Repository navigation
chore(deps): update dependency torchvision to v0.29.0 - autoclosed - #43
renovate-bot wants to merge 1 commit into
Conversation
|
Review the following changes in direct dependencies. Learn more about Socket for GitHub.
|
|
Tick the box to add this pull request to the merge queue (same as
|
![gemini-code-assist[bot]](https://avatars.githubusercontent.com/in/956858?s=60&v=4){kind=small}
There was a problem hiding this comment.
Code Review
This pull request updates the torchvision dependency in requirements.txt from 0.16.2 to 0.28.0. The review feedback correctly points out that this upgrade will break the notebooks/image-classifier.ipynb notebook because the .next() method on DataLoader iterators has been removed in newer PyTorch versions. It is recommended to update the notebook to use the standard Python next(dataiter) function instead.
Important
The consumer version of Gemini Code Assist on GitHub is being sunset. Starting June 18, 2026, new organization installations will be blocked, and all code review activity will officially cease on July 17, 2026.
For more details on the timeline and next steps, please review the Help Documentation.
| pandas | ||
| torch | ||
| torchvision==0.16.2 | ||
| torchvision==0.28.0 |
There was a problem hiding this comment.
Updating torchvision to 0.28.0 will require a newer version of torch. In newer versions of PyTorch, the .next() method on DataLoader iterators has been removed. This will break the execution of the notebook notebooks/image-classifier.ipynb on lines 77 and 229, where dataiter.next() is called, throwing an AttributeError. Please update the notebook to use the standard Python next(dataiter) function instead: images, labels = next(dataiter).
bd7e819 to
470add8
Compare
renovate-bot
changed the title
renovate-bot
changed the title
1 participant
This PR contains the following updates:
==0.16.2→==0.29.0Release Notes
pytorch/vision (torchvision)
v0.29.0: TorchVision 0.29: ABI stability!Compare Source
TorchVision 0.29 is out! It comes with two major changes: ABI stability, and deprecation of the image decoders and encoders (now in TorchCodec)!
ABI Stability with torch 2.14
TorchVision is now ABI stable w.r.t.
torch 2.14! This means thattorchvision 0.29will be compatible with future versions oftorch:2.15,2.16, etc. You won’t need to install a new version of TorchVision when you upgrade torch.As a result, we might stop releasing TorchVision in sync with pytorch. But TorchVision is still actively maintained and developed: we’ll still be pushing releases, just not with the same cadence.
Thanks to Adrian Abeyta @adabeyta for the fantastic porting effort!
PRs: #9524, #9597, #9598, #9605, #9612, #9610, #9614, #9584, #9617, #9618, #9619, #9620, #9582, #9573, #9625, #9623, #9626, #9583, #9633, #9572, #9549, #9533, #9535, #9539, #9543, #9550, #9552, #9555, #9557, #9558, #9554
Image decoders and encoders are deprecated. Use TorchCodec!
The image decoders and encoders in
torchvision.ioare now deprecated, and they will be removed in a future release. They are now available in torchcodec >= 0.16, where they are significantly more capable. You’ll just need topip install torchcodec, and you can refer to this migration guide for migrating your code (most APIs for decoding are the same).This finalizes a clear separation of concerns for the three media-processing libraries of PyTorch:
torchcodecis for decoding and encoding all media (images, videos, and audio), whiletorchvisionandtorchaudiofocus on the transforms.Bug fixes
[ops] Fix for deformable convolution kernels always running on default stream (#9522)
[ops, MPS] Fix gradient overaccumulation in ROI ops (#9563, #9510)
[transforms] Fix JPEG transform for non-contiguous batches (#9615)
Contributors
🎉 We're grateful for our community, which helps us improve Torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:
Adrian Abeyta, Andrey Talman, Dmitry Nikolaev, Irakli Salia, Jeff Daily , Kasra Ghodsi, Nicolas Hug, Nikita Shulga, Simon Byrne, Yutao Xu, Zhewen
v0.28.0: TorchVision 0.28.0 ReleaseCompare Source
TorchVision 0.28 is out with some small enhancement and bug-fixes:
Enhancements
Bug fixes
F.resizeontv_tensors.Maskto honorNEAREST_EXACTinterpolation. Previously theinterpolationargument was ignored for mask inputs (resize_maskhardcodedNEAREST), soNEAREST_EXACTsilently produced plainNEARESToutput (#9497)Contributors
🎉 We're grateful for our community, which helps us improve Torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:
Andrey Talman, Benson Ma, Jason Fried, Joanne Yun, Nicolas Hug
v0.27.1: TorchVision 0.27.1 ReleaseCompare Source
This is a patch release, which is compatible with PyTorch 2.12.1. There are no new features added.
