model_id stringlengths 12 92 | model_card stringlengths 166 900k | model_labels listlengths 2 250 |
|---|---|---|
facebook/mask2former-swin-tiny-ade-semantic |
# Mask2Former
Mask2Former model trained on ADE20k semantic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation
](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearc... | [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror... |
facebook/mask2former-swin-base-IN21k-cityscapes-instance |
# Mask2Former
Mask2Former model trained on Cityscapes instance segmentation (base-IN21k version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation
](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookres... | [
"person",
"rider",
"car",
"truck",
"bus",
"train",
"motorcycle",
"bicycle"
] |
facebook/mask2former-swin-large-cityscapes-instance |
# Mask2Former
Mask2Former model trained on Cityscapes instance segmentation (large-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation
](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookre... | [
"person",
"rider",
"car",
"truck",
"bus",
"train",
"motorcycle",
"bicycle"
] |
facebook/mask2former-swin-base-IN21k-cityscapes-panoptic |
# Mask2Former
Mask2Former model trained on Cityscapes panoptic segmentation (base-IN21k version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation
](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookres... | [
"road",
"sidewalk",
"building",
"wall",
"fence",
"pole",
"traffic light",
"traffic sign",
"vegetation",
"terrain",
"sky",
"person",
"rider",
"car",
"truck",
"bus",
"train",
"motorcycle",
"bicycle"
] |
facebook/mask2former-swin-small-cityscapes-semantic |
# Mask2Former
Mask2Former model trained on Cityscapes semantic segmentation (small-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation
](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookre... | [
"road",
"sidewalk",
"building",
"wall",
"fence",
"pole",
"traffic light",
"traffic sign",
"vegetation",
"terrain",
"sky",
"person",
"rider",
"car",
"truck",
"bus",
"train",
"motorcycle",
"bicycle"
] |
facebook/mask2former-swin-tiny-cityscapes-semantic |
# Mask2Former
Mask2Former model trained on Cityscapes semantic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation
](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookres... | [
"road",
"sidewalk",
"building",
"wall",
"fence",
"pole",
"traffic light",
"traffic sign",
"vegetation",
"terrain",
"sky",
"person",
"rider",
"car",
"truck",
"bus",
"train",
"motorcycle",
"bicycle"
] |
Xpitfire/segformer-finetuned-segments-cmp-facade |
**Semantic segmentation** is the task of classifying each pixel in an image to a corresponding category. It has a wide range of use cases in fields such as medical imaging, autonomous driving, robotics, etc. For the facade dataset we are interested to classify the front-view of buildings based on 12 distinct classes. ... | [
"unknown",
"background",
"facade",
"window",
"door",
"cornice",
"sill",
"balcony",
"blind",
"molding",
"deco",
"pillar",
"shop"
] |
openmmlab/upernet-convnext-tiny |
# UperNet, ConvNeXt tiny-sized backbone
UperNet framework for semantic segmentation, leveraging a ConvNeXt backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a ConvNeXt backbone was introduced in t... | [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror... |
openmmlab/upernet-convnext-small |
# UperNet, ConvNeXt small-sized backbone
UperNet framework for semantic segmentation, leveraging a ConvNeXt backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a ConvNeXt backbone was introduced in ... | [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror... |
openmmlab/upernet-convnext-base |
# UperNet, ConvNeXt base-sized backbone
UperNet framework for semantic segmentation, leveraging a ConvNeXt backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a ConvNeXt backbone was introduced in t... | [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror... |
openmmlab/upernet-convnext-large |
# UperNet, ConvNeXt large-sized backbone
UperNet framework for semantic segmentation, leveraging a ConvNeXt backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a ConvNeXt backbone was introduced in ... | [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror... |
openmmlab/upernet-convnext-xlarge |
