model_id stringlengths 7 105 | model_card stringlengths 1 130k | model_labels listlengths 2 80k |
|---|---|---|
Matthijs/mobilevit-small |
# MobileViT (small-sized model)
MobileViT model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this repository](ht... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
karthiksv/vit-base-patch16-224-cifar10 |
<!-- 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-base-patch16-224-cifar10
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/go... | [
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
karthiksv/vit-base-patch16-224-in21k-finetuned-cifar10 |
<!-- 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-base-patch16-224-in21k-finetuned-cifar10
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://h... | [
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
matteopilotto/vit-base-patch16-224-in21k-snacks |
# Vision Transformer fine-tuned on `Matthijs/snacks` dataset
Vision Transformer (ViT) model pre-trained on ImageNet-21k and fine-tuned on [**Matthijs/snacks**](https://huggingface.co/datasets/Matthijs/snacks) for 5 epochs using various data augmentation transformations from `torchvision`.
The model achieves a **94.9... | [
"apple",
"banana",
"cake",
"candy",
"carrot",
"cookie",
"doughnut",
"grape",
"hot dog",
"ice cream",
"juice",
"muffin",
"orange",
"pineapple",
"popcorn",
"pretzel",
"salad",
"strawberry",
"waffle",
"watermelon"
] |
jadohu/BEiT-finetuned |
<!-- 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. -->
# BEiT-finetuned
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-bas... | [
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
microsoft/cvt-w24-384-22k |
# Convolutional Vision Transformer (CvT)
CvT-w24 model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Wu et al. and first released in [this repository](https://g... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
Ahmed9275/Vit-Cifar100 |
<!-- 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-base-beans-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/v... | [
"apple",
"aquarium_fish",
"bowl",
"boy",
"bridge",
"bus",
"butterfly",
"camel",
"can",
"castle",
"caterpillar",
"cattle",
"baby",
"chair",
"chimpanzee",
"clock",
"cloud",
"cockroach",
"couch",
"cra",
"crocodile",
"cup",
"dinosaur",
"bear",
"dolphin",
"elephant",
"... |
eslamxm/vit-base-food101 |
<!-- 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-base-food101-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google... | [
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_ch... |
aricibo/swin-tiny-patch4-window7-224-finetuned-eurosat |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | [
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
AykeeSalazar/vc-bantai-vit-withoutAMBI |
<!-- 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. -->
# vc-bantai-vit-withoutAMBI
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/googl... | [
"public drinking",
"public smoking",
"non-violation"
] |
nickmuchi/swin-tiny-patch4-window7-224-finetuned-eurosat |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | [
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
Vemi/orchid219_ft_vit-large-patch16-224-in21k-finetuned-eurosat |
<!-- 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. -->
# orchid219_ft_vit-large-patch16-224-in21k-finetuned-eurosat
This model is a fine-tuned version of [gary109/orchid219_ft_vit-large... | [
"n0000",
"n0001",
"n0002",
"n0003",
"n0004",
"n0005",
"n0006",
"n0007",
"n0008",
"n0009",
"n0010",
"n0011",
"n0012",
"n0013",
"n0014",
"n0015",
"n0016",
"n0017",
"n0018",
"n0019",
"n0020",
"n0021",
"n0022",
"n0023",
"n0024",
"n0025",
"n0026",
"n0027",
"n0028",... |
Annabelleabbott/swin-tiny-patch4-window7-224-finetuned-eurosat |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | [
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
schoenml/swin-tiny-patch4-window7-224-finetuned-eurosat |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | [
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
mehnaazasad/swin-tiny-patch4-window7-224-finetuned-eurosat |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | [
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
GRANTHE2761/swin-tiny-patch4-window7-224-finetuned-eurosat |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | [
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
mbyanfei/swin-tiny-patch4-window7-224-finetuned-eurosat |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | [
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
eugenecamus/resnet-50-base-beans-demo |
<!-- 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. -->
# resnet-50-base-beans-demo
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50... | [
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
Jazzweller/swin-tiny-patch4-window7-224-finetuned-eurosat |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | [
"down",
"up"
] |
apple/mobilevit-small |
# MobileViT (small-sized model)
MobileViT model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this repository](ht... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
apple/mobilevit-x-small |
# MobileViT (extra small-sized model)
MobileViT model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this reposito... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
apple/mobilevit-xx-small |
# MobileViT (extra extra small-sized model)
MobileViT model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this re... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
facebook/levit-384 |
# LeViT
LeViT-384 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
](https://arxiv.org/abs/2104.01136) by Graham et al. and first released in [this repository](https://github.com/facebookresearch/LeViT).