v0.27.0: TorchVision 0.27 ReleaseCompare Source
TorchVision 0.27 is out! This is a small release where the main improvement is the addition of the popular lanczos interpolation mode for the
v2.Resizetransform on CPU. Results are equivalent to PIL's, but you can expect TorchVision to be faster as it leverages AVX2 (on x86) and NEON paths (on ARM).Improvements
[transforms] Add support for lanczos interpolation mode (#9459)
[transforms] Drastically speed-up
Resizeon NEON ARM (#9439)[ops] Vectorize masks_to_boxes for performance (#9358)
[ops, transforms] Add direct XYWH-CXCYWH conversion for better performance (#9326)
[datasets] torchvision.datasets.voc: update dataset and project site URLs (#9216)
[ops] Add support for rotated boxes in
box_iou(#9404, #9379)[ops][MPS] Improve runtime complexity of
roi_align(#9100)[Code quality] #9359, #9364, #9359, #9317, #9409, #9408, #9416, #9411, #4463, #9475, #9427, #9448, #9443, #9396, #9316, #9286, #9324, #9338, #9381, #9386
[Documentation] #9339, #9351, #9323, #9374, #9412, #9378, #9428, #9431, #9474, #9472, #9463, #9440, #9385, #9327, #9334, #8879, #9350, #9392
Bug Fixes
[transforms] Fix incorrect normalization axis in
v2.ElasticTransform(#9300)[transforms] Fix: add clamping to avoid v2.ElasticTransform IndexError when bbox equals canvas size (#9436)
[transforms] Fix
tv_tensors.wrapto preserve subclass types forBoundingBoxesandKeyPoints(#9332)[transforms] Fix CXCYWH to XYXY conversion for integer bounding boxes (#9322)
[ops] Fix masks_to_boxes for empty masks (#9357)
Contributors
🎉 We're grateful for our community, which helps us improve Torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:
Andrew Strelsky, Andrey Talman, David Miguel Susano Pinto, fruet, Joan Salvà Soler, jsalvasoler , Look001122, MPSFuzz , mselim00, Murat Raimbekov, Nicolas Hug , Nikita Shulga, Pierre Moulon, ribbon-otter, Richard Barnes, shrianshChari, Timon Erhart, Ting Lu, Wei Shan Sun, Zhitao Yu
v0.26.0: TorchVision 0.26 ReleaseCompare Source
TorchVision 0.26 is out! It is compatible with torch 2.11. It's a small release that comes with the following changes:
Breaking changes and deprecations
The video decoding and encoding utilities of TorchVision, which have been deprecate for a long time, are now removed. This includes
torchvision.io.video.*,read_video,write_video, theVideoReaderclass, etc. Users are encouraged to switch to TorchCodec, which is faster and more stable.The rare torchvision utilities that were still relying on video decoding (like the video datasets) have been transparently migrated to TorchCodec.
Note: the image decoders and encoders are staying in TorchVision.
