Re-add scripts and README, removing previous large files

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: mit
+tags:
+- climate
+pretty_name: Sall-ee Project
+size_categories:
+- 10K<n<100K
+---
+
+Notice: train set include 80% of original dataset, test and val sets have 10%.

clear.py

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+import os
+import random
+
+# Change the static percentage parameter to scale down
+def remove_random_images(base_path, percentage=5):
+    """Removes 5% percentage of images randomly from each class subdirectory."""
+    subsets = ['test', 'train', 'val']
+    
+    for subset in subsets:
+        subset_path = os.path.join(base_path, subset)
+        if not os.path.exists(subset_path):
+            continue
+        
+        for class_name in os.listdir(subset_path):
+            class_path = os.path.join(subset_path, class_name)
+            if os.path.isdir(class_path):
+                images = [f for f in os.listdir(class_path) if f.endswith('.jpg')]
+                num_to_remove = int(len(images) * (percentage / 100))
+                
+                if num_to_remove > 0:
+                    images_to_remove = random.sample(images, num_to_remove)
+                    
+                    for img in images_to_remove:
+                        os.remove(os.path.join(class_path, img))
+                    
+                    print(f"Removed {num_to_remove} images from {subset}/{class_name}")
+
+# Define base path
+base_directory = "classify"
+
+# Run the function
+remove_random_images(base_directory)

count.py

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+import os
+
+def count_images_in_folders(base_path):
+    """Counts the number of images in each class subdirectory under test, train, and val."""
+    subsets = ['test', 'train', 'val']
+    
+    for subset in subsets:
+        subset_path = os.path.join(base_path, subset)
+        if not os.path.exists(subset_path):
+            continue
+        
+        for class_name in os.listdir(subset_path):
+            class_path = os.path.join(subset_path, class_name)
+            if os.path.isdir(class_path):
+                image_count = len([f for f in os.listdir(class_path) if f.endswith('.jpg')])
+                print(f"Folder {subset}/{class_name} has {image_count} images")
+
+# Define base path
+base_directory = "classify"
+
+# Run the function
+count_images_in_folders(base_directory)

split.py

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+import os
+import shutil
+import random
+
+# Define paths
+train_dir = "train"
+val_dir = "val"
+test_dir = "test"
+
+# Define split ratios
+train_ratio = 0.8
+val_ratio = 0.1
+test_ratio = 0.1
+
+# Ensure output directories exist
+for split_dir in [train_dir, val_dir, test_dir]:
+    os.makedirs(split_dir, exist_ok=True)
+
+# Get class names (subdirectories current dir)
+class_names = [d for d in os.listdir() if os.path.isdir(d) and d not in {"train", "val", "test"}]
+
+# Process each class
+for class_name in class_names:
+    class_path = class_name
+    images = [f for f in os.listdir(class_path) if os.path.isfile(os.path.join(class_path, f))]
+    
+    # Shuffle images randomly
+    random.shuffle(images)
+
+    # Compute split indices
+    total_images = len(images)
+    train_count = int(total_images * train_ratio)
+    val_count = int(total_images * val_ratio)
+
+    # Split images
+    train_images = images[:train_count]
+    val_images = images[train_count:train_count + val_count]
+    test_images = images[train_count + val_count:]
+
+    # Define destination directories for the class
+    for split_name, split_images in zip(["train", "val", "test"], [train_images, val_images, test_images]):
+        split_class_dir = os.path.join(split_name, class_name)
+        os.makedirs(split_class_dir, exist_ok=True)
+        
+        # Move images
+        for image in split_images:
+            src = os.path.join(class_path, image)
+            dst = os.path.join(split_class_dir, image)
+            shutil.move(src, dst)
+
+print("Dataset successfully split into train, val, and test sets.")