{
    "name": "10_Face_Recognition_FaceNet_LFW_DL",
    "query": "Help me create a PyTorch face recognition project using the FaceNet model with the LFW dataset. Load the dataset in `src/model.py`. Get the model from Hugging Face (you can find it at https://huggingface.co/py-feat/facenet) and save it in `models/saved_models/`. Ensure the data is preprocessed to ensure the standardization of facial images in `src/data_loader.py`. Use facial embeddings in `src/model.py` to improve the performance of your system. Print the recognition accuracy and save it to `results/metrics/recognition_accuracy.txt`. Next, visualize the embedding results and save them as `results/figures/embedding_visualization.png`. The model should load without issues, ideally with some error handling if something goes wrong. The visualizations should make it easy to see how the embeddings represent distinct facial features.",
    "tags": [
        "Computer Vision",
        "Supervised Learning"
    ],
    "requirements": [
        {
            "requirement_id": 0,
            "prerequisites": [],
            "criteria": "The \"LFW\" (Labeled Faces in the Wild) dataset is loaded in `src/data_loader.py`.",
            "category": "Dataset or Environment",
            "satisfied": null
        },
        {
            "requirement_id": 1,
            "prerequisites": [
                0
            ],
            "criteria": "Data alignment and standardization of facial images is performed in `src/data_loader.py`.",
            "category": "Data preprocessing and postprocessing",
            "satisfied": null
        },
        {
            "requirement_id": 2,
            "prerequisites": [],
            "criteria": "The \"FaceNet\" model in \"PyTorch\" is used, loading from [Hugging Face](https://huggingface.co/py-feat/facenet). Save the model in models/saved_models/.",
            "category": "Machine Learning Method",
            "satisfied": null
        },
        {
            "requirement_id": 3,
            "prerequisites": [
                0,
                1,
                2
            ],
            "criteria": "Facial embeddings are used in `src/model.py`.",
            "category": "Machine Learning Method",
            "satisfied": null
        },
        {
            "requirement_id": 4,
            "prerequisites": [
                0,
                1,
                2,
                3
            ],
            "criteria": "Recognition accuracy is printed and saved as `results/metrics/recognition_accuracy.txt`.",
            "category": "Performance Metrics",
            "satisfied": null
        },
        {
            "requirement_id": 5,
            "prerequisites": [
                0,
                1,
                2,
                3
            ],
            "criteria": "Embedding results are visualized and saved as `results/figures/embedding_visualization.png`.",
            "category": "Visualization",
            "satisfied": null
        }
    ],
    "preferences": [
        {
            "preference_id": 0,
            "criteria": "The model loading process should be smooth, with clear handling of any issues if the model fails to load.",
            "satisfied": null
        },
        {
            "preference_id": 1,
            "criteria": "Embedding visualizations should be clear and effectively highlight distinct facial features.",
            "satisfied": null
        }
    ],
    "is_kaggle_api_needed": false,
    "is_training_needed": false,
    "is_web_navigation_needed": true,
    "hint": "The page https://huggingface.co/py-feat/facenet provides guidance on how to use FaceNet; however, Hugging Face does not currently offer a model entry for direct use."
}