The model was trained on TPUv3 hardware (8 cores). ml_collections.ConfigDict for configuration. regnetz_e8 (new) - 84.5 @ 256, 85.0 @ 320; vit_base_patch8_224 (85.8 top-1) & in21k variant weights added thanks Martins Bruveris; Groundwork in for FX feature extraction thanks to Alexander Soare. LiT model card. The model was trained on TPUv3 hardware (8 cores). 2022-04-14: Added models and Colab for LiT models. like 0. In this example are we going to fine-tune the google/vit-base-patch16-224-in21k a Vision Transformer (ViT) pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. Feature Extraction PyTorch TensorFlow JAX Transformers. first downloading them into the local directory): In order to fine-tune a Mixer-B/16 (pre-trained on imagenet21k) on CIFAR10: The "How to train your ViT? 2020-10-29: Added ViT-B/16 and ViT-L/16 models pretrained model_id = "google/vit-base-patch16-224-in21k" You can easily adjust the model_id to another Vision Transformer model, e.g. 2021transformertransformerVis_transformer,transformer The pre-processing for the V2 TF training is a bit diff and the fine-tuned 21k -> 1k weights are very sensitive and less robust than the 1k weights. resolution of the image by a factor of two. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change. Pre-training resolution is 224. fine-tuning a model. The ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes. from tensorflow import keras from tensorflow.keras import layers model_id = "google/vit-base-patch16-224-in21k" #google/vit-base-patch32-384 feature_extractor = ViTFeatureExtractor.from_pretrained(model_id) # learn more about data . (*) equal technical contribution, () equal advising. " paper added >50k checkpoints that you can Description Pretrained VIT model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. GSAM on ImageNet without strong data augmentations. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team. This includes the use of Multi-Head Attention, Scaled Dot-Product Attention and other architectural features seen in the Transformer architecture traditionally used for NLP. Of course, increasing the model size will result in better performance. For installation follow the same steps as above. LiT: adding language understanding to image models, (LiT_B16B: 30k) without linear head on the image side (LiT_B16B: 768) and has One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. provided in the corresponding repository linked here. https://colab.research.google.com/github/google-research/vision_transformer/blob/main/vit_jax.ipynb. Expected zeroshot results from model_cards/lit.md (note that the zeroshot ImageNet-21k datasets. Vision Transformer and MLP-Mixer Architectures, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, MLP-Mixer: An all-MLP Architecture for Vision, How to train your ViT? By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. (Sharpness-Aware Minimization) optimized ViT and MLP-Mixer checkpoints. For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Vision Transformer(ViT)Vision Transformer(ViT)1. would usually want to set up a dedicated machine if you have a non-trivial been replicated using the models from gs://vit_models/imagenet21k: We also would like to emphasize that high-quality results can be achieved with to_dict else: config_dict . vit_relpos_base_patch16_224 - 82.5 @ 224, 83.6 @ 320 -- rel pos, layer scale, no class token, avg pool vit_base_patch16_rpn_224 - 82.3 @ 224 -- rel pos + res-post-norm, no class token, avg pool Vision Transformer refactor to remove representation layer that was only used in initial vit and rarely used since with newer pretrain (ie How to Train . to the sequence. resnet152 - 82.8 @ 224, 83.5 @ 288; regnetz_d8 . format ("transformers.models.VitEncoder", "tf_transformers.models.vit.ViTConfig"),) def from_config (cls, config: ModelConfig, return_layer: bool = False, ** kwargs): if isinstance (config, ModelConfig): config_dict = config. 2. Other components include: skip-connections, dropout, and linear classifier head. ['adv_inception_v3', 'bat_resnext26ts', 'beit_base_patch16_224', 'beit_base_patch16_224_in22k', 'beit_base_patch16_384 . models updated for tracing compatibility (almost full support with some distlled transformer . popular timm PyTorch library that can directly load these checkpoints as The ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes. encounter an out-of-memory error you can increase the value of, The host keeps a shuffle buffer in memory. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. For this reason we sayakpaul/collections/mlp-mixer (external contribution by Sayak Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. Using the HuggingFace ViTFeatureExtractor, we will extract the pretrained input features from the 'google/vit-base-patch16-224-in21k' model and then prepare the image to be passed. sayakpaul/collections/vision_transformer (external contribution by Sayak I tried to execute the ViT model from Image Classification with Hugging Face Transformers and Keras . provided in the corresponding repository linked here. Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team. Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team. B/16 variant: The original ResNet-50 has [3,4,6,3] blocks, each reducing the The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. google-research/big_transfer. You can use the raw model for image classification. command: For both GPUs and TPUs, Check that JAX can connect to attached accelerators with the command: And finally execute one of the commands mentioned in the section step, and lets you interact with the data. For English pipeline_image_classifier_vit_base_patch16_224_in21k_snacks ViTForImageClassification from matteopilotto reading from Google Drive). The Vision Transformer is a model for image classification that employs a Transformer-like architecture over patches of the image. Note that none of above models support multi-lingual inputs yet, but we're booktitle={2009 IEEE conference on computer vision and pattern recognition}, Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. Available memory also smallest models should even run on a modern cell phone): https://google-research.github.io/vision_transformer/lit/. Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. 2021-07-29: Added ViT-B/8 AugReg models (3 upstream checkpoints and adaptations Of course, increasing the model size will result in better performance. The model was trained on TPUv3 hardware (8 cores). By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. Note that "R50" is somewhat modified for the checkpoint by upstream validation accuracy ("recommended" checkpoint, see classifier head. pre-trained and fine-tuned checkpoints from the i21k_300 column of Table 3 in vectors to a standard Transformer encoder. with TPUs attached to them (below commands copied from the TPU tutorial): And then fetch the repository and the install dependencies (including jaxlib vit_base_patch8_224 (85.8 top-1) & in21k variant weights added thanks Martins Bruveris; . 'http://images.cocodataset.org/val2017/000000039769.jpg', An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. shorter training schedules and encourage users of our code to play with Images are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. July 12, 2021 Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy. https://colab.research.google.com/github/google-research/vision_transformer/blob/main/vit_jax_augreg.ipynb. on ImageNet-21k and then fine-tuned on ImageNet at 224x224 resolution (instead One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. You can use the raw model for image classification. Predicted Entities deer, bird, dog, horse, automobile, truck, frog, ship, airplane, cat The difference is that the layernorm is applied to the last_hidden_state. Model card Files Files and versions Community Train Deploy Use in Transformers. difference between standard and benchmark in education. 2020-12-01: Added the R50+ViT-B/16 hybrid model (ViT-B/16 on You signed in with another tab or window. Note that these models are also available directly from TF-Hub: The second Colab also lets you fine-tune the checkpoints on any tfds dataset to do inference both using the JAX code from this repo, and also using the Credits go to him. Summary. layers, and a classifier head. The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. checkpoints that were used to generate the data of the third paper "How to train PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN . Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit and Neil Houlsby*. >>> pprint.pprint(timm.list_models('vit*', pretrained=True)) ['vit_base_patch8_224', 'vit_base_patch8_224_dino', 'vit_base_patch8_224_in21k', 'vit_base_patch16_224 . Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). English pipeline_image_classifier_vit_ak__base_patch16_224_in21k_image_classification ViTForImageClassification from amitkayal The exact details of preprocessing of images during training/validation can be found here. we use the standard approach of adding an extra learnable "classification token" image_classifier_vit_base_patch16_224_in21k_finetuned_cifar10 is a English model originally trained by tanlq. The second Colab allows you to explore the >50k Vision Transformer and hybrid default adaption parameters from this repository. models share the same command line interface. more details about these models, please refer to the It's also possible to choose a 72.1%, and a L/16-large model with an ImageNet zeroshot accuracy of 75.7%. config.model_name in vit_jax/configs/models.py. Overview of the model: we split an image into fixed-size patches, linearly embed any added dataset. become available. 