(#9341, #9421, #9370, #9366)
Improvements
[ops] Speed up masks_to_boxes on CPU and GPU (#9358)
[ops] Improve runtime complexity of roi_align on MPS (#9100)
Various code quality improvements (#8760, #9364, #9317, #9359, #9334, #9286, #9327)
Various documentation improvements (#9339, #9374, #9323, #9324, #8879, #9350)
Bug Fixes
[transforms] Fix edge case conversion from CXCYWH to XYXY for integer bounding boxes in F.convert_bounding_box_format (#9322)
[transforms] Fix tv_tensors.wrap to preserve subclass types for BoundingBoxes and KeyPoints (#9332)
[transforms] Fix incorrect normalization axis in v2.ElasticTransform (#9300)
[ops] Fix masks_to_boxes for empty masks (#9357)
[io] Fix CPU jpeg and png decoder/encoder error-path leak on malformed inputs (#9434)
Contributors
🎉 We're grateful for our community, which helps us improve Torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:
Adam J. Stewart, Andrey Talman, Jaebeom, MPSFuzz , Murat Raimbekov, Nicolas Hug, ribbon-otter , Roy Hvaara, Salman Chishti, Scott Todd, Zhitao Yu
v0.25.0: TorchVision 0.25 ReleaseCompare Source
TorchVision 0.25 is out! It is compatible with torch 2.10. It's a small release that comes with the following improvements:
Enhancement
[transforms] KeyPoints aren't clamped by default anymore after a transform. This is a bug-fix that comes with a change of behavior. We also added the
SanitizeKeyPointstransform to remove keypoints outside of the image area (#9236, #9235)[utils]
draw_bounding_boxesnow supports alabel_background_colorsparameter (#9204)[io] Fixed an issue in the GIF decoder (
decode_gif,decode_image) which affected some (not all) animated GIFs. (#9241)[misc] Various code-quality and docs improvements (#9218, #9270, #9250, #9247)
Contributors
🎉 We're grateful for our community, which helps us improve Torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:
Andrei Moraru, Andrey Talman, Antoine Simoulin , Arun Prakash A, Björn Barz, Huy Do, Nicolas Hug, Sean Gilligan, Wes Castro, Zhitao Yu
v0.24.1: TorchVision 0.24.1 ReleaseCompare Source
This is a patch release, which is compatible with PyTorch 2.9.1. There are no new features added.
v0.24.0: Torchvision 0.24 releaseCompare Source
Improving KeyPoints and Rotated boxes support!
We are releasing a tutorial on how to use
KeyPointtransformations in our Transforms on KeyPoints with a preview below!Detailed changes
Improvements
[ops] Improve efficiency of the
box_areaandbox_ioufunctions by eliminating the intermediate to "xyxy" conversion (#8992)[ops] Update box operations to support arbitrary batch dimensions (#9058)
[utils] Add control for the background color of label text boxes (#9204)
[transforms] Add support for uint8 image format to the GaussianNoise transform (#9169)
[transforms] Accelerate the
resizetransform on machines with AVX512 (#9190)[transforms] Better error handling in
RandomApplyfor empty list of transforms (#9130)[documentation] New tutorial for
KeyPointstransforms (#9209)[documentation] Various documentation improvements (#9186, #9180, #9172)
[code quality] Various code quality improvements (#9193, #9161, #9201, #9218, #9160)
Bug Fixes and deprecations
[transforms] Fix output of some geometric transforms for rotated boxes (#9181, #9175)
[transforms] Fix clamping for key points and add sanitization feature (#9236, #9235)
[datasets] Update download links to official repo for the Caltech-101 & 256 datasets (#9205)
[ops] Raise error in
drop_block[2,3]dby enforcing odd-sized block sizes (#9157)[io] Removed deprecated
video_readervideo decoding backend. (#9208)Contributors
🎉 We're grateful for our community, which helps us improve Torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release: @alperenunlu, @AndreiMoraru123, @atalman, @AntoineSimoulin, @5had3z, @dcasbol, @GdoongMathew, @hrsvrn, @JonasKlotz, @zklaus, @NicolasHug, @rdong8, @scotts, @get9, @diaz-esparza, @ZainRizvi, @Callidior, and @pytorch/xla-devs
v0.23.0: Torchvision 0.23 releaseCompare Source
Highlight - Transforming KeyPoints and Rotated boxes!
📦 This release is introducing two highly popular feature requests: Transforms support for KeyPoints and Rotated Bounding Boxes!
