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license: cc-by-nc-sa-4.0 |
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# T. cruzi Detection Model |
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## Overview |
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This repository contains an object detection model trained using the TensorFlow Object Detection API for detecting *Trypanosoma cruzi* parasites in microscopic images. The model is based on SSD MobileNet V2 architecture and has been developed by Spotlab. |
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This model is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). |
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**Model ID:** 2xmn544x |
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## Model Details |
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- **Architecture:** SSD MobileNet V2 |
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- Training data: published on [zenodo](https://zenodo.org/records/15007339) |
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| Sample type | Training Image | Training Label | Validation Image | Validation Label | |
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| ----------------- | -------------- | -------------- | ---------------- | ---------------- | |
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| Human CSF | 261 | 512 | 68 | 191 | |
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| Human Blood thick | 61 | 55 | 26 | 14 | |
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| Human Blood thin | 156 | 95 | 154 | 64 | |
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| Mice Blood thin | 570 | 2648 | 105 | 503 | |
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| Total | 1048 | 3310 | 353 | 772 | |
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- **Performance:** |
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| Metrics | Human | Mice | |
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| --------- | ----- | ---- | |
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| Precision | 86 | 95.8 | |
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| Recall | 87 | 85 | |
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| F1 score | 86.5 | 90.1 | |
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## Usage Instructions |
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### Prepare the app |
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1. Download huggingSpot from google play store |
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2. Download model using model URL and API KEY |
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3. |
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<p align="center"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6509bcfc7e0d56c2717248be/SFFEvw8TPK-FWPqW5rLgg.png" alt="download_app" width="45%"/> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6509bcfc7e0d56c2717248be/QAcQvyQ85MB0QA_9KzdiO.png" alt="download_model" width="45%"/> |
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</p> |
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### Image Preparation |
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1. **Increase the zoom** until the inner square is clearly visible in the preview |
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2. Once the inner square appears, the model is ready for detection |
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### Example Images |
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Here are examples of properly prepared images for detection: |
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) |
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### Example Predictions |
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The model can detect T. cruzi parasites with high accuracy: |
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