Di... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
facebook/levit-256 |
# LeViT
LeViT-256 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
](https://arxiv.org/abs/2104.01136) by Graham et al. and first released in [this repository](https://github.com/facebookresearch/LeViT).
Di... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
facebook/levit-192 |
# LeViT
LeViT-192 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
](https://arxiv.org/abs/2104.01136) by Graham et al. and first released in [this repository](https://github.com/facebookresearch/LeViT).
Di... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
facebook/levit-128 |
# LeViT
LeViT-128 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
](https://arxiv.org/abs/2104.01136) by Graham et al. and first released in [this repository](https://github.com/facebookresearch/LeViT).
Di... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
facebook/levit-128S |
# LeViT
LeViT-128S model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
](https://arxiv.org/abs/2104.01136) by Graham et al. and first released in [this repository](https://github.com/facebookresearch/LeViT).
D... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
q2-jlbar/swin-tiny-patch4-window7-224-finetuned-eurosat |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | [
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
amehta633/cifar-10-vgg-pretrained | [
"plane",
"car",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] | |
aspis/swin-base-finetuned-snacks |
<!-- 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. -->
# swin-base-finetuned-snacks
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co... | [
"apple",
"banana",
"juice",
"muffin",
"orange",
"pineapple",
"popcorn",
"pretzel",
"salad",
"strawberry",
"waffle",
"watermelon",
"cake",
"candy",
"carrot",
"cookie",
"doughnut",
"grape",
"hot dog",
"ice cream"
] |
aspis/swin-finetuned-food101 |
<!-- 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. -->
# swin-finetuned-food101
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/mic... | [
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_ch... |
edumunozsala/vit_base-224-in21k-ft-cifar10 |
# Model vit_base-224-in21k-ft-cifar10
## **A finetuned model for Image classification in Spanish**
This model was trained using Amazon SageMaker and the Hugging Face Deep Learning container,
The base model is **Vision Transformer (base-sized model)** which is a transformer encoder model (BERT-like) pretrained on a ... | [
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
edumunozsala/vit_base-224-in21k-ft-cifar100 |
# Model vit_base-224-in21k-ft-cifar100
## **A finetuned model for Image classification in Spanish**
This model was trained using Amazon SageMaker and the Hugging Face Deep Learning container,
The base model is **Vision Transformer (base-sized model)** which is a transformer encoder model (BERT-like) pretrained on a... | [
"apple",
"aquarium_fish",
"baby",
"bear",
"beaver",
"bed",
"bee",
"beetle",
"bicycle",
"bottle",
"bowl",
"boy",
"bridge",
"bus",
"butterfly",
"camel",
"can",
"castle",
"caterpillar",
"cattle",
"chair",
"chimpanzee",
"clock",
"cloud",
"cockroach",
"couch",
"cra",
... |
shivarama23/swin-tiny-patch4-window7-224-finetuned-image_quality |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-image_quality
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-2... | [
"bad",
"good"
] |
amyeroberts/swin-tiny-patch4-window7-224-finetuned-eurosat |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# amyeroberts/swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-2... | [
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
microsoft/swinv2-tiny-patch4-window8-256 |
# Swin Transformer v2 (tiny-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/micr... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
microsoft/swinv2-tiny-patch4-window16-256 |
# Swin Transformer v2 (tiny-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/micr... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
saiharsha/vit-base-beans |
<!-- 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-base-beans
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"
] |
flyswot/test2 |
<!-- 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. -->
# test2
This model is a fine-tuned version of [flyswot/convnext-tiny-224_flyswot](https://huggingface.co/flyswot/convnext-tiny-224... | [
"container",
"control shot",
"cover",
"edge + spine",
"flysheet",
"other",
"page + folio",
"scroll"
] |
microsoft/swinv2-small-patch4-window8-256 |
# Swin Transformer v2 (small-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/mic... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
microsoft/swinv2-small-patch4-window16-256 |