# UperNet, ConvNeXt xlarge-sized backbone
UperNet framework for semantic segmentation, leveraging a ConvNeXt backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a ConvNeXt backbone was introduced in... | [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror... |
openmmlab/upernet-swin-tiny |
# UperNet, Swin Transformer tiny-sized backbone
UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a Swin Transformer back... | [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror... |
openmmlab/upernet-swin-small |
# UperNet, Swin Transformer small-sized backbone
UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a Swin Transformer bac... | [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror... |
openmmlab/upernet-swin-base |
# UperNet, Swin Transformer base-sized backbone
UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a Swin Transformer back... | [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror... |
openmmlab/upernet-swin-large |
# UperNet, Swin Transformer large-sized backbone
UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a Swin Transformer bac... | [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror... |
facebook/mask2former-swin-base-IN21k-cityscapes-semantic |
# Mask2Former
Mask2Former model trained on Cityscapes semantic segmentation (base-IN21k, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation
](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Ma... | [
"road",
"sidewalk",
"building",
"wall",
"fence",
"pole",
"traffic light",
"traffic sign",
"vegetation",
"terrain",
"sky",
"person",
"rider",
"car",
"truck",
"bus",
"train",
"motorcycle",
"bicycle"
] |
facebook/mask2former-swin-base-IN21k-coco-instance |
# Mask2Former
Mask2Former model trained on COCO instance segmentation (base-sized IN21k version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation
](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookres... | [
"person",
"bicycle",
"car",
"motorbike",
"aeroplane",
"bus",
"train",
"truck",
"boat",
"traffic light",
"fire hydrant",
"stop sign",
"parking meter",
"bench",
"bird",
"cat",
"dog",
"horse",
"sheep",
"cow",
"elephant",
"bear",
"zebra",
"giraffe",
"backpack",
"umbrell... |
andrewljohnson/segformer-b0-finetuned-magic-cards-230117 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-magic-cards-230117
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mi... | [
"unlabeled",
"front",
"back"
] |
andrewljohnson/segformer-b5-finetuned-magic-cards-230117 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b5-finetuned-magic-cards-230117
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mi... | [
"unlabeled",
"front",
"back"
] |
andrewljohnson/segformer-b5-finetuned-magic-cards-230117-2 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b5-finetuned-magic-cards-230117-2
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/... | [
"unlabeled",
"front",
"back"
] |
andrewljohnson/segformer-b5-finetuned-magic-cards-230117-3 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b5-finetuned-magic-cards-230117-3
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/... | [
"unlabeled",
"front",
"back"
] |
mraottth/trashbot_v1 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# trashbot
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the mraottth/all_locatio... | [
"unlabeled",
"trash"
] |
mraottth/trashbot |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# trashbot
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the mraottth/all_locatio... | [
"unlabeled",
"trash"
] |
s3nh/SegFormer-b0-person-segmentation |
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
<img src = 'https://images.unsplash.com/photo-1438761681033-6461ffad8d80?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80'>
... | [
"background",
"person"
] |
s3nh/SegFormer-b4-person-segmentation |
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
<img src = 'https://images.unsplash.com/photo-1438761681033-6461ffad8d80?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80'>
... | [
"background",
"person"
] |
s3nh/SegFormer-b5-person-segm |
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
<img src = 'https://images.unsplash.com/photo-1438761681033-6461ffad8d80?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80'>
... | [
"background",
"person"
] |
DunnBC22/mit-b0-CMP_semantic_seg_with_mps_v2 |
# mit-b0-CMP_semantic_seg_with_mps_v2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0).
It achieves the following results on the evaluation set:
- Loss: 1.0863
