Add model

README.md

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+---
+tags:
+- image-classification
+- timm
+library_name: timm
+license: cc-by-nc-4.0
+datasets:
+- imagenet-1k
+---
+# Model card for hiera_base_224.mae
+
+A Hiera image feature model. Pretrained on ImageNet-1k with Self-Supervised Masked Autoencoder (MAE) method by paper authors.
+
+
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 50.8
+  - GMACs: 9.4
+  - Activations (M): 30.4
+  - Image size: 224 x 224
+- **Dataset:** ImageNet-1k
+- **Papers:**
+  - Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles: https://arxiv.org/abs/2306.00989
+  - Masked Autoencoders Are Scalable Vision Learners: https://arxiv.org/abs/2111.06377
+- **Original:** https://github.com/facebookresearch/hiera
+
+## Model Usage
+### Image Classification
+```python
+from urllib.request import urlopen
+from PIL import Image
+import timm
+
+img = Image.open(urlopen(
+    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
+))
+
+model = timm.create_model('hiera_base_224.mae', pretrained=True)
+model = model.eval()
+
+# get model specific transforms (normalization, resize)
+data_config = timm.data.resolve_model_data_config(model)
+transforms = timm.data.create_transform(**data_config, is_training=False)
+
+output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1
+
+top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
+```
+
+### Feature Map Extraction
+```python
+from urllib.request import urlopen
+from PIL import Image
+import timm
+
+img = Image.open(urlopen(
+    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
+))
+
+model = timm.create_model(
+    'hiera_base_224.mae',
+    pretrained=True,
+    features_only=True,
+)
+model = model.eval()
+
+# get model specific transforms (normalization, resize)
+data_config = timm.data.resolve_model_data_config(model)
+transforms = timm.data.create_transform(**data_config, is_training=False)
+
+output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1
+
+for o in output:
+    # print shape of each feature map in output
+    # e.g.:
+    #  torch.Size([1, 96, 56, 56])
+    #  torch.Size([1, 192, 28, 28])
+    #  torch.Size([1, 384, 14, 14])
+    #  torch.Size([1, 768, 7, 7])
+
+    print(o.shape)
+```
+
+### Image Embeddings
+```python
+from urllib.request import urlopen
+from PIL import Image
+import timm
+
+img = Image.open(urlopen(
+    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
+))
+
+model = timm.create_model(
+    'hiera_base_224.mae',
+    pretrained=True,
+    num_classes=0,  # remove classifier nn.Linear
+)
+model = model.eval()
+
+# get model specific transforms (normalization, resize)
+data_config = timm.data.resolve_model_data_config(model)
+transforms = timm.data.create_transform(**data_config, is_training=False)
+
+output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor
+
+# or equivalently (without needing to set num_classes=0)
+
+output = model.forward_features(transforms(img).unsqueeze(0))
+# output is unpooled, a (1, 49, 768) shaped tensor
+
+output = model.forward_head(output, pre_logits=True)
+# output is a (1, num_features) shaped tensor
+```
+
+## Model Comparison
+### By Top-1
+
+|model                            |top1  |top1_err|top5  |top5_err|param_count|
+|---------------------------------|------|--------|------|--------|-----------|
+|hiera_huge_224.mae_in1k_ft_in1k     |86.834|13.166  |98.01 |1.99    |672.78     |
+|hiera_large_224.mae_in1k_ft_in1k    |86.042|13.958  |97.648|2.352   |213.74     |
+|hiera_base_plus_224.mae_in1k_ft_in1k|85.134|14.866  |97.158|2.842   |69.9       |
+|hiera_base_224.mae_in1k_ft_in1k     |84.49 |15.51   |97.032|2.968   |51.52      |
+|hiera_small_224.mae_in1k_ft_in1k    |83.884|16.116  |96.684|3.316   |35.01      |
+|hiera_tiny_224.mae_in1k_ft_in1k     |82.786|17.214  |96.204|3.796   |27.91      |
+
+## Citation
+```bibtex
+@article{ryali2023hiera,
+  title={Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles},
+  author={Ryali, Chaitanya and Hu, Yuan-Ting and Bolya, Daniel and Wei, Chen and Fan, Haoqi and Huang, Po-Yao and Aggarwal, Vaibhav and Chowdhury, Arkabandhu and Poursaeed, Omid and Hoffman, Judy and Malik, Jitendra and Li, Yanghao and Feichtenhofer, Christoph},
+  journal={ICML},
+  year={2023}
+}
+```
+```bibtex
+@Article{MaskedAutoencoders2021,
+  author  = {Kaiming He and Xinlei Chen and Saining Xie and Yanghao Li and Piotr Doll{'a}r and Ross Girshick},
+  journal = {arXiv:2111.06377},
+  title   = {Masked Autoencoders Are Scalable Vision Learners},
+  year    = {2021},
+}
+```

config.json

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+{
+  "architecture": "hiera_base_224",
+  "num_classes": 0,
+  "num_features": 768,
+  "pretrained_cfg": {
+    "tag": "mae",
+    "custom_load": false,
+    "input_size": [
+      3,
+      224,
+      224
+    ],
+    "fixed_input_size": true,
+    "interpolation": "bicubic",
+    "crop_pct": 0.9,
+    "crop_mode": "center",
+    "mean": [
+      0.485,
+      0.456,
+      0.406
+    ],
+    "std": [
+      0.229,
+      0.224,
+      0.225
+    ],
+    "num_classes": 0,
+    "pool_size": null,
+    "first_conv": "patch_embed.proj",
+    "classifier": "head.fc",
+    "license": "cc-by-nc-4.0"
+  }
+}

model.safetensors

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pytorch_model.bin

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