# Swin Transformer v2 (small-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/mic... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
microsoft/swinv2-base-patch4-window8-256 |
# Swin Transformer v2 (base-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/micr... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
microsoft/swinv2-base-patch4-window16-256 |
# Swin Transformer v2 (base-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/micr... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
microsoft/swinv2-base-patch4-window12-192-22k |
# Swin Transformer v2 (tiny-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k at resolution 192x192. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/mic... | [
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9",
"label_10",
"label_11",
"label_12",
"label_13",
"label_14",
"label_15",
"label_16",
"label_17",
"label_18",
"label_19",
"label_20",
"label_21",
"label_22",
"lab... |
microsoft/swinv2-large-patch4-window12-192-22k |
# Swin Transformer v2 (large-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k at resolution 192x192. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/mi... | [
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9",
"label_10",
"label_11",
"label_12",
"label_13",
"label_14",
"label_15",
"label_16",
"label_17",
"label_18",
"label_19",
"label_20",
"label_21",
"label_22",
"lab... |
microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft |
# Swin Transformer v2 (base-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repo... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
microsoft/swinv2-base-patch4-window12to24-192to384-22kto1k-ft |
# Swin Transformer v2 (base-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repo... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft |
# Swin Transformer v2 (base-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repo... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft |
# Swin Transformer v2 (large-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this rep... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
efederici/convnext-base-224-22k-1k-orig-cats-vs-dogs |
# convnext-base-224-22k-1k-orig-cats-vs-dogs
This model is a fine-tuned version of [facebook/convnext-base-224-22k-1k](https://huggingface.co/facebook/convnext-base-224-22k-1k) on the cats_vs_dogs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0103
- Accuracy: 0.9973
<p align="center">
... | [
"cat",
"dog"
] |
raedinkhaled/vit-base-mri |
<!-- 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-base-mri
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-pa... | [
"cad",
"healthy"
] |
raedinkhaled/swin-tiny-patch4-window7-224-finetuned-mri |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-mri
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https:... | [
"cad",
"healthy"
] |
sudo-s/modelversion01 |
<!-- 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. -->
# modelversion01
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-... | [
"0",
"1",
"10",
"100",
"101",
"102",
"103",
"104",
"105",
"106",
"107",
"108",
"109",
"11",
"110",
"111",
"112",
"113",
"114",
"115",
"116",
"117",
"118",
"119",
"12",
"120",
"121",
"122",
"123",
"124",
"125",
"126",
"127",
"128",
"129",
"13",
... |
skylord/swin-finetuned-food101 |
<!-- 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. -->
# swin-finetuned-food101
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/mic... | [
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_ch... |
sasha/dog-food-vit-base-patch16-224-in21k |
# dog-food-vit-base-patch16-224-in21k
This model was trained on the `train` split of the [Dogs vs Food](https://huggingface.co/datasets/sasha/dog-food) dataset -- try training your own using the
[the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb)!
... | [
"dog",
"food"
] |
Sampson2022/test2 |
# ResNet-50 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been wri... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
autoevaluate/image-multi-class-classification |
<!-- 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. -->
# image-classification
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/micro... | [
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9"
] |
sasha/dog-food-swin-tiny-patch4-window7-224 |
# dog-food-swin-tiny-patch4-window7-224
This model was trained on the `train` split of the [Dogs vs Food](https://huggingface.co/datasets/sasha/dog-food) dataset -- try training your own using the
[the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb)!
... | [
"dog",
"food"
] |
sasha/dog-food-convnext-tiny-224 |
# dog-food-convnext-tiny-224
This model was trained on the `train` split of the [Dogs vs Food](https://huggingface.co/datasets/sasha/dog-food) dataset -- try training your own using the
[the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb)!
