diff --git a/filters/dark-filter.py b/filters/dark-filter.py
deleted file mode 100644
index 60ce24e..0000000
--- a/filters/dark-filter.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import os
-from PIL import Image
-import numpy as np
-
-def remove_dark_images(directory, threshold):
-    for filename in os.listdir(directory):
-        if filename.endswith(".jpg") or filename.endswith(".png"):
-            file_path = os.path.join(directory, filename)
-            image = Image.open(file_path)
-            image_data = np.asarray(image)
-            avg_pixel_value = np.mean(image_data)
-            if avg_pixel_value < threshold:
-                os.remove(file_path)
-                print(f"Removed {filename}")
-
-# Example usage
-remove_dark_images("./images/", 50)
-
diff --git a/filters/fit.py b/filters/fit.py
deleted file mode 100644
index 2e5b6b1..0000000
--- a/filters/fit.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import os
-from PIL import Image
-
-def resize_and_center_crop_images(directory, size):
-    for filename in os.listdir(directory):
-        if filename.endswith(".jpg") or filename.endswith(".png"):
-            file_path = os.path.join(directory, filename)
-            image = Image.open(file_path)
-            width, height = image.size
-            if width > height:
-                left = (width - height) / 2
-                top = 0
-                right = (width + height) / 2
-                bottom = height
-            else:
-                left = 0
-                top = (height - width) / 2
-                right = width
-                bottom = (height + width) / 2
-            image = image.crop((left, top, right, bottom))
-            image = image.resize(size, Image.LANCZOS)
-            image.save(file_path)
-            print(f"Resized and center cropped {filename}")
-
-# Example usage
-s = 256
-resize_and_center_crop_images("./images/", (s, s))
-
diff --git a/filters/hand-filter.py b/filters/hand-filter.py
deleted file mode 100644
index fd15f55..0000000
--- a/filters/hand-filter.py
+++ /dev/null
@@ -1,82 +0,0 @@
-from ultralytics import YOLO
-import cv2
-import os
-import shutil
-from tqdm import tqdm
-
-def person_area_ratio(image_path: str, model_path: str = "yolov8s.pt", total_area: float = None):
-    # Load YOLO model
-    model = YOLO(model_path)
-    
-    # Predict objects in image
-    results = model(image_path, verbose=False)
-    
-    # Find person (object class 0) and get area of bounding box
-    area_ratio = 0
-    for r in results:
-        # Get the Boxes object containing the detection boxes
-        boxes = r.boxes
-        
-        # Filter the boxes by class value 0 (person)
-        person_boxes = boxes[boxes.cls == 0]
-        
-        # Check if there is any person box
-        if len(person_boxes) > 0:
-            # Get the first person box
-            x1, y1, x2, y2 = person_boxes.data[0][:4]
-            
-            # Calculate the area of the bounding box
-            area = (x2 - x1) * (y2 - y1)
-            
-            # Calculate the total image area if not provided
-            if total_area is None:
-                image = cv2.imread(image_path)
-                h, w = image.shape[:2]
-                total_area = h * w
-            
-            # Calculate the ratio of the bounding box area to the total image area
-            area_ratio = round(float(area / total_area), 2)
-            
-            # Break the loop
-            break
-    
-    return area_ratio
-
-def main():
-    source_dir = "./images/"
-    destination_dir = "./new-images/"
-    
-    # Create destination directory if it doesn't exist
-    if not os.path.exists(destination_dir):
-        os.makedirs(destination_dir)
-    
-    # Calculate total image area for first image in source directory
-    total_area = None
-    for filename in os.listdir(source_dir):
-        if filename.endswith(".jpg") or filename.endswith(".jpeg") or filename.endswith(".png"):
-            image_path = os.path.join(source_dir, filename)
-            image = cv2.imread(image_path)
-            h, w = image.shape[:2]
-            total_area = h * w
-            break
-    
-    # Iterate over images in source directory with tqdm progress bar
-    with tqdm(os.listdir(source_dir), desc="Filtering images", unit="image") as pbar:
-        for filename in pbar:
-            # Check if file is an image
-            if filename.endswith(".jpg") or filename.endswith(".jpeg") or filename.endswith(".png"):
-                # Get image path
-                image_path = os.path.join(source_dir, filename)
-                
