High-Performance Open-Source Archive
coco_detection_dataset() into
coco_detection_dataset() (detection only) and new
coco_segmentation_dataset() (instance segmentation)
reducing memory usage by ~50% (@Chandraveersingh1717,
#280).coco_classes() is now aligned with the 90 sparse
pytorch COCO classses, in order to match pretrained model predictions.
(@318).$categories to $classes for
consistency (character vector of class names; old attribute is
deprecated with a warning) (#300).model_fcn_resnet50() with
visualization utilities draw_segmentation_masks() and
vision_make_grid() (@DerrickUnleashed, #281).search_collection(), get_collection_catalog(),
and list_collection_datasets() functions for discovering
and exploring collections (#271, @ANAMASGARD).target_transform_coco_masks() and
target_transform_trimap_masks() transformation functions
for explicit segmentation mask generation (@ANAMASGARD).connectivity argument for drawing
lines between keypoints in draw_keypoints() (@DerrickUnleashed,
#303).model_maskrcnn_resnet50_fpn() and
model_maskrcnn_resnet50_fpn_v2() for instance segmentation
(#278, @ANAMASGARD).model_convnext_*_detection() for object
detection, with * within tiny/small/base (#262, @ANAMASGARD).model_convnext_*_fcn() and
model_convnext_*_upernet() for semantic segmentation, with
* within tiny/small/base (#265, @ANAMASGARD).vggface2_dataset() for loading the VGGFace2
dataset (@DerrickUnleashed, #238).moth dataset to
rf100_biology_collection() and currency and
wine_label to rf100_document_collection()
(#274).model_maskrcnn_*() and
model_fasterrcnn_*() now match the pytorch implementation
(#318).transform_ now correctly manage batched 4D torch
tensors and 4D arrays (#313).mnist_datataset() and derivatives now correctly return
item x() values with a 1-channel dimension (@Chandraveersingh1717,
#307).transform_affine(), transform_rotate() and
random derivatives now use interpolation and
fill parameter in favor of resampleand
fillcolor (@Chandraveersingh1717,
#299).draw_keypoints() documentation and error message
(@srishtiii28,
#296).model_fasterrcnn_* did not provide boxes output
normalized to image size, did not manage batches, fix performance of the
roi_align() function (#284)..getbatch method from MNIST as it is providing
inconsistent tensor dimensions with .getitem due to
non-vectorized transform_ operations (#264).draw_keypoints() (@DerrickUnleashed,
#303).model_deeplabv3_* and
model_convnext_*_detection() to ensure consistency (@DerrickUnleashed
#302).draw_keypoints() documentation: corrected
connectivity parameter type, obsolete note,
colors description default (@srishtiii28 #296).lfw_people_dataset() and
lfw_pairs_dataset() for loading Labelled Faces in the Wild
(LFW) datasets (@DerrickUnleashed, #203).places365_dataset() for loading the Places365
dataset (@koshtiakanksha, #196).pascal_segmentation_dataset(), and
pascal_detection_dataset() for loading the Pascal Visual
Object Classes datasets (@DerrickUnleashed, #209).whoi_plankton_dataset(),
whoi_small_plankton_dataset(), and
whoi_small_coral_dataset() (@cregouby, #236).rf100_document_collection(),
rf100_medical_collection(),
rf100_biology_collection(),
rf100_damage_collection(),
rf100_infrared_collection(), and
rf100_underwater_collection(). These are collections of
datasets from Roboflow 100 under the same theme, for a total of 35
datasets (@koshtiakanksha, @cregouby, #239).rf100_peixos_segmentation_dataset(). (@koshtiakanksha,
@cregouby,
#250).model_maxvit() for MaxViT: Multi-Axis Vision
Transformer (#229, @koshtiakanksha).model_facenet_pnet(),
model_facenet_rnet(), and model_facenet_onet()
for Facenet MTCNN face detection models. (@DerrickUnleashed, #227)model_mtcnn() and
model_inception_resnet_v1() models for face detection and
recognition. (@DerrickUnleashed, #217)model_mobilenet_v3_large() and
model_mobilenet_v3_small() models for efficient image
classification. (@DerrickUnleashed, #237)model_convnext_() family models for
image classification, thanks to @horlar1 contribution. (@cregouby, #251)model_fasterrcnn_resnet50_() models and 2
model_fasterrcnn_mobilenet_v3_large_() for object