## Examp... | [
"dog",
"food"
] |
Matthijs/mobilenet_v1_1.0_224 |
# MobileNet V1
MobileNet V1 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Howard et al, and first released in [this repository](https://github.com/tensorflow/models/... | [
"background",
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"o... |
Matthijs/mobilenet_v1_0.75_192 |
# MobileNet V1
MobileNet V1 model pre-trained on ImageNet-1k at resolution 192x192. It was introduced in [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Howard et al, and first released in [this repository](https://github.com/tensorflow/models/... | [
"background",
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"o... |
optimum/vit-base-patch16-224 |
# ONNX convert of ViT (base-sized model)
Conversion of [ViT-base](https://huggingface.co/google/vit-base-patch16-224), which has a classification head to perform **image classification**.
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 c... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-plantdisease
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-22... | [
"pepper__bell___bacterial_spot",
"pepper__bell___healthy",
"potato___early_blight",
"potato___late_blight",
"potato___healthy",
"tomato_bacterial_spot",
"tomato_early_blight",
"tomato_late_blight",
"tomato_leaf_mold",
"tomato_septoria_leaf_spot",
"tomato_spider_mites_two_spotted_spider_mite",
... |
douwekiela/resnet-18-finetuned-dogfood |
<!-- 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. -->
# resnet-18-finetuned-dogfood
This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-... | [
"chicken",
"dog",
"muffin"
] |
sasha/swin-tiny-finetuned-dogfood |
<!-- 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. -->
# swin-tiny-finetuned-dogfood
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.c... | [
"chicken",
"dog",
"muffin"
] |
SerdarHelli/ThyroidTumorClassificationModel |
Thyroid nodule is one of the most common endocrine carcinomas. Due to its higher reveal ability and ability to distinguish between benign and malignant nodules in pathological features, ultrasonography has become the most widely used modality for finding and diagnosing thyroid cancer when compared to CT and MRI.
In t... | [
"0",
"1"
] |
sudo-s/exper_batch_8_e4 |
<!-- 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. -->
# exper_batch_8_e4
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-bas... | [
"0",
"1",
"10",
"100",
"101",
"102",
"103",
"104",
"105",
"106",
"107",
"108",
"109",
"11",
"110",
"111",
"112",
"113",
"114",
"115",
"116",
"117",
"118",
"119",
"12",
"120",
"121",
"122",
"123",
"124",
"125",
"126",
"127",
"128",
"129",
"13",
... |
sudo-s/exper_batch_8_e8 |
<!-- 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. -->
# exper_batch_8_e8
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-bas... | [
"0",
"1",
"10",
"100",
"101",
"102",
"103",
"104",
"105",
"106",
"107",
"108",
"109",
"11",
"110",
"111",
"112",
"113",
"114",
"115",
"116",
"117",
"118",
"119",
"12",
"120",
"121",
"122",
"123",
"124",
"125",
"126",
"127",
"128",
"129",
"13",
... |
sudo-s/exper_batch_16_e4 |
<!-- 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. -->
# exper_batch_16_e4
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-ba... | [
"0",
"1",
"10",
"100",
"101",
"102",
"103",
"104",
"105",
"106",
"107",
"108",
"109",
"11",
"110",
"111",
"112",
"113",
"114",
"115",
"116",
"117",
"118",
"119",
"12",
"120",
"121",
"122",
"123",
"124",
"125",
"126",
"127",
"128",
"129",
"13",
... |
sudo-s/exper_batch_16_e8 |
<!-- 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. -->
# exper_batch_16_e8
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-ba... | [
"0",
"1",
"10",
"100",
"101",
"102",
"103",
"104",
"105",
"106",
"107",
"108",
"109",
"11",
"110",
"111",
"112",
"113",
"114",
"115",
"116",
"117",
"118",
"119",
"12",
"120",
"121",
"122",
"123",
"124",
"125",
"126",
"127",
"128",
"129",
"13",
... |
sudo-s/exper_batch_32_e4 |
<!-- 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. -->
# exper_batch_32_e4
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-ba... | [
"0",
"1",
"10",
"100",
"101",
"102",
"103",
"104",
"105",
"106",
"107",
"108",
"109",
"11",
"110",
"111",
"112",
"113",
"114",
"115",
"116",
"117",
"118",
"119",
"12",
"120",
"121",
"122",
"123",
"124",
"125",
"126",
