First version

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+#########Datasets
+*.txt text eol=lf
+*.jpg filter=lfs diff=lfs merge=lfs -text
+########/Datasets

.gitignore

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+*.cache
+.vscode
+__pycache__
+*.pt
+
+runs
+wandb
+pretrained_weights

README.md

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----
-license: unknown
----
+# Layer Freezing and Transformer-Based Data Curation for Enhanced Transfer Learning in YOLO ArchitecturesCuration for Enhanced Transfer Learning in YOLO
+
+...
+
+## Table of Contents
+
+- [Installation](#installation)
+- [Usage](#usage)
+- [Examples](#examples)
+- [Contributing](#contributing)
+- [License](#license)
+
+## Installation
+
+### Using pip
+
+1. Clone the repository:
+
+   ```sh
+   git clone https://huggingface.co/AndrzejDD/enhanced-transfer-learning
+   cd enhanced-transfer-learning
+   ```
+2. Create a virtual environment (optional but recommended):
+
+   ```sh
+   python -m venv enhanced-tl
+   source enhanced-tl/bin/activate  # On Windows use `venv\Scripts\activate`
+   ```
+3. Install the required packages:
+
+   ```sh
+   pip install -r requirements.txt
+   ```
+
+### Using conda
+
+1. Clone the repository:
+
+   ```sh
+   git clone https://huggingface.co/AndrzejDD/enhanced-transfer-learning
+   cd enhanced-transfer-learning
+   ```
+2. Create a conda environment from the provided environment file:
+
+   ```sh
+   conda env create -f environment.yml
+   ```
+3. Activate the conda environment:
+
+   ```sh
+   conda activate enhanced-tl
+   ```
+
+After completing these steps, the required dependencies will be installed, and you can start training your models.
+
+## Usage
+
+To display the help message and see all available options, run the following command:
+
+```bash
+python3 main.py --help
+```
+
+### Example Output
+
+When you run the help command, you will see an output like this:
+
+```console
+usage: main.py [-h] [--dataset DATASET_NAME] [--epochs EPOCHS] [--batch BATCH] [--imgsz IMGSZ] 
+               [--patience PATIENCE] [--cache CACHE] [--pretrained] [--cos_lr] [--profile] [--plots] [--resume]
+               [--model MODEL_NAME] [--run RUN_NAME]
+
+options:
+  -h, --help            show this help message and exit
+  --dataset 		DATASET_NAME
+                        Dataset name to be used
+  --epochs EPOCHS       Number of epochs for training
+  --batch BATCH         Batch size
+  --imgsz IMGSZ         Image size for training
+  --patience PATIENCE   Early stopping patience
+  --cache CACHE         Caching mechanism to use
+  --pretrained          Use pretrained weights
+  --cos_lr              Use cosine learning rate schedule
+  --profile             Enable training profiling
+  --plots               Generate training plots
+  --resume              Resume training from a checkpoint
+  --model		MODEL_NAME
+                        Name of the YOLO model to use
+  --run 		RUN_NAME
+			Name of the run configuration
+
+```
+
+To run the project, use the following command:
+
+```bash
+python3 main.py --dataset "Dataset Name" --epochs 1000 --batch 16 --imgsz 640 --patience 30 --model "yolov10s" --run "Finetuning"
+```
+
+## Examples
+
+### Example 1: Fine-Tuning the YOLOv10 Model
+
+To fine-tune the YOLOv10 small model (`yolov10s`) with frozen backbone layers, run:
+
+```bash
+python3 main.py --dataset "Dataset Name" --epochs 1000 --batch 16 --pretrained --plots --model "yolov10s" --run "Finetuning"
+span
+```
+
+## License
+
+Please note that the license for each specific dataset should be checked from its source. Additionally, ensure to review the licenses for the YOLOv10 and YOLOv8 models as well.

datasets/Birds-Nest/dataset.yaml

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+names:
+- nest
+
+nc: 1
+
+train: train/images ##Comment when training just on similar images
+# train: train_similarity/images ##Uncomment when training just on similar images
+test: test
+val: val