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README.md

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+---
+language: id
+license: mit
+tags:
+  - image-classification
+  - plant-identification
+  - pytorch
+  - cnn
+  - vision-transformer
+  - efficientnet
+  - vit
+datasets:
+  - gumi-banten
+metrics:
+  - accuracy
+model-index:
+  - name: CNN EfficientNet-B4 — Gumi Banten
+    results:
+      - task:
+            type: image-classification
+        metrics:
+          - type: accuracy
+            value: 0.9851
+---
+
+# 🌿 Gumi Banten Plant Identification — CNN EfficientNet-B4
+
+Model identifikasi tanaman Gumi Banten menggunakan arsitektur hybrid **CNN (EfficientNet-B4) + Vision Transformer (ViT)**.
+
+## 📊 Performa Model
+| Metrik | Nilai |
+|--------|-------|
+| **Test Accuracy** | 0.9851 (98.51%) |
+| **Best Val Accuracy** | 0.9876 (98.76%) |
+| **Jumlah Kelas** | 10 |
+
+## 🏗️ Arsitektur
+- **CNN Backbone**: EfficientNet-B4 (pretrained, global_pool=avg)
+- **Classifier Head**: Dropout → Linear(1792→512) → GELU → Dropout → Linear(512→N)
+- **Input Size**: 224×224 px
+- **Framework**: PyTorch (weights disimpan sebagai HDF5/.h5)
+
+## 📦 File dalam Repo
+| File | Deskripsi |
+|------|-----------|
+| `gumi_banten_cnn_vit.h5` | Model weights dalam format HDF5 |
+| `best_model.pth` | Checkpoint PyTorch lengkap |
+| `config.json` | Konfigurasi model |
+| `class_names.txt` | Daftar nama kelas |
+
+## 🚀 Cara Menggunakan
+
+### Load dari .pth (PyTorch — Direkomendasikan)
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+pth_path = hf_hub_download(repo_id="Wisnu1354/CNN-GumiBanten", filename="best_model.pth")
+ckpt = torch.load(pth_path, map_location='cpu')
+model.load_state_dict(ckpt['model_state'])
+model.eval()
+```
+
+### Load dari .h5
+```python
+import h5py, torch, numpy as np
+from huggingface_hub import hf_hub_download
+
+def load_from_h5(h5_path, model_class, cfg):
+    with h5py.File(h5_path, 'r') as hf:
+        class_names = list(hf['metadata/class_names'][:])
+        state_dict = {}
+        def _load(name, obj):
+            if isinstance(obj, h5py.Dataset):
+                state_dict[name.replace('/', '.')] = torch.tensor(obj[()])
+        hf['model_weights'].visititems(_load)
+    model = model_class(cfg)
+    model.load_state_dict(state_dict)
+    model.eval()
+    return model, class_names
+
+h5_path = hf_hub_download(repo_id="Wisnu1354/CNN-GumiBanten", filename="gumi_banten_cnn_vit.h5")
+model, class_names = load_from_h5(h5_path, EfficientNetB4, CFG)
+```
+
+## 📋 Kelas yang Didukung
+- Daun Ancak
+- Daun Base
+- Daun Bila
+- Daun Bingin
+- Daun Dapdap
+- Daun Intaran
+- Daun Kayu Tulak
+- Daun Kelor
+- Daun Nagasari
+- Daun Pucuk Rejuna
+
+## 🧑‍💻 Training
+Dilatih di Google Colab menggunakan GPU A100/V100/T4.
+
+---
+*Dibuat oleh: Gumi Banten Research | 2026-04-28*