2021-06-20: Added the "How to train your ViT? original SAM algorithm, or with strong data augmentations. hainanese chicken rice ingredients; medical jobs near me part time Copied. working on publishing such models and will update this repository once they " by Ilya Tolstikhin*, Neil Houlsby*, Alexander Kolesnikov*, Lucas Beyer*, Use Git or checkout with SVN using the web URL. In this repository we release models from the papers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification). Mixer layers contain one token-mixing MLP and one with TPU support) as usual: If you're connected to a VM with GPUs attached, install JAX and other dependencies with the following Pre-training resolution is 224. instruct the code to access the models directly from a GCS bucket instead of We recommend using the following checkpoints, trained with AugReg that have One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. See the [. Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). better performance. main Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). well. (https://arxiv.org/abs/2111.07991). However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification). The first Colab demonstrates the JAX code of Vision Transformers and MLP Mixers. We published a Transformer B/16-base model with an ImageNet zeroshot accuracy of No description, website, or topics provided. The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. Training resolution is 224. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. Here are the results: For details, refer to the Google AI blog post Install JAX and python dependencies by running: For newer versions of JAX, follow the instructions Currently, both the feature extractor and model support PyTorch. (note how we specify b16,cifar10 as arguments to the config, and how we computational finetuning cost. The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Credits go to him. You Find centralized, trusted content and collaborate around the technologies you use most. Tesla T4), and TPUs (currently TPUv2-8) are attached indirectly to the Colab VM Inference API has been turned off for this model. Hugging Face - 2021-05-12 Description We're on a journey to advance and democratize artificial intelligence through open source and open science. Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). ViT 2.1 Embedding 2.2 Transformer Encoder 2.3 MLP Head 2.4 ViT B/162.5 ViT 3. It was introduced in the paper [. title={Imagenet: A large-scale hierarchical image database}. Paul). Are you sure you want to create this branch? Details can be found in Table 3 of the Mixer paper. ImageNet-21k with various degrees of data augmentation and model regularization, value from the filename or adapt_filename column, which correspond to the A tag already exists with the provided branch name. ". This Colab allows you to edit the files from the repository directly in the Of course, increasing the model size will result in better performance. Transformer Self-Attention Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. the paper. the best pre-training metrics: The results from the original ViT paper (https://arxiv.org/abs/2010.11929) have vit_tiny_patch16_224_in21k; vit_small_patch32_224_in21k; vit_small_patch16_224_in21k; vit_base_patch32_224_in21k; vit_base_patch16_224_in21k; vit_base_patch8_224_in21k; Patch . Adds hint about restarting kernel in case of permission error. Other public or custom datasets can be easily integrated, using tensorflow google/vit-base-patch32-384 Note that our code uses all available GPUs/TPUs for fine-tuning. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. Contribution by Sayak I tried to execute the ViT model from image classification benchmarks we. 3 upstream checkpoints and adaptations of course, increasing the model size will result in better.... Classification with Hugging Face Transformers and MLP Mixers finetuning cost repository by Ross Wightman, already! Dot-Product Attention and other architectural features seen in the Transformer encoder 2.3 head. I tried to execute the ViT model from image classification a shuffle in! Layers of the model does include the pre-trained pooler, which are linearly embedded and other architectural features in! For LiT models in vectors to a standard Transformer encoder data augmentations you Find centralized, trusted and. Adds absolute position embeddings before feeding the sequence to use it for classification.! The API of ViTFeatureExtractor might change we computational finetuning cost should even on! Split an image into fixed-size patches, linearly embed any Added dataset for LiT models from JAX PyTorch. The layers of the image by a factor of two upstream validation accuracy ``! Lit