We illustrated the use of Rotated Bounding Boxes below. You can expect keypoints and rotated boxes to work with all existing torchvision transforms in
torchvision.transforms.v2. You can find some examples on how to use those transformations in our Transforms on Rotated Bounding Boxes tutorials.Detailed changes
New Features
[transforms] Added support for
BoundingBoxesformats and transforms (#9104, #9084, #9095, #9138)[transforms] Added the
KeyPointstoTVTensorand support for transforms (#8817)Improvements
[utils] Add label background to
draw_bounding_boxes(#9018)[MPS] Add deformable conv2d kernel support on MPS (#9017, #9115)
[documentation] Various documentation improvements (#9063, #9119, #9083, #9105, #9106, #9145)
[code-quality] Bunch of code quality improvements (#9087, #9093, #8814, #9035, #9120, #9080, #9027, #9062, #9117, #9024, #9032)
Bug Fixes
[datasets] Fix COCO dataset to avoid issue when copying the dataset results (#9107)
[datasets] Raise error when
download=Truefor LFW dataset, which is not available for download anymore #9040)[tv_tensors] Add error message when setting 1D tensor
ToImage()(#9114)[io] Warn when webp is asked to decode into grayscale (#9101)
Contributors
🎉 We're grateful for our community, which helps us improve Torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release: @AlannaBurke, @Alexandre-SCHOEPP, @atalman, @AntoineSimoulin, @BoyuanFeng, @cyyever, @elmuz, @emmanuel-ferdman, @hmk114, @Isalia20, @NicolasHug, @malfet, @chengolivia, @RhutvikH, @hvaara, @scotts, @alinpahontu2912, @tsahiasher, and @youcefouadjer.
v0.22.1: TorchVision 0.22.1 ReleaseCompare Source
Key info
This is a patch release, which is compatible with PyTorch 2.7.1. There are no new features added.
v0.22.0: Torchvision 0.22 releaseCompare Source
Key info
Detailed Changes
Deprecations
[io] Video decoding and encoding capabilities are deprecated and will be removed soon in 0.25! Please migrate to TorchCodec! (#8997)
Bug Fixes
[io] Fix sync bug with
encode_jpegon CUDA (#8929)[transforms]
pin_memory()now preservesTVTensorclass and metadata (#8921)Improvements
[datasets] Most datasets now support a
loaderparameter, which allow you to decode images directly into tensors withtorchvision.io.decode_image(), instead of relying on PIL. This should lead to faster pipelines! (#8945, #8972, #8939, #8922)[datasets] Add
classesattribute to theFlowers102dataset (#8838)[datasets] Added 'test' split support for Places365 dataset (#8928)
[datasets] Reduce output log on MNIST (#8865)
[ops] Perf: greatly speed-up NMS on CUDA when
num_boxesis high (#8766, #8925)[ops] Add
roi_alignnondeterministic support for XPU (#8931)[all] Improvements on input checks and error messages (#8959, #8994, #8944, #8995, #8993, #8866, #8882, #8851, #8844, #8991)
[build] Various build improvements / platforms support (#8913, #8933, #8936, #8792)
[docs] Various documentation improvements (#8843, #8860, #9014, #9015, #8932)
[misc] Other non-user-facing changes (#8872, #8982, #8976, #8935, #8977, #8978, #8963, #8975, #8974, #8950, #8970, #8924, #8964, #8996, #8920, #8873, #8876, #8885, #8890, #8901, #8999, #8998, #8973, #8897, #9007, #8852)
Contributors
We're grateful for our community, which helps us improve torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:
Aditya Kamath, Alexandre Ghelfi, PhD, Alfredo Tupone, amdfaa, Andrey Talman, Antoine Simoulin, Aurélien Geron, bjarzemb, deekay42, Frost Mitchell, frost-intel , GdoongMathew, Hangxing Wei, Huy Do, Nicolas Hug, Nikita Shulga, Noopur, Ruben, tvukovic-amd, Wenchen Li, Wieland Morgenstern , Yichen Yan, Yonghye Kwon, Zain Rizvi
v0.21.0: Torchvision 0.21 releaseCompare Source
Highlights
Image decoding
Torchvision continues to improve its image decoding capabilities. For this version, we added support for HEIC and AVIF image formats. Things are a bit different this time: to enable it, you'll need to
pip install torchvision-extra-decoders, and the decoders are available in torchvision astorchvision.io.decode_heic()andtorchvision.io.decode_avif(). This is still experimental / BETA, so let us know if you encounter any issue.Read more in our docs!