- Mean Iou: 0.4097
- Mean Accuracy: 0.5538
- Overall Accuracy: 0.6951
- Per Category Iou:
- Segment 0: 0.5921698... | [
"background",
"facade",
"window",
"door",
"cornice",
"sill",
"balcony",
"blind",
"molding",
"deco",
"pillar",
"shop"
] |
AlmogM/segformer-b0-finetuned-fish-almogm |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-fish-almogm
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) o... | [
"background",
"fish"
] |
yiming19/segformer-b0-finetuned-segments-construction-1 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-construction-1
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvid... | [
"unlabeled",
"ruler",
"socket",
"wall",
"window",
"heater",
"floor",
"ceiling",
"skirting",
"door",
"light"
] |
shivalikasingh/video-mask2former-swin-tiny-youtubevis-2021-instance |
# Video Mask2Former
Video Mask2Former model trained on YouTubeVIS-2021 instance segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764) and first released in [this repository](https://github.com/facebookresearch/... | [
"airplane",
"bear",
"bird",
"boat",
"car",
"cat",
"cow",
"deer",
"dog",
"duck",
"earless_seal",
"elephant",
"fish",
"flying_disc",
"fox",
"frog",
"giant_panda",
"giraffe",
"horse",
"leopard",
"lizard",
"monkey",
"motorbike",
"mouse",
"parrot",
"person",
"rabbit",
... |
shivalikasingh/video-mask2former-swin-tiny-youtubevis-2019-instance |
# Video Mask2Former
Video Mask2Former model trained on YouTubeVIS-2019 instance segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764) and first released in [this repository](https://github.com/facebookresearch/... | [
"person",
"giant_panda",
"lizard",
"parrot",
"skateboard",
"sedan",
"ape",
"dog",
"snake",
"monkey",
"hand",
"rabbit",
"duck",
"cat",
"cow",
"fish",
"train",
"horse",
"turtle",
"bear",
"motorbike",
"giraffe",
"leopard",
"fox",
"deer",
"owl",
"surfboard",
"airpla... |
shivalikasingh/video-mask2former-swin-small-youtubevis-2021-instance |
# Video Mask2Former
Video Mask2Former model trained on YouTubeVIS-2021 instance segmentation (small-sized version, Swin backbone). It was introduced in the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764) and first released in [this repository](https://github.com/facebookresearch... | [
"airplane",
"bear",
"bird",
"boat",
"car",
"cat",
"cow",
"deer",
"dog",
"duck",
"earless_seal",
"elephant",
"fish",
"flying_disc",
"fox",
"frog",
"giant_panda",
"giraffe",
"horse",
"leopard",
"lizard",
"monkey",
"motorbike",
"mouse",
"parrot",
"person",
"rabbit",
... |
shivalikasingh/video-mask2former-swin-base-IN21k-youtubevis-2021-instance |
# Video Mask2Former
Video Mask2Former model trained on YouTubeVIS-2021 instance segmentation (base-sized version, Swin backbone). It was introduced in the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764) and first released in [this repository](https://github.com/facebookresearch/... | [
"airplane",
"bear",
"bird",
"boat",
"car",
"cat",
"cow",
"deer",
"dog",
"duck",
"earless_seal",
"elephant",
"fish",
"flying_disc",
"fox",
"frog",
"giant_panda",
"giraffe",
"horse",
"leopard",
"lizard",
"monkey",
"motorbike",
"mouse",
"parrot",
"person",
"rabbit",
... |
shivalikasingh/video-mask2former-swin-large-youtubevis-2021-instance |
# Video Mask2Former
Video Mask2Former model trained on YouTubeVIS-2021 instance segmentation (large-sized version, Swin backbone). It was introduced in the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764) and first released in [this repository](https://github.com/facebookresearch... | [
"airplane",
"bear",
"bird",
"boat",
"car",
"cat",
"cow",
"deer",
"dog",
"duck",
"earless_seal",
"elephant",
"fish",
"flying_disc",
"fox",
"frog",
"giant_panda",
"giraffe",
"horse",
"leopard",
"lizard",
"monkey",
"motorbike",
"mouse",
"parrot",
"person",
"rabbit",
... |
shivalikasingh/video-mask2former-swin-small-youtubevis-2019-instance |
# Video Mask2Former
Video Mask2Former model trained on YouTubeVIS-2019 instance segmentation (small-sized version, Swin backbone). It was introduced in the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764) and first released in [this repository](https://github.com/facebookresearch... | [
"person",
"giant_panda",
"lizard",
"parrot",
"skateboard",
"sedan",
"ape",
"dog",
"snake",
"monkey",
"hand",
"rabbit",
"duck",
"cat",
"cow",
"fish",
"train",
"horse",
"turtle",
"bear",
"motorbike",
"giraffe",
"leopard",
"fox",
"deer",
"owl",
