Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+pipeline_tag: image-classification
+library_name: pytorch
+base_model: microsoft/swin-small-patch4-window7-224
+metrics:
+- accuracy
+- f1
+- auc
+tags:
+- swin-transformer
+- timm
+- image-classification
+- plant-disease
+- tea-leaf
+- rgb-hsv
+- color-aware
+datasets:
+- tea-leaf-disease
+language:
+- en
+---
+
+# Swin Transformer (RGB + HSV) for Tea Leaf Disease Classification 🌱🍃
+
+This repository provides a **Swin Transformer Small** model fine-tuned for **tea leaf disease classification** using a **color-aware RGB + HSV fusion** strategy.  
+The model achieves **strong generalization performance** with high accuracy and AUC on the test set.
+
+---
+
+## 🧠 Model Overview
+
+- **Architecture:** Swin Transformer Small (`swin_small_patch4_window7_224`)
+- **Pretrained:** Yes (ImageNet)
+- **Input:** RGB + HSV
+- **HSV Fusion:** Raw HSV channels (no sin/cos encoding)
+- **Gating:** Vector gate (disabled in this run)
+- **DropPath:** 0.2
+- **EMA:** Enabled
+- **AMP:** Enabled
+- **Framework:** PyTorch (timm-style training)
+
+---
+
+## 📐 Model Complexity
+
+| Metric | Value |
+|------|------|
+| Parameters | **49.47M** |
+| GFLOPs | **17.16** |
+| Weights size | **~200 MB** |
+
+---
+
+## 📊 Final Test Performance
+
+Evaluation performed using **EMA weights from the best checkpoint (epoch 93)**.
+
+| Metric | Score |
+|------|------|
+| **Top-1 Accuracy** | **96.01%** |
+| **Macro-F1** | **95.51%** |
+| **Macro-AUC** | **99.59%** |
+
+**Benchmark details**
+- Test images: **212**
+- Total inference time: **2.30s**
+- Throughput: **92.3 images/sec**
+
+---
+
+## 🚀 Inference Speed
+
+- **Post-warmup forward-only**
+- **92.3 img/s** on GPU
+
+---
+
+## 🗂️ Training Details
+
+- **Experiment name:** `swin_small_hsv_raw`
+- **Device:** CUDA
+- **Epochs:** 100
+- **Best checkpoint:** Epoch 93
+- **Gradient accumulation:** 1
+- **HSV gate warmup:** 5 epochs
+
+---
+
+## 📦 Model Files
+
+- `model.safetensors` — final EMA weights (recommended)
+- Config and training artifacts included in repository
+
+---
+
+## 🧪 Intended Use
+
+This model is designed for:
+- Tea leaf disease classification
+- Agricultural decision-support systems
+- Research on color-aware vision transformers
+
+⚠️ **Not intended as a medical or agronomic diagnostic tool.**
+
+---
+
+## ⚠️ Limitations
+
+- Performance may degrade under:
+  - extreme lighting changes
+  - motion blur
+  - unseen disease categories
+- Dataset-specific bias may exist
+
+---
+
+## 🧑‍💻 How to Use (PyTorch + timm)
+
+```python
+import timm
+import torch
+from PIL import Image
+from torchvision import transforms
+
+# Create model
+model = timm.create_model(
+    "swin_small_patch4_window7_224",
+    pretrained=False,
+    num_classes=NUM_CLASSES
+)
+
+# Load weights
+state = torch.load("model.safetensors", map_location="cpu")
+model.load_state_dict(state, strict=False)
+model.eval()
+
+# Preprocessing
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=(0.485, 0.456, 0.406),
+        std=(0.229, 0.224, 0.225)
+    )
+])
+
+img = Image.open("tea_leaf.jpg").convert("RGB")
+x = transform(img).unsqueeze(0)
+
+with torch.no_grad():
+    logits = model(x)
+pred = logits.argmax(dim=1).item()
+
+print("Predicted class:", pred)
+
+---