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README.md

@@ -13,22 +13,18 @@ tags:
 
 # Flower Image Classifier (ResNet-18, transfer learning)
 
-🚀 **Try the live demo:** [flower-classifier-demo Space](https://huggingface.co/spaces/delcenjo/flower-classifier-demo) — upload a photo and get a prediction.
-📦 **Code on GitHub:** [delcenjo/flower-image-classifier](https://github.com/delcenjo/flower-image-classifier)
+Try the live demo: https://huggingface.co/spaces/delcenjo/flower-classifier-demo
+Code on GitHub: https://github.com/delcenjo/flower-image-classifier
 
-A small image classifier that recognises five flower species — **daisy,
-dandelion, roses, sunflowers, tulips** — using transfer learning on a
-pre-trained ResNet-18. The ImageNet backbone is frozen and only a new
-classification head is trained, so it runs well on CPU.
+A small image classifier that recognises five flower species (daisy, dandelion,
+roses, sunflowers, tulips) using transfer learning on a pre-trained ResNet-18.
+The ImageNet backbone is frozen and only a new classification head is trained,
+so it runs well on CPU.
 
-- **Architecture:** ResNet-18 (ImageNet weights), final layer replaced with a 5-class head
-- **Input:** RGB image resized to 128×128, normalised with ImageNet statistics
-- **Test accuracy:** ~0.77 (5 balanced classes; random baseline 0.20)
-- **Dataset:** TensorFlow Flowers (~3,670 images)
-
-## Files
-
-`flower_classifier.pt` is a checkpoint: `{"model_state": state_dict, "classes": [...]}`.
+- Architecture: ResNet-18 (ImageNet weights), final layer replaced with a 5-class head
+- Input: RGB image resized to 128x128, normalised with ImageNet statistics
+- Test accuracy: about 0.77 (5 balanced classes; random baseline 0.20)
+- Dataset: TensorFlow Flowers (about 3,670 images)
 
 ## Usage
 
@@ -63,5 +59,5 @@ print(classes[int(probs.argmax())], float(probs.max()))
 
 ## Limitations
 
-Trained on a small dataset at 128×128 with a frozen backbone, so accuracy is
-modest. Unfreezing the last ResNet block and training at 224×224 would improve it.
+Trained on a small dataset at 128x128 with a frozen backbone, so accuracy is
+modest. Unfreezing the last ResNet block and training at 224x224 would improve it.