"surfboard",
"airpla... |
shivalikasingh/video-mask2former-swin-base-IN21k-youtubevis-2019-instance |
# Video Mask2Former
Video Mask2Former model trained on YouTubeVIS-2019 instance segmentation (base-sized version, Swin backbone). It was introduced in the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764) and first released in [this repository](https://github.com/facebookresearch/... | [
"person",
"giant_panda",
"lizard",
"parrot",
"skateboard",
"sedan",
"ape",
"dog",
"snake",
"monkey",
"hand",
"rabbit",
"duck",
"cat",
"cow",
"fish",
"train",
"horse",
"turtle",
"bear",
"motorbike",
"giraffe",
"leopard",
"fox",
"deer",
"owl",
"surfboard",
"airpla... |
shivalikasingh/video-mask2former-swin-large-youtubevis-2019-instance |
# Video Mask2Former
Video Mask2Former model trained on YouTubeVIS-2019 instance segmentation (large-sized version, Swin backbone). It was introduced in the paper [Mask2Former for Video Instance Segmentation
](https://arxiv.org/abs/2112.10764) and first released in [this repository](https://github.com/facebookresearch... | [
"person",
"giant_panda",
"lizard",
"parrot",
"skateboard",
"sedan",
"ape",
"dog",
"snake",
"monkey",
"hand",
"rabbit",
"duck",
"cat",
"cow",
"fish",
"train",
"horse",
"turtle",
"bear",
"motorbike",
"giraffe",
"leopard",
"fox",
"deer",
"owl",
"surfboard",
"airpla... |
alanoix/segformer_b0_flair_one |
# pretrained model
- https://huggingface.co/nvidia/mit-b0
- SegFormer (b0-sized) encoder pre-trained-only
- SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository... | [
"none",
"building",
"pervious surface",
"impervious surface",
"bare soil",
"water",
"coniferous",
"deciduous",
"brushwood",
"vineyard",
"herbaceous vegetation",
"agricultural land",
"plowed land"
] |
Efferbach/segformer-finetuned-lane-1k-steps |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-finetuned-lane-1k-steps
This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-cityscapes-512-1024](http... | [
"background",
"left",
"right"
] |
Efferbach/segformer-finetuned-lane-10k-steps |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-finetuned-lane-10k-steps
This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-cityscapes-512-1024](htt... | [
"background",
"left",
"right"
] |
Efferbach/mobilevit-small-10k-steps |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilevit-small-10k-steps
This model is a fine-tuned version of [apple/deeplabv3-mobilevit-small](https://huggingface.co/apple/d... | [
"background",
"left",
"right"
] |
Efferbach/mobilenet_v2_1-10k-steps |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilenet_v2_1-10k-steps
This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobil... | [
"background",
"left",
"right"
] |
zklee98/segformer-b1-solarModuleAnomaly-v0.1 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b1-solarModuleAnomaly-v0.1
This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1)... | [
"unlabelled",
"anomaly"
] |
mattmdjaga/segformer_b0_clothes | # Segformer B0 fine-tuned for clothes segmentation
SegFormer model fine-tuned on [ATR dataset](https://github.com/lemondan/HumanParsing-Dataset) for clothes segmentation.
The dataset on hugging face is called "mattmdjaga/human_parsing_dataset".
```python
from transformers import SegformerImageProcessor, AutoModelForS... | [
"background",
"hat",
"hair",
"sunglasses",
"upper-clothes",
"skirt",
"pants",
"dress",
"belt",
"left-shoe",
"right-shoe",
"face",
"left-leg",
"right-leg",
"left-arm",
"right-arm",
"bag",
"scarf"
] |
Onegafer/segformer-v-mesh-0 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-v-mesh-0
This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the Onegafer/v... | [
"background",
"windows"
] |
bilal01/segformer-b0-finetuned-segments-sidewalk-2 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-sidewalk-2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/m... | [
"unlabeled",
"flat-road",
"flat-sidewalk",
"flat-crosswalk",
"flat-cyclinglane",
"flat-parkingdriveway",
"flat-railtrack",
"flat-curb",
"human-person",
"human-rider",
"vehicle-car",
"vehicle-truck",
"vehicle-bus",
"vehicle-tramtrain",
"vehicle-motorcycle",
"vehicle-bicycle",
"vehicle... |
shehan97/mobilevitv2-1.0-voc-deeplabv3 |
# MobileViTv2 + DeepLabv3 (shehan97/mobilevitv2-1.0-voc-deeplabv3)
<!-- Provide a quick summary of what the model is/does. -->
MobileViTv2 model pre-trained on PASCAL VOC at resolution 512x512.