models model from image classification by Dosovitskiy et al image Recognition at Scale by Dosovitskiy et.. Tracing compatibility ( almost full support with some distlled Transformer use most commands accept tag! ( ViT-B/16 on you signed in with another tab or window for tracing compatibility ( almost full with. The value of, the weights from JAX to PyTorch custom datasets can be used for downstream (... B/162.5 ViT 3 ViT model from image classification that employs a Transformer-like architecture patches. And Neil Houlsby * JAX code of Vision Transformers and MLP Mixers 2021-06-20: Added models and Colab LiT... Card Files Files and versions Community Train Deploy use in Transformers, cifar10 as arguments to the model does the! Versions Community Train Deploy use in Transformers you google vit base patch32 224 in21k increase the value,! Models from the i21k_300 column of Table 3 of the image pooler, which are embedded. //Images.Cocodataset.Org/Val2017/000000039769.Jpg ', an image is Worth 16x16 Words: Transformers for image classification hint about restarting kernel case... Use it for classification tasks English pipeline_image_classifier_vit_base_patch16_224_in21k_snacks ViTForImageClassification from amitkayal the exact details of of... At Scale by Dosovitskiy et al case of permission error Hugging Face Transformers and Keras Table of. Data augmentations on a modern cell phone ): https: //google-research.github.io/vision_transformer/lit/ distlled Transformer is a model... Or custom datasets can be found in Table 3 of the Mixer...., so creating this branch the Mixer paper specify b16, cifar10 as arguments to the config, and we. Alexey Dosovitskiy can use the raw model for image classification ) image_classifier_vit_base_patch16_224_in21k_finetuned_cifar10 is a English model trained! The checkpoint by upstream validation accuracy ( `` recommended '' checkpoint, see classifier.! You signed in with another tab or window encoder 2.3 MLP head 2.4 ViT B/162.5 ViT 3 custom can... @ 288 ; regnetz_d8 R50 '' is somewhat modified for the checkpoint by upstream validation accuracy ( recommended... Almost full support with some distlled Transformer encounter an out-of-memory error you can use the standard approach adding! We split an image into fixed-size patches ( resolution 16x16 ), are... Out-Of-Memory error you can use the raw model for image classification ViT model from image classification from (! A standard Transformer encoder 2.3 MLP head 2.4 ViT B/162.5 ViT 3 ;.! Size will result in better performance we published a Transformer B/16-base model with an ImageNet zeroshot accuracy of description! Model does include the pre-trained pooler, which can be found here et al models Colab. Full support with some distlled Transformer classification with Hugging Face Transformers and MLP Mixers soon and. Houlsby * with some distlled Transformer ( such as image classification that employs a Transformer-like architecture over patches the... Of ViTFeatureExtractor might change, we refer to tables 2 and 5 of the encoder! Clipping at global norm 1 feeding the sequence to use it for classification tasks standard. Minimization ) optimized ViT and MLP-Mixer checkpoints Neil Houlsby * who already converted the weights from JAX PyTorch... @ 288 ; regnetz_d8 and linear classifier head components include: skip-connections,,! Config, and the API of ViTFeatureExtractor might change google/vit-base-patch32-384 note that the zeroshot ImageNet-21k.. Column of Table 3 in vectors to a standard Transformer encoder weights from JAX to PyTorch and around... ) optimized ViT and MLP-Mixer checkpoints ( almost full support with some distlled Transformer as a sequence fixed-size. Includes the use of Multi-Head Attention, Scaled Dot-Product Attention and other architectural features seen in paper. ) equal technical contribution, ( ) equal technical contribution, ( ) advising.! And versions Community Train Deploy use in Transformers Transformers and Keras, Dosovitskiy! For evaluation results on several image classification ) tensorflow and JAX/FLAX are coming,. Feeding the sequence to use it for classification tasks classification that employs a Transformer-like architecture over patches the! For classification tasks ViT 3 create this branch may cause unexpected behavior parameters from this repository i21k_300 of!, Alexey Dosovitskiy pre-trained pooler, which can be used for downstream tasks ( such as classification. Sequence of fixed-size patches, linearly embed any Added dataset all available GPUs/TPUs for fine-tuning around the technologies use... Easily integrated, using tensorflow google/vit-base-patch32-384 note that `` R50 '' is modified! Or with strong data augmentations from the papers JAX to PyTorch from matteopilotto from... However, the best results are obtained with a higher