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filter=lfs diff=lfs merge=lfs -text +dataset/dataset/train/bearded[[:space:]]pig/RCNX3143.JPG filter=lfs diff=lfs merge=lfs -text +dataset/dataset/train/bearded[[:space:]]pig/RCNX3196.JPG filter=lfs diff=lfs merge=lfs -text +dataset/dataset/train/bearded[[:space:]]pig/RCNX3197.JPG filter=lfs diff=lfs merge=lfs -text diff --git a/dataset/COS 30019 Ultralytics/README.md b/dataset/COS 30019 Ultralytics/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b7b6fe62a6bb3094e101370f91e1ad6d0425a5af --- /dev/null +++ b/dataset/COS 30019 Ultralytics/README.md @@ -0,0 +1,60 @@ +# Ultralytics Wildlife Detect AI +This project features an AI program for Sarawak Wildlife +animal detection. +The AI model, built with PyTorch and leveraging Ultralytics +YOLOv11, identifies animals through a rigorous process of +training and validation. + +## Requirement of the AI models: +* **Python: 3.10.16 (Latest PyTorch requires Python 3.9 or later)** +* **PyTorch: 2.7.0** +* CUDA: 12.8 +* cuDNN: 9.8 + +This AI model offers flexible deployment options: +you can run it locally in your device, +within a Conda virtual environment, +or on Google Colab. +If opting for Conda, +remember to create a dedicated environment beforehand. +\ +The link of the Google Colab is [here](https://colab.research.google.com/drive/1yT3ovYlcu4lbgF0tfwcztI3ryiHk3eNj?usp=sharing). + +### Active Conda Virtual Environment: +Highly recommend running the model within a virtual environment. +This approach ensures that your main system environment remains unaffected, +preventing any potential conflicts with existing packages. + +```commandline +conda create -n ultralytics python=3.10.16 -y +conda active ultralytics +``` + +### Install Pytorch (GPU): +With conda virtual environment: +```commandline +conda install cuda=12.8 cudnn=9.8 -c nvidia -y +pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128 +``` + +### Install PyTorch (CPU): +```commandline +pip3 install torch torchvision torchaudio +``` + +### Requirement PIP Package: +All necessary package requirements are included within the Ultralytics package itself; +therefore, no additional installations are needed. + +```commandline +pip install ultralytics +``` + +## Resources: +[Python](https://www.python.org/) +\ +[Pytorch](https://pytorch.org/) +\ +[Ultralytics in GitHub](https://github.com/ultralytics/ultralytics) +\ +[Ultralytics Tutorial](https://docs.ultralytics.com/) diff --git a/dataset/COS 30019 Ultralytics/dataset_new/val.cache b/dataset/COS 30019 Ultralytics/dataset_new/val.cache new file mode 100644 index 0000000000000000000000000000000000000000..8f51d4c5b6821fa3085a82274e13e8f26103adfb Binary files /dev/null and b/dataset/COS 30019 Ultralytics/dataset_new/val.cache differ diff --git a/dataset/COS 30019 Ultralytics/dataset_new/val/bearded pig/RCNX3206.JPG b/dataset/COS 30019 Ultralytics/dataset_new/val/bearded pig/RCNX3206.JPG new file mode 100644 index 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sha256:4d9e26a3bf780ee53316e456958e14ee9dc3b531d3b30371bb92f70659fbd2a6 +size 620448 diff --git a/dataset/COS 30019 Ultralytics/main.py b/dataset/COS 30019 Ultralytics/main.py new file mode 100644 index 0000000000000000000000000000000000000000..3e81083013d03995686be34c84c031ca6bb7d943 --- /dev/null +++ b/dataset/COS 30019 Ultralytics/main.py @@ -0,0 +1,132 @@ +import os +import shutil +import random +from ultralytics import YOLO + +training_folder = "dataset/train" +detect_folder = "dataset/detect" +new_folder = "dataset_new" + +model = YOLO('yolo11n-cls.pt') +# YOLO models: yolo11n-cls.pt, yolo11s-cls.pt, yolo11m-cls.pt, yolo11l-cls.pt, yolo11x-cls.pt + +epochs = 10 # Train the image 10 times +imgsz = 224 # Change the image size to 224 * 224 +batch = 8 # Process 8 images each time +device = 'cpu' # 'cpu' or 0, 1, 2 +patience = 10 # Stop training if there is no improvement after 10 rounds +project = "animal_model" # Save the result in this folder +name = "wildlife" # Name of