It was introduced in [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin M... | [
"background",
"aeroplane",
"bicycle",
"bird",
"boat",
"bottle",
"bus",
"car",
"cat",
"chair",
"cow",
"diningtable",
"dog",
"horse",
"motorbike",
"person",
"pottedplant",
"sheep",
"sofa",
"train",
"tvmonitor"
] |
matei-dorian/segformer-b0-finetuned-human-parsing |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-human-parsing
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0)... | [
"background",
"hat",
"hair",
"sunglasses",
"upper-clothes",
"skirt",
"pants",
"dress",
"belt",
"left-shoe",
"right-shoe",
"face",
"left-leg",
"right-leg",
"left-arm",
"right-arm",
"bag",
"scarf"
] |
matei-dorian/segformer-b5-finetuned-human-parsing |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b5-finetuned-human-parsing
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5)... | [
"background",
"hat",
"hair",
"sunglasses",
"upper-clothes",
"skirt",
"pants",
"dress",
"belt",
"left-shoe",
"right-shoe",
"face",
"left-leg",
"right-leg",
"left-arm",
"right-arm",
"bag",
"scarf"
] |
zho/segformer-finetuned-sidewalk-10k-steps |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-finetuned-sidewalk-10k-steps
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b... | [
"unlabeled",
"flat-road",
"flat-sidewalk",
"flat-crosswalk",
"flat-cyclinglane",
"flat-parkingdriveway",
"flat-railtrack",
"flat-curb",
"human-person",
"human-rider",
"vehicle-car",
"vehicle-truck",
"vehicle-bus",
"vehicle-tramtrain",
"vehicle-motorcycle",
"vehicle-bicycle",
"vehicle... |
millionhz/segformer-b0-finetuned-flame |
# SegFormer (b0-sized) model fine-tuned on FLAME
The model was trained for a deep learning project titled [Forest Fire Detection](https://github.com/millionhz/forest-fire-detection).
## Model Description
The model is intended to be used for fire detection through image segmentation.
The provided pretrained model w... | [
"background",
"fire"
] |
bilal01/segformer-b0-finetuned-segments-test |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-test
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0)... | [
"unlabeled",
"stamp"
] |
DiTo97/binarization-segformer-b3 |
# binarization-segformer-b3
This model is a fine-tuned version of [nvidia/segformer-b3-1024-1024](https://huggingface.co/nvidia/segformer-b3-finetuned-cityscapes-1024-1024)
on the same ensemble of 13 datasets as the [SauvolaNet](https://arxiv.org/pdf/2105.05521.pdf) work publicly available
in their GitHub [repositor... | [
"background",
"text"
] |
bilal01/segformer-b0-finetuned-segments-stamp-verification |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-stamp-verification
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/... | [
"unlabeled",
"stamp"
] |
edwardhuang/test-carbonate-segmentation2 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-carbonate-segmentation2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the ... | [
"micrite",
"cement",
"peloid/pellet/ooid",
"biotic",
"scale bar"
] |
apple/mobilevitv2-1.0-voc-deeplabv3 |
# MobileViTv2 + DeepLabv3 (shehan97/mobilevitv2-1.0-voc-deeplabv3)
<!-- Provide a quick summary of what the model is/does. -->
MobileViTv2 model pre-trained on PASCAL VOC at resolution 512x512.
It was introduced in [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin M... | [
"background",
"aeroplane",
"bicycle",
"bird",
"boat",
"bottle",
"bus",
"car",
"cat",
"chair",
"cow",
"diningtable",
"dog",
"horse",
"motorbike",
"person",
"pottedplant",
"sheep",
"sofa",
"train",
"tvmonitor"
] |
ArturR01/segformer-b0-example-pytorch-blog |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-example-pytorch-blog
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on... | [
"unlabeled",
"flat-road",
"flat-sidewalk",
"flat-crosswalk",
"flat-cyclinglane",
"flat-parkingdriveway",
"flat-railtrack",
"flat-curb",
"human-person",
"human-rider",
"vehicle-car",
"vehicle-truck",
"vehicle-bus",
"vehicle-tramtrain",
"vehicle-motorcycle",
"vehicle-bicycle",
"vehicle... |
thesisabc/segformer-b0-finetuned-segments-sidewalk-2 | # SegFormer (b0-sized) model fine-tuned on Segments.ai sidewalk-semantic.