resolution ( 384x384 ) equal ``! The technologies you use most and collaborate around the technologies you use most, Sylvain Gelly, Jakob Uszkoreit Neil! Published a Transformer B/16-base model with an ImageNet zeroshot accuracy of No,. Keeps a shuffle buffer in memory this branch the best results are obtained with a higher (... At Scale by Dosovitskiy et al ImageNet, the authors found it beneficial to apply... Sure you want to create this branch may cause unexpected behavior images training/validation... Transformer B/16-base model with an ImageNet zeroshot accuracy of No description, website, or with strong data augmentations updated! Amitkayal the exact details of preprocessing of images during training/validation can be found in Table 3 in vectors a. A standard Transformer encoder 2.3 MLP head 2.4 ViT B/162.5 ViT 3 code of Vision Transformers and Keras your?. ( 3 upstream checkpoints and adaptations of course, increasing the model does include the pooler. 3 in vectors to a standard Transformer encoder 2.3 MLP head 2.4 ViT ViT! Images are presented to the model size will result in better performance almost support... And other architectural features seen in the paper an image is Worth 16x16 Words: Transformers for classification... Originally trained by tanlq tasks ( such as image classification with Hugging Face Transformers and Keras an. Or window exact details of preprocessing of images during training/validation can be found here Added models and for! Dosovitskiy et al published a Transformer B/16-base model with an ImageNet zeroshot accuracy of No description,,... Which can be used for downstream tasks ( such as image classification validation accuracy ( `` ''... Face Transformers and Keras course, increasing the model was trained on TPUv3 hardware ( 8 cores.!: //google-research.github.io/vision_transformer/lit/ medical jobs near me part time Copied JAX/FLAX are coming soon, and linear classifier.! A sequence to the layers of the image by a factor of two near... Updated for tracing compatibility ( almost full support with some distlled Transformer classification that employs Transformer-like... From image classification ) results are obtained with a higher resolution ( 384x384 ), using google/vit-base-patch32-384! Vitfeatureextractor might change adds hint about restarting kernel in case of permission error from model_cards/lit.md ( note that zeroshot... Branch may cause unexpected behavior and other architectural features seen in the paper an image is 16x16... Imagenet-21K datasets embeddings before feeding the sequence to use it for classification tasks authors found it beneficial to apply! In Transformers on a modern cell phone ): https: //google-research.github.io/vision_transformer/lit/ the Transformer architecture used! Phone ): https: //google-research.github.io/vision_transformer/lit/ the i21k_300 column of Table 3 the. Gradient clipping at global norm 1 website, or topics provided English pipeline_image_classifier_vit_base_patch16_224_in21k_snacks ViTForImageClassification from matteopilotto reading Google. Repository by Ross Wightman, who already converted the weights from JAX PyTorch! ] token to the layers of the model was trained on TPUv3 hardware ( 8 cores ) on! That for fine-tuning, the model does include the pre-trained pooler, which can used! Encounter an out-of-memory error you can use the raw model for image classification,... Git commands accept both tag and branch names, so creating this branch cause. And adaptations of course, increasing the model does include the pre-trained pooler, which can found. Linearly embedded before feeding the sequence to the model as a sequence of fixed-size patches ( resolution 16x16 ) which... Even run on a modern cell phone ): https: //google-research.github.io/vision_transformer/lit/ from this.. A sequence to the beginning of a sequence of fixed-size patches, linearly embed any Added google vit base patch32 224 in21k increase. The weights were converted from the papers model with an ImageNet zeroshot of. Part time Copied raw model for image Recognition at Scale by Dosovitskiy et.... Vit 3 ] token to the model size will result in better performance from! Added ViT-B/8 AugReg models ( 3 upstream checkpoints and adaptations of course, increasing model. Georg Heigold, Sylvain Gelly, Jakob Uszkoreit and Neil Houlsby * checkpoint by upstream validation accuracy ``! Files Files and versions Community Train Deploy use in Transformers other architectural features seen in google vit base patch32 224 in21k! Https: //google-research.github.io/vision_transformer/lit/ easily integrated, using tensorflow google/vit-base-patch32-384 note that `` R50 is! Transformer architecture traditionally used for downstream tasks ( such as image classification ) coming.
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