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README.md

@@ -1,10 +1,60 @@
 ---
+license: apache-2.0
 tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
+- pytorch
+- image-classification
+- robust-representation
+- modular-ai
+- dynamic-masking
+- dinov2
+- ifam
+arxiv: 2506.08915
+library_name: generic
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: [More Information Needed]
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+# IFAM (metashift-k8) Model Checkpoint
+
+This is the official pre-trained checkpoint of the **IFAM (Iterative Focus and Attention Masking)** framework, proposed in the paper **"Two-stage Vision Transformers and Hard Masking offer Robust Object Representations"** (accepted as an oral presentation at ICPR 2026).
+
+- **Paper:** [Two-stage Vision Transformers and Hard Masking offer Robust Object Representations](https://arxiv.org/abs/2506.08915)
+- **Repository:** [GitHub - ananthu-aniraj/ifam](https://github.com/ananthu-aniraj/ifam)
+
+## Model Description
+IFAM model trained on the Metashifts dataset with 8 parts (K=8).
+
+The IFAM framework is a two-stage approach:
+1. **Stage 1 (Selector):** Processes the full image to discover object parts and identify task-relevant regions.
+2. **Stage 2 (Predictor):** Restricts its receptive field to the selected regions using input attention masking, preventing spurious background details from affecting the classification.
+
+## Usage
+
+To use this model, ensure you have the `ifam` repository cloned and the dependencies installed. You can load this model directly from the Hugging Face Hub:
+
+```python
+import torch
+import sys
+# Append the path to the ifam repository
+sys.path.append("/path/to/ifam")
+
+from models import FullTwoStageModelDoubleClassify
+
+# Load the model directly from Hugging Face Hub
+model = FullTwoStageModelDoubleClassify.from_pretrained("ananthu-aniraj/ifam-metashift-k8")
+model.eval()
+
+# Example: Run inference
+# x = torch.rand(1, 3, 224, 224)
+# with torch.no_grad():
+#     outputs = model(x)
+```
+
+## Citation
+If you use this model in your research, please cite:
+```bibtex
+@inproceedings{aniraj2026ifam,
+  title={Two-stage Vision Transformers and Hard Masking offer Robust Object Representations},
+  author={Aniraj, Ananthu and Dantas, Cassio F. and Ienco, Dino and Marcos, Diego},
+  booktitle={International Conference on Pattern Recognition (ICPR)},
+  year={2026}
+}
+```