Update README.md

README.md

@@ -1,10 +1,17 @@
 ---
 license: creativeml-openrail-m
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- deep-fake
+- detectioon
 ---
 
-
 ![pipeline](dfd.jpg)
 
+
+# **Image-Deep-Fake-Detector**
+
 ```
 Classification report:
 
@@ -16,4 +23,40 @@ Classification report:
     accuracy                         0.9935      9521
    macro avg     0.9935    0.9935    0.9935      9521
 weighted avg     0.9935    0.9935    0.9935      9521
-```
+```
+
+The **precision score** is a key metric to evaluate the performance of a deep fake detector. Precision is defined as:
+
+\[
+\text{Precision} = \frac{\text{True Positives}}{\text{True Positives + False Positives}}
+\]
+
+It indicates how well the model avoids false positives, which in the context of a deep fake detector means it measures how often the "Fake" label is correctly identified without mistakenly classifying real content as fake.
+
+From the **classification report**, the precision values are:
+
+- **Real:** 0.9933
+- **Fake:** 0.9937
+- **Macro average:** 0.9935
+- **Weighted average:** 0.9935
+
+### Key Observations:
+1. **High precision (0.9933 for Real, 0.9937 for Fake):**  
+   The model rarely misclassifies real content as fake and vice versa. This is critical for applications like deep fake detection, where false accusations (false positives) can have significant consequences.
+
+2. **Macro and Weighted Averages (0.9935):**  
+   The precision is evenly high across both classes, which shows that the model is well-balanced in its performance for detecting both real and fake content.
+
+3. **Reliability of Predictions:**  
+   With precision near 1.0, when the model predicts a video as fake (or real), it's highly likely to be correct. This is essential in reducing unnecessary manual verification in real-world applications like social media content moderation or fraud detection.
+
+### ONNX Exchange
+
+The ONNX model is converted using the following method, which directly writes the ONNX files to the repository using the Hugging Face write token.
+
+🧪 : https://huggingface.co/spaces/prithivMLmods/convert-to-onnx-dir
+
+![Screenshot 2025-01-27 at 19-03-01 ONNX - a Hugging Face Space by prithivMLmods.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/5T979tVYJ4jCKzlE6nOma.png)
+
+### Conclusion:
+The deep fake detector model demonstrates **excellent precision** for both the "Real" and "Fake" classes, indicating a highly reliable detection system with minimal false positives. Combined with similarly high recall and F1-score, the overall accuracy (99.35%) reflects that this is a robust and trustworthy model for identifying deep fakes.