
# ResNet-50

Pretrained model on [ImageNet](http://www.image-net.org/). The ResNet architecture was introduced in
[this paper](https://arxiv.org/abs/1512.03385).

## Intended uses

You can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head
to fine-tune it on a downstream task (another classification task with different labels, image segmentation or
object detection, to name a few).

## Evaluation results

This model has a top1-accuracy of 76.13% and a top-5 accuracy of 92.86% in the evaluation set of ImageNet.

{"license": "afl-3.0", "tags": ["image-classification", "resnet"], "datasets": ["imagenet"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
transformers pytorch resnet image-classification custom_code dataset:imagenet arxiv:1512.03385 license:afl-3.0 autotrain_compatible region:us
TAGS
#transformers #pytorch #resnet #image-classification #custom_code #dataset-imagenet #arxiv-1512.03385 #license-afl-3.0 #autotrain_compatible #region-us 


# ResNet-50

Pretrained model on ImageNet. The ResNet architecture was introduced in
this paper.

## Intended uses

You can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head
to fine-tune it on a downstream task (another classification task with different labels, image segmentation or
object detection, to name a few).

## Evaluation results

This model has a top1-accuracy of 76.13% and a top-5 accuracy of 92.86% in the evaluation set of ImageNet.

# ResNet-50

Pretrained model on ImageNet. The ResNet architecture was introduced in
this paper. ## Intended uses

You can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head
to fine-tune it on a downstream task (another classification task with different labels, image segmentation or
object detection, to name a few). ## Evaluation results

This model has a top1-accuracy of 76.13% and a top-5 accuracy of 92.86% in the evaluation set of ImageNet.
TAGS
#transformers #pytorch #resnet #image-classification #custom_code #dataset-imagenet #arxiv-1512.03385 #license-afl-3.0 #autotrain_compatible #region-us 
 # ResNet-50

Pretrained model on ImageNet. The ResNet architecture was introduced in
this paper. ## Intended uses

You can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head
to fine-tune it on a downstream task (another classification task with different labels, image segmentation or
object detection, to name a few). ## Evaluation results

This model has a top1-accuracy of 76.13% and a top-5 accuracy of 92.86% in the evaluation set of ImageNet.
passage: TAGS
#transformers #pytorch #resnet #image-classification #custom_code #dataset-imagenet #arxiv-1512.03385 #license-afl-3.0 #autotrain_compatible #region-us 
# ResNet-50

Pretrained model on ImageNet. The ResNet architecture was introduced in
this paper.## Intended uses

You can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head
to fine-tune it on a downstream task (another classification task with different labels, image segmentation or
object detection, to name a few).## Evaluation results

This model has a top1-accuracy of 76.13% and a top-5 accuracy of 92.86% in the evaluation set of ImageNet.