Detailed changes
New Features
[io] Add support for decoding AVIF and HEIC image formats (#8671)
Improvements
[datasets] Don't error when dataset is already downloaded (#8691)
[datasets] Don't print when dataset is already downloaded (#8681)
[datasets] remove printing info in datasets (#8683)
[utils] Add
label_colorsargument todraw_bounding_boxes(#8578)[models] Add
__deepcopy__support forDualGraphModule(#8708)[Docs] Various documentation improvements (#8798, #8709, #8576, #8620, #8846, #8758)
[Code quality] Various code quality improvements (#8757, #8755, #8754, #8689, #8719, #8772, #8774, #8791, #8705)
Bug Fixes
[io] Fix memory leak in
decode_webp(#8712)[io] Fix pyav 14 compatibility error (#8776)
[models] Fix order of auxiliary networks in googlenet.py (#8743)
[transforms] Fix
adjust_hueon ARM (#8618)[reference scripts] Fix error when loading the cached dataset in video classification reference(#8727)
[build] fix CUDA build with NVCC_FLAGS in env (#8692)
Tracked Regressions
[build] aarch64 builds are build with manylinux_2_34_aarch64 tag according to auditwheel check (#8883)
Contributors
We're grateful for our community, which helps us improve torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:
amdfaa Andreas Floros, Andrey Talman , Beh Chuen Yang, David Miguel Susano Pinto, GdoongMathew, Jason Chou, Li-Huai (Allan) Lin, Maohua Li, Nicolas Hug , pblwk, R. Yao, sclarkson, vfdev, Ștefan Talpalaru
v0.20.1Compare Source
v0.20.0: Torchvision 0.20 releaseCompare Source
Highlights
Encoding / Decoding images
Torchvision is further extending its encoding/decoding capabilities. For this version, we added a WEBP decoder, and a batch JPEG decoder on CUDA GPUs, which can lead to 10X speed-ups over CPU decoding.
We have also improved the UX of our decoding APIs to be more user-friendly. The main entry point is now
torchvision.io.decode_image(), and it can take as input either a path (as str orpathlib.Path), or a tensor containing the raw encoded data.Read more on the docs!
We also added support for HEIC and AVIF decoding, but these are currently only available when building from source. We are working on making those available directly in the upcoming releases. Stay tuned!
Detailed changes
Bug Fixes
[datasets] Update URL of SBDataset train_noval (#8551)
[datasets] EuroSAT: fix SSL certificate issues (#8563)
[io] Check average_rate availability in video reader (#8548)
New Features
[io] Add batch JPEG GPU decoding (
decode_jpeg()) (#8496)[io] Add WEBP image decoder:
decode_image(),decode_webp()(#8527, #8612, #8610)[io] Add HEIC and AVIF decoders, only available when building from source (#8597, #8596, #8647, #8613, #8621)
Improvements
[io] Add support for decoding 16bits png (#8524)
[io] Allow decoding functions to accept the mode parameter as a string (#8627)
[io] Allow
decode_image()to support paths (#8624)[io] Automatically send video to CPU in io.write_video (#8537)
[datasets] Better progress bar for file downloading (#8556)
[datasets] Add Path type annotation for ImageFolder (#8526)
[ops] Register nms and roi_align Autocast policy for PyTorch Intel GPU backend (#8541)
[transforms] Use Sequence for parameters type checking in
transforms.RandomErase(#8615)[transforms] Support
v2.functional.gaussian_blurbackprop (#8486)[transforms] Expose
transforms.v2utils for writing custom transforms. (#8670)[utils] Fix f-string in color error message (#8639)
[packaging] Revamped and improved debuggability of setup.py build (#8535, #8581, #8581, #8582, #8590, #8533, #8528, #8659)
[Documentation] Various documentation improvements (#8605, #8611, #8506, #8507, #8539, #8512, #8513, #8583, #8633)
[tests] Various tests improvements (#8580, #8553, #8523, #8617, #8518, #8579, #8558, #8617, #8641)
[code quality] Various code quality improvements (#8552, #8555, #8516, #8526, #8602, #8615, #8639, #8532)
[ci] #8562, #8644, #8592, #8542, #8594, #8530, #8656
Contributors
We're grateful for our community, which helps us improve torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:
Adam J. Stewart, AJS Payne, Andreas Floros, Andrey Talman, Bhavay Malhotra, Brizar, deekay42, Ehsan, Feng Yuan, Joseph Macaranas, Martin, Masahiro Hiramori, Nicolas Hug, Nikita Shulga , Sergii Dymchenko, Stefan Baumann, venkatram-dev, Wang, Chuanqi
v0.19.1: TorchVision 0.19.1 ReleaseCompare Source
This is a patch release, which is compatible with PyTorch 2.4.1. There are no new features added.
