an image keeping image center invariant", "concept": [ "unitary_transforms" ], "topics": [ "transform_affine" ] }, { "page": "transform_center_crop", "title": "Crops the given image at the center", "concept": [ "unitary_transforms" ], "topics": [ "transform_center_crop" ] }, { "page": "transform_color_jitter", "title": "Randomly change the brightness, contrast and saturation of an image", "concept": [ "random_transforms" ], "topics": [ "transform_color_jitter" ] }, { "page": "transform_convert_image_dtype", "title": "Convert a tensor image to the given 'dtype' and scale the values accordingly", "concept": [ "unitary_transforms" ], "topics": [ "transform_convert_image_dtype" ] }, { "page": "transform_crop", "title": "Crop the given image at specified location and output size", "concept": [ "unitary_transforms" ], "topics": [ "transform_crop" ] }, { "page": "transform_five_crop", "title": "Crop image into four corners and a central crop", "concept": [ "combining_transforms" ], "topics": [ "transform_five_crop" ] }, { "page": "transform_grayscale", "title": "Convert image to grayscale", "concept": [ "unitary_transforms" ], "topics": [ "transform_grayscale" ] }, { "page": "transform_hflip", "title": "Horizontally flip a PIL Image or Tensor", "concept": [ "unitary_transforms" ], "topics": [ "transform_hflip" ] }, { "page": "transform_linear_transformation", "title": "Transform a tensor image with a square transformation matrix and a mean_vector computed offline", "concept": [ "unitary_transforms" ], "topics": [ "transform_linear_transformation" ] }, { "page": "transform_normalize", "title": "Normalize a tensor image with mean and standard deviation", "concept": [ "unitary_transforms" ], "topics": [ "transform_normalize" ] }, { "page": "transform_pad", "title": "Pad the given image on all sides with the given \"pad\" value", "concept": [ "unitary_transforms" ], "topics": [ "transform_pad" ] }, { "page": "transform_perspective", "title": "Perspective transformation of an image", "concept": [ "unitary_transforms" ], "topics": [ "transform_perspective" ] }, { "page": "transform_random_affine", "title": "Random affine transformation of the image keeping center invariant", "concept": [ "random_transforms" ], "topics": [ "transform_random_affine" ] }, { "page": "transform_random_apply", "title": "Apply a list of transformations randomly with a given probability", "concept": [ "combining_transforms" ], "topics": [ "transform_random_apply" ] }, { "page": "transform_random_choice", "title": "Apply single transformation randomly picked from a list", "concept": [ "combining_transforms" ], "topics": [ "transform_random_choice" ] }, { "page": "transform_random_crop", "title": "Crop the given image at a random location", "concept": [ "random_transforms" ], "topics": [ "transform_random_crop" ] }, { "page": "transform_random_erasing", "title": "Randomly selects a rectangular region in an image and erases its pixel values", "concept": [ "random_transforms" ], "topics": [ "transform_random_erasing" ] }, { "page": "transform_random_grayscale", "title": "Randomly convert image to grayscale with a given probability", "concept": [ "random_transforms" ], "topics": [ "transform_random_grayscale" ] }, { "page": "transform_random_horizontal_flip", "title": "Horizontally flip an image randomly with a given probability", "concept": [ "random_transforms" ], "topics": [ "transform_random_horizontal_flip" ] }, { "page": "transform_random_order", "title": "Apply a list of transformations in a random order", "concept": [ "combining_transforms" ], "topics": [ "transform_random_order" ] }, { "page": "transform_random_perspective", "title": "Random perspective transformation of an image with a given probability", "concept": [ "random_transforms" ], "topics": [ "transform_random_perspective" ] }, { "page": "transform_random_resized_crop", "title": "Crop image to random size and aspect ratio", "concept": [ "random_transforms" ], "topics": [ "transform_random_resized_crop" ] }, { "page": "transform_random_rotation", "title": "Rotate the image by angle", "concept": [ "random_transforms" ], "topics": [ "transform_random_rotation" ] }, { "page": "transform_random_vertical_flip", "title": "Vertically flip an image randomly with a given probability", "concept": [ "random_transforms" ], "topics": [ "transform_random_vertical_flip" ] }, { "page": "transform_resize", "title": "Resize the input image to the given size", "concept": [ "unitary_transforms" ], "topics": [ "transform_resize" ] }, { "page": "transform_resized_crop", "title": "Crop an image and resize it to a desired size", "concept": [ "combining_transforms" ], "topics": [ "transform_resized_crop" ] }, { "page": "transform_rgb_to_grayscale", "title": "Convert RGB Image Tensor to Grayscale", "concept": [ "unitary_transforms" ], "topics": [ "transform_rgb_to_grayscale" ] }, { "page": "transform_rotate", "title": "Angular rotation of an