Update README.md

README.md

@@ -6,4 +6,24 @@ base_model:
 - microsoft/resnet-50
 pipeline_tag: image-classification
 library_name: keras
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+---
+
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+This model is a ResNet-50 deep convolutional neural network fine-tuned for the FER-2013 (Facial Expression Recognition 2013) dataset. The dataset consists of low-resolution (48×48) grayscale images of faces categorized into seven core emotional states.
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+This project focused on maximizing the performance of the pre-trained ResNet-50 architecture on this particularly challenging, noisy, and imbalanced dataset.
+Training Details
+Architecture
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+    Base Model: ResNet-50 (pre-trained on ImageNet).
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+    Head: Custom dense layers (224 units) with a high 0.5 dropout rate.
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+    Transfer Learning Strategy: Deep Freezing. The model base was frozen up to the conv5 block, meaning only the final convolutional block (conv5) and the custom head were fine-tuned. This prevents early layers, which are optimized for high-resolution images, from being corrupted by the 48×48 input.
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+Optimization & Regularization
+Technique	Rationale
+Class Weighting	Applied inverse frequency weights to mitigate the severe class imbalance (e.g., Disgust is rare, Happy is abundant).
+Data Augmentation	Used random flips, translations, rotations, and zooms to artificially expand the small dataset and combat overfitting.
+High Dropout	Increased dropout to 0.5 to aggressively regularize the model and prevent the divergence seen in earlier training runs.
+Optimizer	Adam with a very low fine-tuning learning rate of 5e−6.