SegFormer model fine-tuned on [Segments.ai](https://segments.ai) [`sidewalk-semantic`](https://huggingface.co/datasets/segments/sidewalk-semantic). It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation w... | [
"unlabeled",
"flat-road",
"flat-sidewalk",
"flat-crosswalk",
"flat-cyclinglane",
"flat-parkingdriveway",
"flat-railtrack",
"flat-curb",
"human-person",
"human-rider",
"vehicle-car",
"vehicle-truck",
"vehicle-bus",
"vehicle-tramtrain",
"vehicle-motorcycle",
"vehicle-bicycle",
"vehicle... |
vimassaru/segformer-b0-finetuned-teeth-segmentation |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-sidewalk-oct-22
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvi... | [
"background",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"41",
"42",
"43",
"44",
"45",
"46",
"47",
"48"
] |
YIMMYCRUZ/vit-model-ojas |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-model-ojas
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-... | [
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
iammartian0/RoadSense_High_Definition_Street_Segmentation |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-sidewalk
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit... | [
"unlabeled",
"flat-road",
"flat-sidewalk",
"flat-crosswalk",
"flat-cyclinglane",
"flat-parkingdriveway",
"flat-railtrack",
"flat-curb",
"human-person",
"human-rider",
"vehicle-car",
"vehicle-truck",
"vehicle-bus",
"vehicle-tramtrain",
"vehicle-motorcycle",
"vehicle-bicycle",
"vehicle... |
blzncz/segformer-finetuned-4ss1st3r_s3gs3m-10k-steps |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-finetuned-4ss1st3r_s3gs3m-10k-steps
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidi... | [
"bg",
"fallo cohesivo",
"fallo malla",
"fallo adhesivo",
"fallo burbuja"
] |
FashionAI4Wholesale/segformer-b2-finetuned-segments-dresses-071123 |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Mo... | [
"unlabeled",
"dress",
"mannequin",
"background"
] |
varcoder/segformer-b4-crack-segmentation-dataset |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b4-crack-segmentation-dataset
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-... | [
"background",
"crack"
] |
mccaly/test2 | # A Large-Scale Benchmark for Food Image Segmentation
By [Xiongwei Wu](http://xiongweiwu.github.io/), [Xin Fu](https://xinfu607.github.io/), Ying Liu, [Ee-Peng Lim](http://www.mysmu.edu/faculty/eplim/), [Steven C.H. Hoi](https://sites.google.com/view/stevenhoi/home/), [Qianru Sun](https://qianrusun.com/).
<div ali... | [
"background",
"candy",
"egg tart",
"french fries",
"chocolate",
"biscuit",
"popcorn",
"pudding",
"ice cream",
"cheese butter",
"cake",
"wine",
"milkshake",
"coffee",
"juice",
"milk",
"tea",
"almond",
"red beans",
"cashew",
"dried cranberries",
"soy",
"walnut",
"peanut",... |
Lexic0n/segformer-b0-finetuned-human-parsing |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-human-parsing
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0)... | [
"background",
"hat",
"hair",
"sunglasses",
"upper-clothes",
"skirt",
"pants",
"dress",
"belt",
"left-shoe",
"right-shoe",
"face",
"left-leg",
"right-leg",
"left-arm",
"right-arm",
"bag",
"scarf"
] |
Isaacks/segformer-finetuned-ihc |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-finetuned-ihc
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the Isaac... | [
"background",
"tissue"
] |
Isaacks/test_push |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_push
This model is a fine-tuned version of [Isaacks/test_push](https://huggingface.co/Isaacks/test_push) on the Isaacks/ihc... | [
"background",
"tissue"
] |
univers1123/segformer-b0-scene-parse-150 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-scene-parse-150
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the ... | [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror... |
rishitunu/ecc_segformerv1 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ecc_segformerv1
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/ecc... | [
"background",
"crack"
] |
rishitunu/ecc_segformerv2 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ecc_segformerv2
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/ecc... | [
"background",
"crack"
] |
pamixsun/segformer_for_optic_disc_cup_segmentation | # Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This SegFormer model has undergone specialized fine-tuning on the [REFUGE challenge dataset](https://refuge.grand-challenge.org/),
a public benchmark for semantic segmentation of anatomical structures in retinal fundus images.