detection. (@koshtiakanksha, #251)imagenet_label() and
imagenet_classes() for ImageNet classes resolution (#229,
@koshtiakanksha).base_loader() now accept URLs (@cregouby, #246).draw_segmentation_masks() now accepts semantic
segmentation models torch_float() output. (@cregouby #247).getbatch attached method (@cregouby #255)/v2/ URL in
torch-cdn.mlverse.org. (#215)coco_* dataset family
now provides each item$x being an image array (for
consistency with other datasets). You can use
transform = transform_to_tensor to restore the previous x
output to be a torch_tensor().transform_ are now documented into 3 different
categories: unitary transformations, random transformations and
combining transformations. (@cregouby, #250)emnist_dataset is deprecated in favor of
emnist_collection() (@cregouby, #260).fashion_mnist_dataset() for loading the
Fashion-MNIST dataset (@koshtiakanksha, #148).eurosat_dataset(),
eurosat_all_bands_dataset(), and
eurosat100_dataset() for loading RGB, all-band, and
small-subset variants of the EuroSAT dataset (@cregouby, #126).qmnist_dataset() for loading the QMNIST dataset
(@DerrickUnleashed, #153).emnist_dataset() for loading the EMNIST dataset
(@DerrickUnleashed, #152).fgvc_aircraft_dataset() for loading the
FGVC-Aircraft dataset (@DerrickUnleashed, #156).coco_detection_dataset() and
coco_caption_dataset() for loading the MS COCO detection
and captions datasets (@koshtiakanksha, #161, #172).caltech101_dataset() and
caltech256_dataset() for loading the Caltech 101 and 256
datasets (@DerrickUnleashed, #158).fer_dataset() for loading the FER-2013 dataset
(@DerrickUnleashed, #154).flowers102_dataset() for loading the Flowers102
dataset (@DerrickUnleashed, #157).flickr8k_dataset() and
flickr30k_dataset() for loading the Flickr8k and Flickr30k
datasets (@DerrickUnleashed, #159).oxfordiiitpet_dataset(),
oxfordiiitpet_binary_dataset(), and
oxfordiiitpet_segmentation_dataset() for loading the
Oxford-IIIT Pet datasets (@DerrickUnleashed, #162).rf100_document_collection(),
rf100_underwater_collection(),
rf100_medical_collection(),
rf100_biology_collection(), and
rf100_peixos_segmentation_dataset() for loading Roboflow
100 datasets (@koshtiakanksha, #239).model_vit_b_16(), model_vit_b_32(),
model_vit_l_16(), model_vit_l_32(), and
model_vit_h_14() for loading Vision Transformer models
(@DerrickUnleashed, #202).tensor_image_display() and
tensor_image_browse() now accept all
tensor_image dtypes (@cregouby, #115).draw_bounding_boxes() and
draw_segmentation_masks() now accept
image_with_bounding_box and
image_with_segmentation_mask inputs which are the default
items class for respectively detection datasets and segmentation
datasets (@koshtiakanksha, #175).fgvc_aircraft_dataset() gains support for
annotation_level = "all" (@DerrickUnleashed, #168).folder_dataset() now supports TIFF image formats (@cregouby, #169).nms() and batched_nms() functions
provide Non-Maximum Suppression utilities. Added
box_convert() to convert between bounding box formats
(@Athospd, #40).transform_rotation() now correctly uses width × height
for image size instead of width × width (@cregouby, #114).transform_affine() to
reduce confusion with transform_random_affine() (@cregouby, #116).zip::unzip added in version
0.4.0. (#89)tinyimagenet-alexnet example
(#90, @statist-bhfz)torch_lstsq that was removed in torch
v0.10.0transform_adjust_hue() and
transform_linear_transformation() (#72, #73, @sebffischer)draw_bounding_boxes() ,
draw_segmentation_masks() and draw_keypoints()
on top of image tensors, and add a convenience
tensor_image_browse() and
tensor_image_display() functions to visualize image tensors
respectively in browser or in X11 device (#80, @cregouby)nnf_cross_entropy for
numerical stability. (#61)zip to zip::unzip
the tinyimagenet dataset.torch::enumerate() from docs and
tests in favor of coro::loop() (#57)torch. (#58)torch_arange calls after breaking change in
torch. (#47)transform_resize when passing
size with length 1. (#49)transform_rotate. (#31)transform_random_affine and
transform_affine (#32)NEWS.md file to track changes to the
package.
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