Upload README.md

README.md

@@ -2,33 +2,32 @@
 license: apache-2.0
 tags:
 - vision
-- image-segmentation
+- image-classification
 
 datasets:
-- imagenet-21k
 - imagenet-1k
 
 widget:
-- src:https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
-  widget.title
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
+  example_title: Tiger
 
 ---
 
-# ConvNext
+# RegNet
 
-ConvNext model trained on imagenet-21k. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) and first released in [this repository](https://github.com/facebookresearch/ConvNeXt). 
+RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycl). 
 
-Disclaimer: The team releasing ConvNext did not write a model card for this model so this model card has been written by the Hugging Face team.
+Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
 
 ## Model description
 
-weiiii
+The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
 
-![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnext_architecture.png)
+![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/regnet_architecture.png)
 
 ## Intended uses & limitations
 
-You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
+You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for
 fine-tuned versions on a task that interests you.
 
 ### How to use
@@ -36,15 +35,15 @@ fine-tuned versions on a task that interests you.
 Here is how to use this model:
 
 ```python
->>> from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification
+>>> from transformers import AutoFeatureExtractor, RegNetForImageClassification
 >>> import torch
 >>> from datasets import load_dataset
 
 >>> dataset = load_dataset("huggingface/cats-image")
 >>> image = dataset["test"]["image"][0]
 
->>> feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-tiny-224")
->>> model = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224")
+>>> feature_extractor = AutoFeatureExtractor.from_pretrained("")
+>>> model = RegNetForImageClassification.from_pretrained("")
 
 >>> inputs = feature_extractor(image, return_tensors="pt")
 
@@ -59,4 +58,4 @@ Here is how to use this model:
 
 
 
-For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
+For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).