The... | [
"background",
"optic disc",
"optic cup"
] |
baconseason/oneformer_coco_swin_large |
# OneFormer
OneFormer model trained on the COCO dataset (large-sized version, Swin backbone). It was introduced in the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) by Jain et al. and first released in [this repository](https://github.com/SHI-Labs/OneFormer)... | [
"person",
"bicycle",
"car",
"motorcycle",
"airplane",
"bus",
"train",
"truck",
"boat",
"traffic light",
"fire hydrant",
"stop sign",
"parking meter",
"bench",
"bird",
"cat",
"dog",
"horse",
"sheep",
"cow",
"elephant",
"bear",
"zebra",
"giraffe",
"backpack",
"umbrell... |
rishitunu/ecc_segformerv3 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ecc_segformerv3
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/ecc... | [
"background",
"crack"
] |
nomsgadded/Segments |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Segments
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-se... | [
"unlabeled",
"flat-road",
"flat-sidewalk",
"flat-crosswalk",
"flat-cyclinglane",
"flat-parkingdriveway",
"flat-railtrack",
"flat-curb",
"human-person",
"human-rider",
"vehicle-car",
"vehicle-truck",
"vehicle-bus",
"vehicle-tramtrain",
"vehicle-motorcycle",
"vehicle-bicycle",
"vehicle... |
rishitunu/ecc_segformer_main |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ecc_segformer_main
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/... | [
"background",
"crack"
] |
JCAI2000/segformer-b0-finetuned-segments-sidewalk-2-segformer-tutourial |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-sidewalk-2-segformer-tutourial
This model is a fine-tuned version of [nvidia/mit-b0](https://hug... | [
"unlabeled",
"flat-road",
"flat-sidewalk",
"flat-crosswalk",
"flat-cyclinglane",
"flat-parkingdriveway",
"flat-railtrack",
"flat-curb",
"human-person",
"human-rider",
"vehicle-car",
"vehicle-truck",
"vehicle-bus",
"vehicle-tramtrain",
"vehicle-motorcycle",
"vehicle-bicycle",
"vehicle... |
JCAI2000/segformer-b0-finetuned-100by100PNG-50epochs-attempt2-100epochs-backgroundclass |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-100by100PNG-50epochs-attempt2-100epochs-backgroundclass
This model is a fine-tuned version of [nvidia/mit... | [
"background",
"branch"
] |
JCAI2000/segformer-b5-finetuned-100by100PNG-50epochs-attempt2-100epochs-backgroundclass-2 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b3-finetuned-100by100PNG-50epochs-attempt2-100epochs-backgroundclass
This model is a fine-tuned version of [nvidia/mit... | [
"background",
"branch"
] |
Pavarissy/segformer-b0-finetuned-v0 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-v0
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the ton... | [
"artery",
"vein",
"nerve",
"muscle1",
"muscle2",
"muscle3",
"muscle4",
"unknown"
] |
Davidadel66/segformer-b0-finetuned-segments-sidewalk-2 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-sidewalk-2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/m... | [
"unlabeled",
"flat-road",
"flat-sidewalk",
"flat-crosswalk",
"flat-cyclinglane",
"flat-parkingdriveway",
"flat-railtrack",
"flat-curb",
"human-person",
"human-rider",
"vehicle-car",
"vehicle-truck",
"vehicle-bus",
"vehicle-tramtrain",
"vehicle-motorcycle",
"vehicle-bicycle",
"vehicle... |
dbaek111/segformer-b0-finetuned-segments-sidewalk-2 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-sidewalk-2
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/m... | [
"unlabeled",
"flat-road",
"flat-sidewalk",
"flat-crosswalk",
"flat-cyclinglane",
"flat-parkingdriveway",
"flat-railtrack",
"flat-curb",
"human-person",
"human-rider",
"vehicle-car",
"vehicle-truck",
"vehicle-bus",
"vehicle-tramtrain",
"vehicle-motorcycle",
"vehicle-bicycle",
"vehicle... |
dwang-LI/segformer-b-finetuned-segments-sidewalk-2 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b-finetuned-segments-sidewalk-2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mi... | [
"unlabeled",
"flat-road",
"flat-sidewalk",
"flat-crosswalk",
"flat-cyclinglane",
"flat-parkingdriveway",
"flat-railtrack",
"flat-curb",
"human-person",
"human-rider",
"vehicle-car",
"vehicle-truck",
"vehicle-bus",
"vehicle-tramtrain",
"vehicle-motorcycle",
"vehicle-bicycle",
"vehicle... |
TristanPermentier/segformer-b0-scene-parse-150 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-scene-parse-150
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the ... | [
"wall",
"building",
"sky",
"floor",
"tree",
"ceiling",
"road",
"bed ",
"windowpane",
"grass",
"cabinet",
"sidewalk",
"person",
"earth",
"door",
"table",
"mountain",
"plant",
"curtain",
"chair",
"car",
"water",
"painting",
"sofa",
"shelf",
"house",
"sea",
"mirror... |
dwang-LI/segformer-b0-finetuned-segments-sidewalk-2 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-sidewalk-2
