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2 | 2 |
|
3 | 3 | Clasificador de pistachos (Kirmizi vs Siirt) usando Deep Learning con PyTorch Lightning y MLOps. |
4 | 4 |
|
5 | | -## Estructura del proyecto |
6 | | - |
7 | | -``` |
8 | | -. |
9 | | -├── config/ |
10 | | -│ └── configuracion.yaml # Hiperparametros del modelo |
11 | | -├── src/ |
12 | | -│ ├── main.py # Entrypoint CLI (train/sweep) |
13 | | -│ ├── train.py # Entrenamiento con W&B logging |
14 | | -│ ├── sweep.py # W&B Sweeps (grid search) |
15 | | -│ ├── model.py # Modelos CNN (LightningModules) |
16 | | -│ ├── data_module.py # LightningDataModule (kagglehub) |
17 | | -│ ├── api_inferencia.py # FastAPI (POST /predict) |
18 | | -│ ├── utils.py # load_config, get_project_root |
19 | | -│ └── logging_config.py # Configuracion de logging |
20 | | -├── tests/ |
21 | | -│ ├── test_api.py |
22 | | -│ ├── test_data_module.py |
23 | | -│ ├── test_model.py |
24 | | -│ └── test_utils.py |
25 | | -├── notebook/ |
26 | | -│ └── pistachio.ipynb # Notebook exploratorio |
27 | | -├── Dockerfile # Containerizacion (MODE=train|api) |
28 | | -├── entrypoint.sh # Entrypoint flexible |
29 | | -├── .github/workflows/ci.yml # CI/CD con GitHub Actions |
30 | | -├── requirements.txt |
31 | | -└── pytest.ini |
32 | | -``` |
33 | | - |
34 | 5 | ## Dataset |
35 | 6 |
|
36 | 7 | [Pistachio Image Dataset](https://www.kaggle.com/datasets/muratkokludataset/pistachio-image-dataset) - 2 clases (Kirmizi_Pistachio, Siirt_Pistachio). Se descarga automaticamente via kagglehub. |
@@ -124,13 +95,13 @@ Respuesta: |
124 | 95 | ## W&B Report |
125 | 96 |
|
126 | 97 | - Proyecto: https://wandb.ai/14farresa-/pistachio-mlops |
127 | | -- Sweep: https://wandb.ai/14farresa-/pistachio-mlops/sweeps/2147j105 |
| 98 | +- Report: https://api.wandb.ai/links/14farresa-/9smbs51w |
128 | 99 |
|
129 | | -El proyecto incluye un W&B Report con: |
| 100 | +### Mejor run individual |
130 | 101 |
|
131 | | -- Resultados del sweep (learning rate, batch size, modelo) |
132 | | -- Comparacion de arquitecturas (BatchNorm vs Dropout) |
133 | | -- Metricas finales (accuracy, F1, matriz de confusion) |
| 102 | +| Modelo | LR | Batch | Test Acc | Test F1 | |
| 103 | +| ------------- | ------ | ----- | -------- | ------- | |
| 104 | +| cnn_batchnorm | 0.001 | 64 | 0.9317 | 0.9294 | |
134 | 105 |
|
135 | 106 | ### Resultados del sweep |
136 | 107 |
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