Update README.md

README.md

@@ -1,3 +1,61 @@
 ---
 license: cc-by-nc-4.0
+pipeline_tag: feature-extraction
 ---
+# Hiera (Tiny)
+
+Hiera is a hierarchical transformer that is a much more efficient alternative to previous series of hierarchical transformers (ConvNeXT and Swin).
+Vanilla transformer architectures (Dosovitskiy et al. 2020) are very popular yet simple and scalable architectures that enable pretraining strategies such as MAE (He et al., 2022).
+However, they use the same spatial resolution and number of channels throughout the network, ViTs make inefficient use of their parameters. This
+is in contrast to prior “hierarchical” or “multi-scale” models (e.g., Krizhevsky et al. (2012); He et al. (2016)), which use fewer channels but higher spatial resolution in early stages
+with simpler features, and more channels but lower spatial resolution later in the model with more complex features. 
+These models are way too complex though which add overhead operations to achieve state-of-the-art accuracy in ImageNet-1k, making the model slower.
+Hiera attempts to address this issue by teaching the model spatial biases by training MAE.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6141a88b3a0ec78603c9e784/ogkud4qc564bPX3f0bGXO.png)
+
+## How to Use
+
+Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
+Clone the repository.
+```bash
+git lfs install
+git clone https://huggingface.co/merve/hiera-tiny-ft-224-in1k
+pip install timm
+cd hiera-tiny-ft-224-in1k
+```
+
+```
+from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
+from PIL import Image
+import hiera
+from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+import requests
+import sys
+sys.path.append("..")
+
+model = hiera.hiera_small_224(pretrained=True, checkpoint="mae_in1k_ft_in1k") 
+input_size = 224
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+
+# preprocess the image
+transform_list = [
+    transforms.Resize(int((256 / 224) * input_size), interpolation=InterpolationMode.BICUBIC),
+    transforms.CenterCrop(input_size)
+]
+transform_vis  = transforms.Compose(transform_list)
+transform_norm = transforms.Compose(transform_list + [
+    transforms.ToTensor(),
+    transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD),
+])
+img_vis = transform_vis(image)
+img_norm = transform_norm(image)
+
+# Get imagenet class as output
+out = model(img_norm[None, ...])
+# tabby cat
+out.argmax(dim=-1).item()
+```
+
+You can try the fine-tuned model [here](https://colab.research.google.com/drive/1WIYWaCWiv5QK-MpNr-bEvqgTS1DIW19Z?usp=sharing).