This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/m... | [
"unlabeled",
"flat-road",
"flat-sidewalk",
"flat-crosswalk",
"flat-cyclinglane",
"flat-parkingdriveway",
"flat-railtrack",
"flat-curb",
"human-person",
"human-rider",
"vehicle-car",
"vehicle-truck",
"vehicle-bus",
"vehicle-tramtrain",
"vehicle-motorcycle",
"vehicle-bicycle",
"vehicle... |
RintaroMisaka/segformer-b0-finetuned-segments-sidewalk-2 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-sidewalk-2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/m... | [
"unlabeled",
"flat-road",
"flat-sidewalk",
"flat-crosswalk",
"flat-cyclinglane",
"flat-parkingdriveway",
"flat-railtrack",
"flat-curb",
"human-person",
"human-rider",
"vehicle-car",
"vehicle-truck",
"vehicle-bus",
"vehicle-tramtrain",
"vehicle-motorcycle",
"vehicle-bicycle",
"vehicle... |
ayoubkirouane/Segments-Sidewalk-SegFormer-B0 |
## Model Details
+ **Model Name**: Segments-Sidewalk-SegFormer-B0
+ **Model Type**: Semantic Segmentation
+ **Base Model**: nvidia/segformer-b0-finetuned-ade-512-512
+ **Fine-Tuning Dataset**: Sidewalk-Semantic
## Model Description
The **Segments-Sidewalk-SegFormer-B0** model is a semantic segmentation model fine-... | [
"unlabeled",
"flat-road",
"flat-sidewalk",
"flat-crosswalk",
"flat-cyclinglane",
"flat-parkingdriveway",
"flat-railtrack",
"flat-curb",
"human-person",
"human-rider",
"vehicle-car",
"vehicle-truck",
"vehicle-bus",
"vehicle-tramtrain",
"vehicle-motorcycle",
"vehicle-bicycle",
"vehicle... |
JCAI2000/segformerb5-finetuned-largerImages |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformerb5-finetuned-largerImages
This model is a fine-tuned version of [JCAI2000/segformer-b5-finetuned-100by100PNG-50epochs-a... | [
"background",
"branch"
] |
Manduzamzam/segformer-finetuned-sidewalk-10k-steps |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-finetuned-sidewalk-10k-steps
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b... | [
"background",
"object"
] |
JCAI2000/segformerb5-largeImages |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformerb5-largeImages
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the JCAI2... | [
"background",
"branch"
] |
twdent/segformer-b0-finetuned-robot-hiking |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-robot-hiking
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) ... | [
"unlabeled",
"traversable",
"non-traversable"
] |
twdent/segformer-b1-finetuned-Hiking |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b1-finetuned-Hiking
This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the... | [
"unlabeled",
"traversable",
"non-traversable"
] |
twdent/segformer-b5-finetuned-Hiking |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b5-finetuned-Hiking
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the... | [
"unlabeled",
"traversable",
"non-traversable"
] |
twdent/segformer-b5-finetuned-HikingHD |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b5-finetuned-HikingHD
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on t... | [
"unlabeled",
"traversable",
"non-traversable"
] |
twdent/segformer-b0-finetuned-HikingHD |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-HikingHD
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on t... | [
"traversable",
"non-traversable"
] |
giuseppemartino/model1 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model1
This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the giuseppemartino/i-SAID... | [
"background",
"ship",
"small-vehicle",
"tennis-court",
"helicopter",
"basketball-court",
"ground-track-field",
"swimming-pool",
"harbor",
"soccer-ball-field",
"plane",
"storage-tank",
"baseball-diamond",
"large-vehicle",
"bridge",
"roundabout"
] |
RyuNumchon/segformer-b0-finetuned-v0 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-v0
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the ton... | [
"artery",
"vein",
"nerve",
"muscle1",
"muscle2",
"muscle3",
"muscle4",
"unknown"
] |
twdent/segformer-b1-finetuned-HikingHD |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b1-finetuned-HikingHD
This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on t... | [
"unlabeled",
"traversable",
"non-traversable"
] |
SpotLab/MobileViT_DeepLabv3 |
_Forked from [apple/deeplabv3-mobilevit-xx-small](https://huggingface.co/apple/deeplabv3-mobilevit-xx-small)_
# MobileViT + DeepLabV3 (extra extra small-sized model)
MobileViT model pre-trained on PASCAL VOC at resolution 512x512. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vi... | [
"background",
"aeroplane",
"bicycle",
"bird",
"boat",
"bottle",
"bus",
"car",
"cat",
"chair",
"cow",
"diningtable",
"dog",
"horse",
"motorbike",
"person",
"pottedplant",
"sheep",
"sofa",
"train",
"tvmonitor"
] |
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