"127",
"128",
"129",
"13",
... |
sudo-s/exper_batch_32_e8 |
<!-- 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. -->
# exper_batch_32_e8
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-ba... | [
"0",
"1",
"10",
"100",
"101",
"102",
"103",
"104",
"105",
"106",
"107",
"108",
"109",
"11",
"110",
"111",
"112",
"113",
"114",
"115",
"116",
"117",
"118",
"119",
"12",
"120",
"121",
"122",
"123",
"124",
"125",
"126",
"127",
"128",
"129",
"13",
... |
Matthijs/mobilenet_v2_1.0_224 |
# MobileNet V2
MobileNet V2 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in [this repositor... | [
"background",
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"o... |
Matthijs/mobilenet_v2_1.4_224 |
# MobileNet V2
MobileNet V2 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in [this repositor... | [
"background",
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"o... |
jimypbr/cifar10_outputs |
<!-- 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. -->
# cifar10_outputs
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base... | [
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
Shivagowri/vit-snacks |
<!-- 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-snacks
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patc... | [
"apple",
"banana",
"juice",
"muffin",
"orange",
"pineapple",
"popcorn",
"pretzel",
"salad",
"strawberry",
"waffle",
"watermelon",
"cake",
"candy",
"carrot",
"cookie",
"doughnut",
"grape",
"hot dog",
"ice cream"
] |
sudo-s/new_exper3 |
<!-- 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. -->
# new_exper3
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patc... | [
"0",
"1",
"10",
"100",
"101",
"102",
"103",
"104",
"105",
"106",
"107",
"108",
"109",
"11",
"110",
"111",
"112",
"113",
"114",
"115",
"116",
"117",
"118",
"119",
"12",
"120",
"121",
"122",
"123",
"124",
"125",
"126",
"127",
"128",
"129",
"13",
... |
YKXBCi/vit-base-patch16-224-in21k-ucSat |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# YKXBCi/vit-base-patch16-224-in21k-ucSat
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.... | [
"agricultural",
"airplane",
"harbor",
"intersection",
"mediumresidential",
"mobilehomepark",
"overpass",
"parkinglot",
"river",
"runway",
"sparseresidential",
"storagetanks",
"baseballdiamond",
"tenniscourt",
"beach",
"buildings",
"chaparral",
"denseresidential",
"forest",
"fre... |
raedinkhaled/deit-base-mri |
<!-- 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. -->
# deit-base-mri
This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/d... | [
"diseased",
"normale"
] |
gianlab/swin-tiny-patch4-window7-224-finetuned-skin-cancer |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-skin-cancer
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224... | [
"actinic-keratoses",
"basal-cell-carcinoma",
"benign-keratosis-like-lesions",
"dermatofibroma",
"melanocytic-nevi",
"melanoma",
"vascular-lesions"
] |
bazyl/gtsrb-model |
<!-- 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. -->
# gtsrb-model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-pat... | [
"speed limit (20km/h)",
"speed limit (30km/h)",
"no passing veh over 3.5 tons",
"right-of-way at intersection",
"priority road",
"yield",
"stop",
"no vehicles",
"veh > 3.5 tons prohibited",
"no entry",
"general caution",
"dangerous curve left",
"speed limit (50km/h)",
"dangerous curve righ... |
YKXBCi/resnet-50-ucSat |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# YKXBCi/resnet-50-ucSat
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an u... | [
"agricultural",
"airplane",
"harbor",
"intersection",
"mediumresidential",
"mobilehomepark",
"overpass",
"parkinglot",
"river",
"runway",
"sparseresidential",
"storagetanks",
"baseballdiamond",
"tenniscourt",
"beach",
"buildings",
"chaparral",
"denseresidential",
"forest",
"fre... |
YKXBCi/resnet-50-euroSat |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# YKXBCi/resnet-50-euroSat
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an... | [
"river",
"annualcrop",
"herbaceousvegetation",
"industrial",
"residential",
"highway",
"pasture",
"forest",
"sealake",
"permanentcrop"
] |
YKXBCi/vit-base-patch16-224-in21k-aidSat |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# YKXBCi/vit-base-patch16-224-in21k-aidSat
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface... | [