v0.19.0: Torchvision 0.19 releaseCompare Source
Highlights
Encoding / Decoding images
Torchvision is extending its encoding/decoding capabilities. For this version, we added a GIF decoder which is available as
torchvision.io.decode_gif(raw_tensor),torchvision.io.decode_image(raw_tensor), andtorchvision.io.read_image(path_to_image).We also added support for jpeg GPU encoding in
torchvision.io.encode_jpeg(). This is 10X faster than the existing CPU jpeg encoder.Read more on the docs!
Stay tuned for more improvements coming in the next versions. We plan to improve jpeg GPU decoding, and add more image decoders (webp in particular).
Resizing according to the longest edge of an image
It is now possible to resize images by setting
torchvision.transforms.v2.Resize(max_size=N): this will resize the longest edge of the image exactly tomax_size, making sure the image dimension don't exceed this value. Read more on the docs!Detailed changes
Bug Fixes
[datasets]
SBDataset: Only download noval file when image_set='train_noval' (#8475)[datasets] Update the download url in class
EMNIST(#8350)[io] Fix compilation error when there is no
libjpeg(#8342)[reference scripts] Fix use of
cutmix_alphain classification training references (#8448)[utils] Allow
K=1indraw_keypoints(#8439)New Features
[io] Add decoder for GIF images (
decode_gif(),decode_image(),read_image()) (#8406, #8419)[transforms] Add
GaussianNoisetransform (#8381)Improvements
[transforms] Allow v2
Resizeto resize longer edge exactly tomax_size(#8459)[transforms] Add
min_areaparameter toSanitizeBoundingBox(#7735)[transforms] Make
adjust_hue()work withnumpy 2.0(#8463)[transforms] Enable one-hot-encoded labels in
MixUpandCutMix(#8427)[transforms] Create kernel on-device for
transforms.functional.gaussian_blur(#8426)[io] Adding GPU acceleration to
encode_jpeg(10X faster than CPU encoder) (#8391)[io]
read_video: acceptBytesIOobjects onpyavbackend (#8442)[io] Add compatibility with FFMPEG 7.0 (#8408)
[datasets] Add extra to install
gdown(#8430)[datasets] Support encoded
RLEformat in forCOCOsegmentations (#8387)[datasets] Added binary cat vs dog classification target type to Oxford pet dataset (#8388)
[datasets] Return labels for
FER2013if possible (#8452)[ops] Force use of
torch.compileon deterministicroi_alignimplementation (#8436)[utils] add float support to
utils.draw_bounding_boxes()(#8328)[feature_extraction] Add concrete_args to feature extraction tracing. (#8393)
[Docs] Various documentation improvements (#8429, #8467, #8469, #8332, #8262, #8341, #8392, #8386, #8385, #8411).
[Tests] Various testing improvements (#8454, #8418, #8480, #8455)
[Code quality] Various code quality improvements (#8404, #8402, #8345, #8335, #8481, #8334, #8384, #8451, #8470, #8413, #8414, #8416, #8412)
Contributors
We're grateful for our community, which helps us improve torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:
Adam J. Stewart ahmadsharif1, AJS Payne, Andrew Lingg, Andrey Talman, Anner, Antoine Broyelle, cdzhan, deekay42, drhead, Edward Z. Yang, Emin Orhan, Fangjun Kuang, G, haarisr, Huy Do, Jack Newsom, JavaZero, Mahdi Lamb, Mantas, Nicolas Hug, Nicolas Hug , nihui, Richard Barnes , Richard Zou, Richie Bendall, Robert-André Mauchin, Ross Wightman, Siddarth Ijju, vfdev
v0.18.1: TorchVision 0.18.1 ReleaseCompare Source
This is a patch release, which is compatible with [PyTorch
Configuration
📅 Schedule: (UTC)
🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.
♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.
🔕 Ignore: Close this PR and you won't be reminded about this update again.
This PR was generated by Mend Renovate. View the repository job log.