Spaces:
Sleeping
Sleeping
| title: Insect Detection | |
| emoji: π | |
| colorFrom: yellow | |
| colorTo: green | |
| sdk: docker | |
| app_file: Insect_HFspace_Streamlit_App.py | |
| pinned: false | |
| license: mit | |
| tags: | |
| - computer-vision | |
| - image-classification | |
| - insect-classification | |
| - deep-learning | |
| - tensorflow | |
| - mobilenet | |
| - efficientnet | |
| - resnet | |
| - inception | |
| # π¦ Multi-Model Insect Classification System - A Web/Mobile App | |
| ### Developed by Dr. Thyagharajan K K | |
| ## Description | |
| AI-powered insect classification application featuring multiple state-of-the-art deep learning models. Upload images to identify insect species with confidence scores and top-3 predictions. | |
| ## Features | |
| - π― Multiple pre-trained models (Inception V3, EfficientNet, ResNet50) | |
| - πΈ Upload custom images or use sample test images | |
| - π Confidence scores with top-3 predictions | |
| - π Fast inference with model caching | |
| - π± Responsive design for web and mobile | |
| ## Models Available | |
| - **Inception V3** - High accuracy, balanced performance | |
| - **EfficientNet B0** - Efficient and lightweight | |
| - **ResNet50** - Deep residual learning | |
| - (More models coming soon...) | |
| ## How to Use | |
| 1. Select a model from the dropdown | |
| 2. Upload an insect image or choose from sample images | |
| 3. Click "Predict" to get classification results | |
| 4. View predicted class with confidence score | |
| ## Technical Details | |
| - **Framework:** TensorFlow/Keras | |
| - **Input Size:** 300Γ300 pixels | |
| - **Interface:** Streamlit | |
| - **Hosted on:** Hugging Face Spaces | |
| ## License | |
| This project is licensed under the MIT License - see the LICENSE file for details. | |
| ## Citation | |
| If you use this application in your research or educational projects, please provide appropriate attribution. | |
| ## Contact | |
| For questions or collaboration opportunities, please open a discussion in this Space. |