Update README.md

README.md

@@ -1,3 +1,56 @@
 ---
 license: mit
+datasets:
+- imagenet-1k
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: image-classification
 ---
+
+[Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations](https://hmichaeli.github.io/alias_free_convnets/)
+
+Official PyTorch trained model.
+
+This is a ConvNeXt-Tiny variant. 
+
+convnext-baseline is ConvNeXt-Tiny with circular-padded convolutions. 
+convnext-afc is The full ConvNeXt-Tiny-AFC which is shift invariant to circular shifts.
+
+For more details see the [paper](https://arxiv.org/abs/2303.08085) or the [implementation](https://github.com/hmichaeli/alias_free_convnets).
+
+```bash
+git clone https://github.com/hmichaeli/alias_free_convnets.git
+```
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+from alias_free_convnets.models.convnext_afc import convnext_afc_tiny
+
+# baseline
+path = hf_hub_download(repo_id="hmichaeli/convnext-afc", filename="convnext_tiny_basline.pth")
+ckpt = torch.load(path)
+base_model = convnext_afc_tiny(pretrained=False, num_classes=1000)
+base_model.load_state_dict(ckpt, strict=True)
+
+# AFC
+path = hf_hub_download(repo_id="hmichaeli/convnext-afc", filename="convnext_tiny_afc.pth")
+ckpt = torch.load(path)
+afc_model = convnext_afc_tiny(
+        pretrained=False,
+        num_classes=1000,
+        activation='up_poly_per_channel',
+        activation_kwargs={'in_scale': 7, 'out_scale': 7, 'train_scale': True},
+        blurpool_kwargs={"filter_type": "ideal", "scale_l2": False},
+        normalization_type='CHW2',
+        stem_activation_kwargs={"in_scale": 7, "out_scale": 7, "train_scale": True, "cutoff": 0.75},
+        normalization_kwargs={},
+        stem_mode='activation_residual', stem_activation='lpf_poly_per_channel'
+    )
+afc_model.load_state_dict(ckpt, strict=False)
+
+```
+
+