image", "concept": [ "unitary_transforms" ], "topics": [ "transform_rotate" ] }, { "page": "transform_ten_crop", "title": "Crop an image and the flipped image each into four corners and a central crop", "concept": [ "combining_transforms" ], "topics": [ "transform_ten_crop" ] }, { "page": "transform_to_tensor", "title": "Convert an image to a tensor", "concept": [ "unitary_transforms" ], "topics": [ "transform_to_tensor" ] }, { "page": "transform_vflip", "title": "Vertically flip a PIL Image or Tensor", "concept": [ "unitary_transforms" ], "topics": [ "transform_vflip" ] }, { "page": "vggface2_dataset", "title": "VGGFace2 Dataset", "concept": [ "classification_dataset" ], "topics": [ "vggface2_dataset" ] }, { "page": "vision_make_grid", "title": "A simplified version of torchvision.utils.make_grid", "concept": [ "image display" ], "topics": [ "vision_make_grid" ] }, { "page": "whoi_plankton_dataset", "title": "WHOI Plankton Datasets", "concept": [ "classification_dataset" ], "topics": [ "whoi_plankton_dataset", "whoi_small_plankton_dataset" ] }, { "page": "whoi_small_coralnet_dataset", "title": "Coralnet Dataset", "concept": [ "classification_dataset" ], "topics": [ "whoi_small_coralnet_dataset" ] } ], "_readme": "https://github.com/mlverse/torchvision/raw/HEAD/README.md", "_rundeps": [ "abind", "bit", "bit64", "callr", "cli", "coro", "desc", "farver", "fs", "glue", "jpeg", "jsonlite", "labeling", "lifecycle", "magrittr", "png", "processx", "ps", "R6", "rappdirs", "RColorBrewer", "Rcpp", "rlang", "safetensors", "scales", "tiff", "torch", "viridisLite", "withr", "zeallot" ], "_vignettes": [ { "source": "rf100-datasets.Rmd", "filename": "rf100-datasets.html", "title": "RF100 Dataset Catalog", "engine": "knitr::rmarkdown", "headings": [ "Overview", "Quick Search", "Example: Finding a Photovoltaic Dataset", "Complete Catalog", "Collections", "Biology Collection (9 datasets)", "Medical Collection (8 datasets)", "Infrared Collection (4 datasets)", "Damage Collection (3 datasets)", "Underwater Collection (4 datasets)", "Document Collection (6 datasets)", "Usage Example", "Dataset Statistics", "Filtering and Exploration", "Additional Resources", "Citation" ], "created": "2025-12-26 16:42:11", "modified": "2026-04-19 11:47:21", "commits": 3 } ], "_score": 12.609937517477729, "_indexed": true, "_nocasepkg": "torchvision", "_universes": [ "mlverse", "t-kalinowski" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.9.0.9000", "date": "2026-05-27T07:30:18.000Z", "distro": "noble", "commit": "057d27a7cbb8009e708ab5c471729b0fa6903511", "fileid": "6ea0fb44e8ae2192c2d3a5aabf852a9e7f2c68f9aadceb54c94417b05d90a294", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mlverse/actions/runs/26497078837" }, { "r": "4.6.0", "os": "linux", "version": "0.9.0.9000", "date": "2026-05-27T07:30:29.000Z", "distro": "noble", "commit": "057d27a7cbb8009e708ab5c471729b0fa6903511", "fileid": "492957fc670a43f5ded359ad6d05fd21edb4536cc18715e068f61b894e0d8660", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mlverse/actions/runs/26497078837" }, { "r": "4.5.3", "os": "mac", "version": "0.9.0.9000", "date": "2026-05-27T07:30:23.000Z", "commit": "057d27a7cbb8009e708ab5c471729b0fa6903511", "fileid": "657021840b99bfa2a448678c785c129e8bdffebb9b5c83833e42bce71764577e", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mlverse/actions/runs/26497078837" }, { "r": "4.6.0", "os": "mac", "version": "0.9.0.9000", "date": "2026-05-27T07:36:10.000Z", "commit": "057d27a7cbb8009e708ab5c471729b0fa6903511", "fileid": "9ec57ead453e7a38ca5c19ddc09b81ee183244220a9fb52caaf5024df2aeecbb", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mlverse/actions/runs/26497078837" }, { "r": "4.6.0", "os": "wasm", "version": "0.9.0.9000", "date": "2026-05-27T07:30:27.000Z", "commit": "057d27a7cbb8009e708ab5c471729b0fa6903511", "fileid": "6099df2eb17a5237c6d10310461bdb0b912948c0f898610c894a421efb0af0cd", "status": "success", "buildurl": "https://github.com/r-universe/mlverse/actions/runs/26497078837" }, { "r": "4.7.0", "os": "win", "version": "0.9.0.9000", "date": "2026-05-27T07:29:55.000Z", "commit": "057d27a7cbb8009e708ab5c471729b0fa6903511", "fileid": "8356b870dea0bbdff4c0639e38aa9a214bb43f7ab560052c0929893b88001b93", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mlverse/actions/runs/26497078837" }, { "r": "4.5.3", "os": "win", "version": "0.9.0.9000", "date": "2026-05-27T07:30:10.000Z", "commit": "057d27a7cbb8009e708ab5c471729b0fa6903511", "fileid": "093eced4423e1dc79f456c763b0d9d9f65f1d102e73ca82a7b213483a2323c0e", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mlverse/actions/runs/26497078837" }, { "r": "4.6.0", "os": "win", "version": "0.9.0.9000", "date": "2026-05-27T07:29:32.000Z", "commit": "057d27a7cbb8009e708ab5c471729b0fa6903511", "fileid": "90db36195e18c05bb1aca785fe98c1b162d0f7b6b234d813695d0145762467ac", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mlverse/actions/runs/26497078837" } ] }