"airport",
"bareland",
"farmland",
"forest",
"industrial",
"meadow",
"mediumresidential",
"mountain",
"park",
"parking",
"playground",
"pond",
"baseballfield",
"port",
"railwaystation",
"resort",
"river",
"school",
"sparseresidential",
"square",
"stadium",
"storagetanks",
... |
ShihTing/PanJuOffset_TwoClass | # PanJu offset detect by image
Use fintune from google/vit-base-patch16-224(https://huggingface.co/google/vit-base-patch16-224)
## Dataset
```python
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 329
})
validation: Dataset({
features: ['image', 'label'],
... | [
"break",
"normal"
] |
HekmatTaherinejad/swin-tiny-patch4-window7-224-finetuned-eurosat |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | [
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
aihub007/convnext-tiny-224-finetuned-eurosat-albumentations |
<!-- 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. -->
# convnext-tiny-224-finetuned-eurosat-albumentations
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://hu... | [
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
amyeroberts/resnet-18-finetuned-eurosat |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# amyeroberts/resnet-18-finetuned-eurosat
This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/r... | [
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
SiddharthaM/beit-base-patch16-224-pt22k-ft22k-rim_one-new |
<!-- 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. -->
# beit-base-patch16-224-pt22k-ft22k-rim_one-new
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k... | [
"glaucoma",
"normal"
] |
prashanth0205/vit_spectrogram |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vit_spectrogram
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch... | [
"female",
"male"
] |
samayl24/vit-base-beans-demo-v5 |
<!-- 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-base-beans-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/v... | [
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
sl82/swin-tiny-patch4-window7-224-finetuned-eurosat |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | [
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
dgrinwald/swin-tiny-patch4-window7-224-finetuned-eurosat |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | [
"4.x (before awn extrusion)",
"4.9 (awn extrusion)",
"5.x (before flowering)",
"6.x (flowering)"
] |
dingusagar/vit-base-movie-scenes-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. -->
# vit-base-movie-scenes-v1
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google... | [
" batman movie scenes",
"harry potter movie scenes"
] |
dingusagar/vit-base-avengers-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. -->
# vit-base-avengers-v1
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit... | [
"ant man",
"black panther",
"iron man",
"loki",
"scarlet witch",
"spider man",
"thor",
"vision avengers",
"black widow",
"bucky barnes",
"captain america",
"captain marvel",
"docter strage",
"falcon avengers",
"hawkeye avengers",
"hulk"
] |
Loc/lucky-model |
# Vision Transformer (base-sized model)
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: Transfo... | [
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio... |
liyijing024/swin-base-patch4-window7-224-in22k-finetuned |
<!-- 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. -->
# swin-base-patch4-window7-224-in22k-finetuned
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k... | [
"sans-serif",
"scripts",
"serif"
] |
JoonJoon/swin-tiny-patch4-window7-224-finetuned-eurosat |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | [
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
liyijing024/swin-base-patch4-window7-224-in22k-Chinese-finetuned |
<!-- 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. -->
# swin-base-patch4-window7-224-in22k-Chinese-finetuned
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-2... | [
"fangsong",
"heiti",
"kaiti",
"lishu",
"songti"
] |
pthpth/ViTFineTuned |
<!-- 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. -->
# ViTFineTuned
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-pa... | [
"aluminium_foil",
"brown_bread",
"wool",
"corduroy",
"cork",
"cotton",
"cracker",
"lettuce_leaf",
"linen",
"white_bread",
"wood"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.