Update model card

README.md

@@ -1,87 +1,80 @@
----
-license: mit
-tags:
-  - image-rotation
-  - orientation-estimation
-  - angle-detection
-  - circular-gaussian-distribution
-  - mambaout
-  - pytorch
-datasets:
-  - coco
-metrics:
-  - mae
-pipeline_tag: image-classification
----
-
-# Image Rotation Angle Estimation
-
-**[Try the interactive demo](https://huggingface.co/spaces/maxwoe/image-rotation-angle-estimation)**
-
-Predicts the rotation angle of an image using the **Circular Gaussian Distribution (CGD)** method with a **MambaOut Base** backbone.
-
-The model outputs a probability distribution over 360 angle bins (1 degree resolution) and extracts the predicted angle via argmax. It handles the full 360 degree range with no boundary discontinuities.
-
-## Available Checkpoints
-
-| Checkpoint | Dataset | MAE | Median Error |
-|---|---|---|---|
-| `cgd_mambaout_base_coco2017.ckpt` | COCO 2017 | 2.84 deg | 0.55 deg |
-| `cgd_mambaout_base_coco2014.ckpt` | COCO 2014 | 3.71 deg | 0.68 deg |
-
-## Usage
-
-The inference code (`model_cgd.py`, `architectures.py`, `rotation_utils.py`) is included in this repo.
-
-```python
-from huggingface_hub import snapshot_download
-
-# Download inference code
-snapshot_download(
-    repo_id="maxwoe/image-rotation-angle-estimation",
-    allow_patterns=["*.py", "*.json"],
-    local_dir=".",
-)
-
-# Load model (defaults to COCO 2017 checkpoint)
-from model_cgd import CGDAngleEstimation
-model = CGDAngleEstimation.from_pretrained("maxwoe/image-rotation-angle-estimation")
-
-# Or load a specific checkpoint by filename
-# model = CGDAngleEstimation.from_pretrained(
-#     "maxwoe/image-rotation-angle-estimation",
-#     model_name="cgd_mambaout_base_coco2014.ckpt",
-# )
-
-# Predict rotation angle
-from PIL import Image
-image = Image.open("your_image.jpg")
-angle = model.predict_angle(image)
-print(f"Predicted rotation: {angle:.2f} degrees")
-```
-
-## Evaluation Results (COCO 2017, 5 seeds)
-
-| Metric | Value |
-|---|---|
-| MAE | 2.84 deg |
-| Median Error | 0.55 deg |
-| RMSE | 8.45 deg |
-| P90 Error | 3.54 deg |
-| P95 Error | 12.00 deg |
-| Accuracy at 2 deg | 90.2% |
-| Accuracy at 5 deg | 97.5% |
-| Accuracy at 10 deg | 98.1% |
-
-## Model Details
-
-- **Method:** Circular Gaussian Distribution (CGD) — 360 bins, sigma = 6.0 degrees
-- **Backbone:** MambaOut Base (`mambaout_base.in1k`), pretrained on ImageNet-1K
-- **Input size:** 224 x 224 pixels
-- **Output:** Probability distribution over 360 angle bins, converted to angle via argmax
-- **Loss:** KL Divergence with soft Gaussian labels
-- **Optimizer:** AdamW with ReduceLROnPlateau scheduler
-
-## License
-
-MIT
+---
+license: mit
+tags:
+  - image-rotation
+  - orientation-estimation
+  - angle-detection
+  - circular-gaussian-distribution
+  - mambaout
+  - pytorch
+datasets:
+  - coco
+metrics:
+  - mae
+pipeline_tag: image-classification
+---
+
+# Image Rotation Angle Estimation
+
+**[Try the interactive demo](https://huggingface.co/spaces/maxwoe/image-rotation-angle-estimation)** | **[GitHub](https://github.com/maxwoe/image-rotation-angle-estimation)** | **[Paper](https://arxiv.org/abs/2603.25351)**
+
+Predicts the rotation angle of an image using the **Circular Gaussian Distribution (CGD)** method with a **MambaOut Base** backbone.
+
+The model outputs a probability distribution over 360 angle bins (1 degree resolution) and extracts the predicted angle via argmax. It handles the full 360 degree range with no boundary discontinuities.
+
+## Available Checkpoints
+
+| Checkpoint | Dataset | MAE | Median Error |
+|---|---|---|---|
+| `cgd_mambaout_base_coco2017.ckpt` | COCO 2017 | 2.84° | 0.55° |
+| `cgd_mambaout_base_coco2014.ckpt` | COCO 2014 | 3.71° | 0.68° |
+
+## Usage
+
+Download the inference code from this Hub repo (`model_cgd.py`, `architectures.py`, `rotation_utils.py`), then:
+
+```python
+from model_cgd import CGDAngleEstimation
+from PIL import Image
+
+# Load model (defaults to COCO 2017 checkpoint)
+model = CGDAngleEstimation.from_pretrained("maxwoe/image-rotation-angle-estimation")
+
+# Or load a specific checkpoint
+# model = CGDAngleEstimation.from_pretrained(
+#     "maxwoe/image-rotation-angle-estimation",
+#     model_name="cgd_mambaout_base_coco2014.ckpt",
+# )
+
+image = Image.open("your_image.jpg")
+angle = model.predict_angle(image)
+print(f"Predicted rotation: {angle:.1f}°")
+```
+
+`predict_angle` accepts a PIL Image, numpy array, or file path.
+
+## Evaluation Results (COCO 2017, 5 seeds)
+
+| Metric | Value |
+|---|---|
+| MAE | 2.84° |
+| Median Error | 0.55° |
+| RMSE | 8.45° |
+| P90 Error | 3.54° |
+| P95 Error | 12.00° |
+| Accuracy at 2° | 90.2% |
+| Accuracy at 5° | 97.5% |
+| Accuracy at 10° | 98.1% |
+
+## Model Details
+
+- **Method:** Circular Gaussian Distribution (CGD), 360 bins, sigma = 6.0°
+- **Backbone:** MambaOut Base (`mambaout_base.in1k`), pretrained on ImageNet-1K
+- **Input size:** 224 x 224 pixels
+- **Output:** Probability distribution over 360 angle bins, converted to angle via argmax
+- **Loss:** KL Divergence with soft Gaussian labels
+- **Optimizer:** AdamW with ReduceLROnPlateau scheduler
+
+## License
+
+MIT