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README.md

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+---
+tags:
+- image-classification
+- medicinal-plants
+- ayurveda
+- keras
+- xception
+library_name: keras
+license: apache-2.0
+---
+
+# AYUSH Medicinal Plant Classifier
+
+🌿 A deep learning model for classifying 7 Indian medicinal plants based on leaf images.
+
+## Model Description
+
+This model uses transfer learning with Xception architecture to identify medicinal plants commonly used in AYUSH (Ayurveda, Yoga & Naturopathy, Unani, Siddha, and Homeopathy) medicine.
+
+## Classes
+
+The model can identify the following 7 medicinal plants:
+
+1. **Aloevera** (Aloe barbadensis) - Ghritkumari
+2. **Amla** (Phyllanthus emblica) - Indian Gooseberry
+3. **Bhrami** (Bacopa monnieri) - Water Hyssop
+4. **Ginger** (Zingiber officinale) - Adrak
+5. **Neem** (Azadirachta indica) - Nimba
+6. **Tulsi** (Ocimum sanctum) - Holy Basil
+7. **Turmeric** (Curcuma longa) - Haldi
+
+## Model Details
+
+- **Base Architecture:** Xception (pre-trained on ImageNet)
+- **Input Size:** 299x299x3 RGB images
+- **Framework:** TensorFlow/Keras
+- **Training Accuracy:** 97.56%
+- **Validation Accuracy:** 100%
+- **Test Accuracy:** 98.44%
+- **Dataset:** Indian Medicinal Leaves Image Dataset (719 images)
+
+## Usage
+
+### Using Hugging Face Inference API
+
+import requests
+from PIL import Image
+import io
+
+API_URL = "https://api-inference.huggingface.co/models/Tushansh/ayush-medicinal-plant-classifier
+headers = {{"Authorization": "Bearer YOUR_HF_TOKEN"}}
+
+def query(image_bytes):
+response = requests.post(API_URL, headers=headers, data=image_bytes)
+return response.json()
+Load your image
+with open("plant_leaf.jpg", "rb") as f:
+data = f.read()
+
+Get prediction
+output = query(data)
+print(output)
+
+
+### Using Python with transformers
+
+from huggingface_hub import from_pretrained_keras
+
+model = from_pretrained_keras( + REPO_ID + )
+
+Process image and make predictions
+
+
+## Training Details
+
+- **Optimizer:** Adam
+- **Loss:** Categorical Crossentropy
+- **Callbacks:** EarlyStopping, ReduceLROnPlateau
+- **Data Augmentation:** Rotation, Zoom, Flip, Brightness adjustment
+
+## Limitations
+
+- Model is specifically trained for these 7 Indian medicinal plants
+- Best results with clear, well-lit leaf images
+- May not generalize to other plant species
+
+## Ethical Considerations
+
+This model is intended for educational and research purposes. Plant identification should be verified by botanical experts before use in medical applications.
+
+## Citation
+
+@misc{{ayush-plant-classifier,
+author = {{Your Name}},
+title = {{AYUSH Medicinal Plant Classifier}},
+year = {{2025}},
+publisher = {{Hugging Face}}
+}}
+
+## License
+
+Apache 2.0
+
+## Contact
+
+For questions or feedback, please open an issue in the repository.

config.json

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+{
+  "model_type": "image-classification",
+  "task": "image-classification",
+  "framework": "keras",
+  "architectures": [
+    "Xception"
+  ],
+  "num_classes": 7,
+  "image_size": 299,
+  "labels": [
+    "Aloevera",
+    "Amla",
+    "Bhrami",
+    "Ginger",
+    "Neem",
+    "Tulsi",
+    "Turmeric"
+  ],
+  "id2label": {
+    "0": "Aloevera",
+    "1": "Amla",
+    "2": "Bhrami",
+    "3": "Ginger",
+    "4": "Neem",
+    "5": "Tulsi",
+    "6": "Turmeric"
+  },
+  "label2id": {
+    "Aloevera": 0,
+    "Amla": 1,
+    "Bhrami": 2,
+    "Ginger": 3,
+    "Neem": 4,
+    "Tulsi": 5,
+    "Turmeric": 6
+  },
+  "preprocessing": {
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "size": 299,
+    "do_resize": true,
+    "do_normalize": true
+  }
+}

model.keras

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+oid sha256:f0e8c2bffdf4a4653c394d3bce1bebce2581de422f6e18ff515d7c8474c2bc90
+size 87106667