From 630b84006c39142fde448b52d63e36d9db132001 Mon Sep 17 00:00:00 2001
From: Laihui Wei <laihui.wei@wur.nl>
Date: Mon, 21 Apr 2025 14:09:33 +0200
Subject: [PATCH] Modified the format of README
---
README.md | 27 ++++++++++++++-------------
1 file changed, 14 insertions(+), 13 deletions(-)
diff --git a/README.md b/README.md
index d113083..476db6e 100644
--- a/README.md
+++ b/README.md
@@ -32,33 +32,34 @@ DeepLearning MBE 8
In 1-6 experiment_{index}/ folder, you will find the following files:
- 1. one_step_training_ex_{index}.ipynb: A Jupyter Notebook that contains the code used to train the model for the corresponding experiment. It includes all the steps for data loading, model training, and saving the best weights.
+1. `one_step_training_ex_{index}.ipynb`: A Jupyter Notebook that contains the code used to train the model for the corresponding experiment. It includes all the steps for data loading, model training, and saving the best weights.
- 2. one_step_evaluation_ex_{index}.ipynb: A Jupyter Notebook used to evaluate the performance of the trained model. You can run this notebook with the evaluation results (local_evaluation_ex_{index} and test_set_evaluation_ex_{index}) obtained from the training notebook to compute the mAP for both the local and test sets.
+2. `one_step_evaluation_ex_{index}.ipynb`: A Jupyter Notebook used to evaluate the performance of the trained model. You can run this notebook with the evaluation results (local_evaluation_ex_{index} and test_set_evaluation_ex_{index}) obtained from the training notebook to compute the mAP for both the local and test sets.
- 3. test_set_evaluation_visualization_ex_{index}.ipynb: A Jupyter Notebook used to visualize the performance of the trained model. You can run this notebook with the evaluation results (test_set_evaluation_ex_{index}) obtained from the training notebook to visualize test images with bounding boxes.
+3. `test_set_evaluation_visualization_ex_{index}.ipynb`: A Jupyter Notebook used to visualize the performance of the trained model. You can run this notebook with the evaluation results (test_set_evaluation_ex_{index}) obtained from the training notebook to visualize test images with bounding boxes.
- 4. best_weights_ex_{index}.params: This file contains the best model weights that were obtained during the training of the corresponding experiment.
+4. `best_weights_ex_{index}.params`: This file contains the best model weights that were obtained during the training of the corresponding experiment.
- 5. local_evaluation_ex_{index}.txt: The val output of training notebook.
+5. `local_evaluation_ex_{index}.txt`: The val output of training notebook.
- 6. test_set_evaluation_ex_{index}.txt: The test set output of training notebook.
+6. `test_set_evaluation_ex_{index}.txt`: The test set output of training notebook.
- 7. test_set_evaluation_ex_{index}.png: Screenshot of the corresponding experiment results, which include output from Hugging Face.
+7. `test_set_evaluation_ex_{index}.png`: Screenshot of the corresponding experiment results, which include output from Hugging Face.
In experiment_7 Folder, the process is split into two stages: detection and classification. The workflow includes the following key files:
- 1. detection_training_ex_7.ipynb: Contains the code for training the detection model, which detects fruit regions without classifying them.
- 2. detector_best_weights_ex_7.params: Stores the best weights for the detection model after training.
+1. `detection_training_ex_7.ipynb`: Contains the code for training the detection model, which detects fruit regions without classifying them.
- 3. classification_training_ex_7.ipynb: Contains the code for training the classification model, which classifies the detected regions.
+2. `detector_best_weights_ex_7.params`: Stores the best weights for the detection model after training.
- 4. classifier_best_weights_ex_7.params: Stores the best weights for the classification model after training.
+3. `classification_training_ex_7.ipynb`: Contains the code for training the classification model, which classifies the detected regions.
- 5. two_step_inference_ex_7.ipynb: Runs inference by first detecting fruit regions and then classifying them, producing results for both detection and classification.
+4. `classifier_best_weights_ex_7.params`: Stores the best weights for the classification model after training.
- Additional files include detection and classification results for both the test and validation sets, as well as cropped images generated during detection.
+5. `two_step_inference_ex_7.ipynb`: Runs inference by first detecting fruit regions and then classifying them, producing results for both detection and classification.
+
+Additional files include detection and classification results for both the test and validation sets, as well as cropped images generated during detection.
## Usage
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GitLab