Spaces:
Sleeping
Sleeping
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,68 +1,68 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Insect Detection
|
| 3 |
-
emoji: π
|
| 4 |
-
colorFrom: yellow
|
| 5 |
-
colorTo: green
|
| 6 |
-
sdk: docker
|
| 7 |
-
app_file: Insect_HFspace_Streamlit_App.py
|
| 8 |
-
pinned: false
|
| 9 |
-
license: mit
|
| 10 |
-
tags:
|
| 11 |
-
- computer-vision
|
| 12 |
-
- image-classification
|
| 13 |
-
- insect-classification
|
| 14 |
-
- deep-learning
|
| 15 |
-
- tensorflow
|
| 16 |
-
- mobilenet
|
| 17 |
-
- efficientnet
|
| 18 |
-
- resnet
|
| 19 |
-
- inception
|
| 20 |
-
---
|
| 21 |
-
|
| 22 |
-
# π¦ Multi-Model Insect Classification System - A Web/Mobile App
|
| 23 |
-
### Developed by Dr. Thyagharajan K K
|
| 24 |
-
|
| 25 |
-
## Description
|
| 26 |
-
|
| 27 |
-
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.
|
| 28 |
-
|
| 29 |
-
## Features
|
| 30 |
-
|
| 31 |
-
- π― Multiple pre-trained models (Inception V3, EfficientNet, ResNet50)
|
| 32 |
-
- πΈ Upload custom images or use sample test images
|
| 33 |
-
- π Confidence scores with top-3 predictions
|
| 34 |
-
- π Fast inference with model caching
|
| 35 |
-
- π± Responsive design for web and mobile
|
| 36 |
-
|
| 37 |
-
## Models Available
|
| 38 |
-
|
| 39 |
-
- **Inception V3** - High accuracy, balanced performance
|
| 40 |
-
- **EfficientNet B0** - Efficient and lightweight
|
| 41 |
-
- **ResNet50** - Deep residual learning
|
| 42 |
-
- (More models coming soon...)
|
| 43 |
-
|
| 44 |
-
## How to Use
|
| 45 |
-
|
| 46 |
-
1. Select a model from the dropdown
|
| 47 |
-
2. Upload an insect image or choose from sample images
|
| 48 |
-
3. Click "Predict" to get classification results
|
| 49 |
-
4. View predicted class with confidence score
|
| 50 |
-
|
| 51 |
-
## Technical Details
|
| 52 |
-
|
| 53 |
-
- **Framework:** TensorFlow/Keras
|
| 54 |
-
- **Input Size:** 300Γ300 pixels
|
| 55 |
-
- **Interface:** Streamlit
|
| 56 |
-
- **Hosted on:** Hugging Face Spaces
|
| 57 |
-
|
| 58 |
-
## License
|
| 59 |
-
|
| 60 |
-
This project is licensed under the MIT License - see the LICENSE file for details.
|
| 61 |
-
|
| 62 |
-
## Citation
|
| 63 |
-
|
| 64 |
-
If you use this application in your research or educational projects, please provide appropriate attribution.
|
| 65 |
-
|
| 66 |
-
## Contact
|
| 67 |
-
|
| 68 |
For questions or collaboration opportunities, please open a discussion in this Space.
|
|
|
|
| 1 |
+
---
|
| 2 |
+
title: Insect Detection
|
| 3 |
+
emoji: π
|
| 4 |
+
colorFrom: yellow
|
| 5 |
+
colorTo: green
|
| 6 |
+
sdk: docker
|
| 7 |
+
app_file: Insect_HFspace_Streamlit_App.py
|
| 8 |
+
pinned: false
|
| 9 |
+
license: mit
|
| 10 |
+
tags:
|
| 11 |
+
- computer-vision
|
| 12 |
+
- image-classification
|
| 13 |
+
- insect-classification
|
| 14 |
+
- deep-learning
|
| 15 |
+
- tensorflow
|
| 16 |
+
- mobilenet
|
| 17 |
+
- efficientnet
|
| 18 |
+
- resnet
|
| 19 |
+
- inception
|
| 20 |
+
---
|
| 21 |
+
|
| 22 |
+
# π¦ Multi-Model Insect Classification System - A Web/Mobile App
|
| 23 |
+
### Developed by Dr. Thyagharajan K K
|
| 24 |
+
|
| 25 |
+
## Description
|
| 26 |
+
|
| 27 |
+
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.
|
| 28 |
+
|
| 29 |
+
## Features
|
| 30 |
+
|
| 31 |
+
- π― Multiple pre-trained models (Inception V3, EfficientNet, ResNet50)
|
| 32 |
+
- πΈ Upload custom images or use sample test images
|
| 33 |
+
- π Confidence scores with top-3 predictions
|
| 34 |
+
- π Fast inference with model caching
|
| 35 |
+
- π± Responsive design for web and mobile
|
| 36 |
+
|
| 37 |
+
## Models Available
|
| 38 |
+
|
| 39 |
+
- **Inception V3** - High accuracy, balanced performance
|
| 40 |
+
- **EfficientNet B0** - Efficient and lightweight
|
| 41 |
+
- **ResNet50** - Deep residual learning
|
| 42 |
+
- (More models coming soon...)
|
| 43 |
+
|
| 44 |
+
## How to Use
|
| 45 |
+
|
| 46 |
+
1. Select a model from the dropdown
|
| 47 |
+
2. Upload an insect image or choose from sample images
|
| 48 |
+
3. Click "Predict" to get classification results
|
| 49 |
+
4. View predicted class with confidence score
|
| 50 |
+
|
| 51 |
+
## Technical Details
|
| 52 |
+
|
| 53 |
+
- **Framework:** TensorFlow/Keras
|
| 54 |
+
- **Input Size:** 300Γ300 pixels
|
| 55 |
+
- **Interface:** Streamlit
|
| 56 |
+
- **Hosted on:** Hugging Face Spaces
|
| 57 |
+
|
| 58 |
+
## License
|
| 59 |
+
|
| 60 |
+
This project is licensed under the MIT License - see the LICENSE file for details.
|
| 61 |
+
|
| 62 |
+
## Citation
|
| 63 |
+
|
| 64 |
+
If you use this application in your research or educational projects, please provide appropriate attribution.
|
| 65 |
+
|
| 66 |
+
## Contact
|
| 67 |
+
|
| 68 |
For questions or collaboration opportunities, please open a discussion in this Space.
|