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.gitignore

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+# Python cache
+__pycache__/
+*.pyc
+
+# OS files
+.DS_Store
+Thumbs.db
+
+# Environment
+.env
+
+# Hugging Face LFS
+*.pt
+*.bin
+*.onnx

LICENSE

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+MIT License
+
+Copyright (c) 2025 Winston Karanja Ngige
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the “Software”), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# 🐞 IP102 Pest Detector — YOLO11 Small
+
+A custom YOLO11 object detection model trained on the **IP102** dataset — designed for pest detection in precision agriculture.
+
+> **Model Purpose:** Detect and classify 102 pest species in real-time field conditions using computer vision.
+
+---
+
+## 💡 Model Details
+
+- **Model:** YOLO11 Small  
+- **Dataset:** IP102 (Balanced, 14K+ images)  
+- **Image Sizes:** Trained on 640x640 and 896x896  
+- **Classes:** 102 pest species  
+- **Framework:** Ultralytics YOLOv8  
+- **Hardware:** NVIDIA A100 GPU  
+- **Epochs:** 77  
+- **License:** MIT License  
+
+---
+
+## 🧪 Performance
+
+| Metric              | Train Set | Validation Set |
+|----------------------|-----------|-----------------|
+| Precision            | 0.912     | 0.744           |
+| Recall               | 0.923     | 0.789           |
+| mAP@0.5              | 0.941     | 0.815           |
+| mAP@0.5:0.95         | 0.838     | 0.605           |
+
+---
+
+## 📦 Usage
+
+```python
+from ultralytics import YOLO
+
+# Load model
+model = YOLO("path/to/best.pt")
+
+# Run inference
+results = model.predict("your_image.jpg", imgsz=640)
+
+# Display results
+results.show()
+🐜 Class List
+The model detects 102 agricultural pests, including:
+
+rice leaf roller
+
+paddy stem maggot
+
+brown plant hopper
+
+aphids
+
+mole cricket
+
+blister beetle
+...and many more!
+
+(See pests.yaml for the full class list.)
+
+⚖️ License
+This project is released under the MIT License — free for personal and commercial use.
+
+
+
+📚 Citation
+If you use this model in research or production, please cite the IP102 dataset:
+
+Wu, S., Zhan, C., et al.
+"IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition."
+CVPR, 2019.
+
+
+
+💬 Questions?
+Open an issue or reach me on Hugging Face Discussions.

model_card.yaml

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+license: mit
+library_name: ultralytics
+tags:
+  - yolo
+  - yolo11
+  - object-detection
+  - ip102
+  - pest-detection
+  - agriculture
+model-index:
+  - name: IP102 Pest Detector YOLO11 Small
+    results:
+      - task: object-detection
+        dataset:
+          name: IP102
+          type: images
+        metrics:
+          - name: Precision
+            value: 0.912
+          - name: Recall
+            value: 0.923
+          - name: mAP@0.5
+            value: 0.941
+          - name: mAP@0.5:0.95
+            value: 0.838

pests.yaml

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+train: /home/salter/Desktop/project-jumanji/hack/Dataset/output_dataset/images/train
+val: /home/salter/Desktop/project-jumanji/hack/Dataset/output_dataset/images/val
+test: /home/salter/Desktop/project-jumanji/hack/Dataset/output_dataset/images/test
+
+nc: 102
+
+names: ['rice leaf roller', 'rice leaf caterpillar', 'paddy stem maggot', 'asiatic rice borer', 'yellow rice borer', 'rice gall midge', 'Rice Stemfly', 'brown plant hopper', 'white backed plant hopper', 'small brown plant hopper', 'rice water weevil', 'rice leafhopper', 'grain spreader thrips', 'rice shell pest', 'grub', 'mole cricket', 'wireworm', 'white margined moth', 'black cutworm', 'large cutworm', 'yellow cutworm', 'red spider', 'corn borer', 'army worm', 'aphids', 'Potosiabre vitarsis', 'peach borer', 'english grain aphid', 'green bug', 'bird cherry-oataphid', 'wheat blossom midge', 'penthaleus major', 'longlegged spider mite', 'wheat phloeothrips', 'wheat sawfly', 'cerodonta denticornis', 'beet fly', 'flea beetle', 'cabbage army worm', 'beet army worm', 'Beet spot flies', 'meadow moth', 'beet weevil', 'sericaorient alismots chulsky', 'alfalfa weevil', 'flax budworm', 'alfalfa plant bug', 'tarnished plant bug', 'Locustoidea', 'lytta polita', 'legume blister beetle', 'blister beetle', 'therioaphis maculata Buckton', 'odontothrips loti', 'Thrips', 'alfalfa seed chalcid', 'Pieris canidia', 'Apolygus lucorum', 'Limacodidae', 'Viteus vitifoliae', 'Colomerus vitis', 'Brevipoalpus lewisi McGregor', 'oides decempunctata', 'Polyphagotars onemus latus', 'Pseudococcus comstocki Kuwana', 'parathrene regalis', 'Ampelophaga', 'Lycorma delicatula', 'Xylotrechus', 'Cicadella viridis', 'Miridae', 'Trialeurodes vaporariorum', 'Erythroneura apicalis', 'Papilio xuthus', 'Panonchus citri McGregor', 'Phyllocoptes oleiverus ashmead', 'Icerya purchasi Maskell', 'Unaspis yanonensis', 'Ceroplastes rubens', 'Chrysomphalus aonidum', 'Parlatoria zizyphus Lucus', 'Nipaecoccus vastalor', 'Aleurocanthus spiniferus', 'Tetradacus c Bactrocera minax', 'Dacus dorsalis(Hendel)', 'Bactrocera tsuneonis', 'Prodenia litura', 'Adristyrannus', 'Phyllocnistis citrella Stainton', 'Toxoptera citricidus', 'Toxoptera aurantii', 'Aphis citricola Vander Goot', 'Scirtothrips dorsalis Hood', 'Dasineura sp', 'Lawana imitata Melichar', 'Salurnis marginella Guerr', 'Deporaus marginatus Pascoe', 'Chlumetia transversa', 'Mango flat beak leafhopper', 'Rhytidodera bowrinii white', 'Sternochetus frigidus', 'Cicadellidae']
+
+

requirements.txt

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+ultralytics>=8.0.0
+huggingface_hub>=0.20.0
+gitpython>=3.1.31