Update README.md

README.md

@@ -1,199 +1,115 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+datasets:
+- Maxwell-Jia/kepler_flare
+language:
+- en
+metrics:
+- precision
+- recall
 ---
 
-# Model Card for Model ID
+# FCN4Flare
+
+FCN4Flare is a fully convolutional neural network designed for precise point-to-point detection of stellar flares in photometric time-series data. Stellar flares provide valuable insights into stellar magnetic activity and space weather environments, but detecting these flares is challenging due to missing data, imbalanced classes, and diverse flare morphologies. FCN4Flare addresses these challenges with:
+
+- **NaN Mask**: A mechanism to handle missing data points effectively during training.
+- **Mask Dice Loss**: A loss function tailored to mitigate class imbalance by optimizing the overlap between predicted and true flares.
+
+FCN4Flare achieves state-of-the-art performance on the Kepler flare dataset, significantly surpassing previous methods such as Flatwrm2 and Stella. Key performance metrics are summarized in the table below:
+
+| Metric               | FCN4Flare | Flatwrm2 | Stella |
+|----------------------|-----------|----------|--------|
+| Recall               | **0.67**  | 0.26     | 0.50   |
+| Precision            | **0.69**  | 0.08     | 0.09   |
+| F1 Score             | **0.64**  | 0.13     | 0.16   |
+| Average Precision    | **0.55**  | 0.12     | 0.14   |
+| Dice Coefficient     | **0.64**  | 0.12     | 0.15   |
+| Intersection over Union (IoU) | **0.54**  | 0.10     | 0.13   |
+
+
+
+## Inference with AutoModel API
+
+```python
+from transformers import AutoModel
+import torch
+
+model = AutoModel.from_pretrained("Maxwell-Jia/fcn4flare")
+
+# Load your data and create required tensors
+# You need to implement your own data loading logic that returns:
+# 1. input_features: tensor of flux values, shape [batch_size, sequence_length, 1]
+#    - Contains the actual flux measurements and padded values
+# 2. sequence_mask: binary tensor, shape [batch_size, sequence_length]
+#    - 1 indicates real flux values
+#    - 0 indicates padded positions
+input_features, sequence_mask = load_data()
+
+# Example of expected tensor shapes and values:
+# input_features = torch.tensor([
+#     [1.2, 1.5, 1.1, nan, nan],  # nan are padded values
+#     [1.3, 1.4, 1.6, 1.2, 1.1]   # all real values
+# ])
+# sequence_mask = torch.tensor([
+#     [1, 1, 1, 0, 0],  # last 2 positions are padded
+#     [1, 1, 1, 1, 1]   # no padding
+# ])
+
+logits = model(input_features, sequence_mask)
+
+# Apply a threshold to get binary predictions
+threshold = 0.5
+predictions = (logits > threshold).float()
+
+# Implement your own post-processing logic to reduce false positives
+# The post-processing step is crucial for:
+# 1. Filtering out noise and spurious detections
+# 2. Merging nearby detections
+# 3. Applying additional threshold or rule-based filtering
+#
+# Example post-processing strategies:
+# - Apply minimum duration threshold
+# - Merge events that are too close in time
+# - Consider the amplitude of the detected events
+# - Use domain knowledge to validate detections
+final_results = post_process_predictions(predictions)
+
+# Example implementation:
+# def post_process_predictions(predictions):
+#     # Apply minimum duration filter
+#     # Remove detections shorter than X minutes
+#     # Merge events within Y minutes of each other
+#     # Apply additional validation rules
+#     return processed_results
+```
+
+## Inference with pipeline API
+
+```python
+from transformers import pipeline
+
+flare_detector = pipeline("flare-detection", model="Maxwell-Jia/fcn4flare")
+# Only surport for Kepler/K2 light curves now.
+results = flare_detector([
+    "Path/to/your/lightcurve.fits",
+    "Path/to/your/lightcurve.fits",
+    ...
+])
+
+print(results)
+```
+
+## **Citation**
+
+If you find this work useful, please cite our paper:
+```bibtex
+@article{jia2024fcn4flare,
+  title={FCN4Flare: Fully Convolution Neural Networks for Flare Detection},
+  author={Minghui Jia, A-Li Luo, Bo Qiu},
+  journal={arXiv preprint arXiv:2407.21240},
+  year={2024}
+}
+```
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
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-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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-### Direct Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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