training +split = 0.8 # 80% for train, 20% for validation + + +# Organize Dataset +def OrganizeDataset(original_folder, new_folder): + # Generate training and validation folder + train_folder = f"{new_folder}/train" + val_folder = f"{new_folder}/val" + + # Search the picture in the folder + for animal_name in os.listdir(original_folder): + animal_path = f"{original_folder}/{animal_name}" + + if os.path.isdir(animal_path): + print(f"Process {animal_name} ...") + + # Create new folder for training and validation + os.makedirs(f"{train_folder}/{animal_name}", exist_ok=True) + os.makedirs(f"{val_folder}/{animal_name}", exist_ok=True) + + # Get whole image for the animal + all_images = [] + for file in os.listdir(animal_path): + if file.lower().endswith(('.jpg', '.jpeg', '.png')): + all_images.append(file) + + # Random Shuffle the image + random.shuffle(all_images) + + # Calculation split + split_point = int(len(all_images) * split) + + # Assign images to the training set + for i in range(split_point): + image_name = all_images[i] + # Copy image to training folder + shutil.copy2( + f"{animal_path}/{image_name}", + f"{train_folder}/{animal_name}/{image_name}" + ) + + # Assign images to the validation set + for i in range(split_point, len(all_images)): + image_name = all_images[i] + # Copy image to validation folder + shutil.copy2( + f"{animal_path}/{image_name}", + f"{val_folder}/{animal_name}/{image_name}" + ) + + print(f"\t{animal_name}: Training {split_point}, Validation {len(all_images) - split_point}.") + + print("Dataset organized successfully.") + return new_folder + +# Training AI models +def TrainModel(data_folder): + result = model.train( + data = data_folder, + epochs = epochs, + imgsz = imgsz, + batch = batch, + device = device, + patience = patience, + project = project, + name = name + ) + print("Model train successful.") + return result + +# Detect the whole image in the folder. +def DetectImageFolder(model_path, folder_path): + model = YOLO(model_path) + + image = [] + for file in os.listdir(folder_path): + # If the image ends with jpg, jpeg or png, then it will add the image to the list. + if file.lower().endswith(('.jpg', '.jpeg', '.png')): + image.append(os.path.join(folder_path, file)) + + # If the folder doesn`t have any image, it will stop the program and end it. + if not image: + print("The folder is empty.") + return + + print(f"Found {len(image)} image in the folder.") + + for i in image: + image_name = os.path.basename(i) + results = model(i) + + for result in results: + if result.probs is not None: + top_class = result.probs.top1 + confidence = result.probs.top1conf + class_name = result.names[top_class] + + print(f"Image name:\t{image_name}") + print(f"Class name:\t{class_name}") + print(f"Confidence:\t{confidence*100:.1f}%") + + +if __name__ == "__main__": + folder = OrganizeDataset(training_folder,new_folder) + + result = TrainModel(folder) + + # Finding the best model + best_model = result.save_dir / "weights" / "best.pt" + print(f"Save as:\t{best_model}") + + # Detect the image + DetectImageFolder(best_model, detect_folder) \ No newline at end of file diff --git a/dataset/COS 30019 Ultralytics/yolo11n-cls.pt b/dataset/COS 30019 Ultralytics/yolo11n-cls.pt new file mode 100644 index 0000000000000000000000000000000000000000..9906d560a6c041ce2aa8a61e6b8dfaeabbb8ad9d --- /dev/null +++ b/dataset/COS 30019 Ultralytics/yolo11n-cls.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c62d41bf9625777760018bf914d2e6cd472420ccd01706d97a61cb6c82502bd7 +size 5790624 diff --git a/dataset/COS30019 - Who did What.docx b/dataset/COS30019 - Who did What.docx new file mode 100644 index 0000000000000000000000000000000000000000..03601fdbf0b7877e2c39c435cfd07a94a318dede Binary files /dev/null and b/dataset/COS30019 - Who did What.docx differ diff --git a/dataset/COS30019_Assignment2Question_Sem1_2025 (Updated).pdf b/dataset/COS30019_Assignment2Question_Sem1_2025 (Updated).pdf new 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