-                # Calculate ratio of bounding box area of detected person to total image area
-                area_ratio = person_area_ratio(image_path, "yolov8s.pt", total_area)
-                
-                # Check if ratio is below 0.35
-                if area_ratio < 0.35:
-                    # Copy image to destination directory
-                    shutil.copy(image_path, destination_dir)
-                    pbar.write(f"Copied {filename} to {destination_dir}")
-
-if __name__ == "__main__":
-    main()
-
diff --git a/filters/overlap-filter.py b/filters/overlap-filter.py
deleted file mode 100644
index 9ed4571..0000000
--- a/filters/overlap-filter.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import os
-import json
-import numpy as np
-from scipy.spatial.transform import Rotation as R
-from tqdm import tqdm
-from shutil import copyfile
-
-def calculate_frustum_overlap(frustum1, frustum2, K):
-    # Calculate the overlap between two camera frustums using the projection method
-    corners1 = np.dot(frustum1, K.T)
-    corners1 = corners1[:, :2] / corners1[:, 2:]
-    min_x1, min_y1 = np.min(corners1, axis=0)
-    max_x1, max_y1 = np.max(corners1, axis=0)
-    corners2 = np.dot(frustum2, K.T)
-    corners2 = corners2[:, :2] / corners2[:, 2:]
-    min_x2, min_y2 = np.min(corners2, axis=0)
-    max_x2, max_y2 = np.max(corners2, axis=0)
-    dx = min(max_x1, max_x2) - max(min_x1, min_x2)
-    dy = min(max_y1, max_y2) - max(min_y1, min_y2)
-    if dx >= 0 and dy >= 0:
-        overlap_area = dx * dy
-        area1 = (max_x1 - min_x1) * (max_y1 - min_y1)
-        overlap_ratio = overlap_area / area1
-        return overlap_ratio
-    else:
-        return 0.0
-
-def select_frames(file_path, overlap_threshold=0.60, target_ratio=1/3):
-    with open(file_path, 'r') as f:
-        data = json.load(f)
-    camera = data['camera']
-    fx, fy, cx, cy = camera['params'][:4]
-    K = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
-    images = data['images']
-    selected_frames = []
-    
-    print("Calculating camera frustums for all frames...")
-    for frame_name in tqdm(images.keys()):
-        pose = images[frame_name]
-        q = pose[:4]
-        t = pose[4:]
-        r = R.from_quat(q)
-        rot_mat = r.as_matrix()
-        trans_vec = np.array(t).reshape(3, 1)
-        transform_mat = np.hstack((rot_mat, trans_vec))
-        transform_mat = np.vstack((transform_mat, [0, 0, 0, 1]))
-        transform_mat = np.linalg.inv(transform_mat)
-        transform_mat[1,:] *= -1
-        transform_mat[:, 1] *= -1
-        # Calculate the camera frustum for the current frame
-        n=0.01 # near plane
-        f=100.0 # far plane
-        t=n/fx*(camera['height']/2-cy)
-        b=-t
-        r_=n/fy*(camera['width']/2-cx)
-        l_=-r_
-        n_corners=np.array([[l_,b,-n],[l_,t,-n],[r_,t,-n],[r_,b,-n]])
-        f_corners=np.array([[l_,b,-f],[l_,t,-f],[r_,t,-f],[r_,b,-f]])
-        corners_cam=np.vstack((n_corners,f_corners))
-        corners_cam_homo=np.hstack((corners_cam,np.ones((corners_cam.shape[0], 1))))
-        corners_world_homo=np.dot(corners_cam_homo , transform_mat.T)
-        selected_frames.append((frame_name,corners_world_homo))
-    
-    target_num_frames=int(len(selected_frames)*target_ratio)
-    
-    print("Removing frames with high overlap...")
-    batch_num = 0
-    while len(selected_frames)>target_num_frames:
-      print(f"Processing batch {batch_num}...")
-      overlaps=[]
-      for i in tqdm(range(0,len(selected_frames)-len(selected_frames)%2-1,2)):
-          frame1_frustum=selected_frames[i][1]
-          frame2_frustum=selected_frames[i+1][1]
-          overlap=calculate_frustum_overlap(frame1_frustum[:,:3],frame2_frustum[:,:3],K)
-          overlaps.append(overlap)
-
-      remove_indices=[]
-      for i in range(len(overlaps)):
-          if i+1 < len(overlaps) and overlaps[i]>overlap_threshold:
-              remove_indices.append(2*i+np.argmax([overlaps[i],overlaps[i+1]]))
-      
-      for idx in sorted(remove_indices)[::-1]:
-          del selected_frames[idx]
-      
-      if len(selected_frames)%2==1:
-          del selected_frames[-1]
-      
-      batch_num += 1
-
-    return [frame[0] for frame in selected_frames]
-
-json_data_dir = './JSON_DATA'
-frames_dir = './frames'
-selected_frames_dir = './selected_frames'
-
-for file_name in tqdm(os.listdir(json_data_dir)):
-    file_path = os.path.join(json_data_dir, file_name)
-    folder_name = os.path.splitext(file_name)[0]
-    src_folder_path = os.path.join(frames_dir, folder_name)
-    dst_folder_path = os.path.join(selected_frames_dir, folder_name)
-    if not os.path.exists(dst_folder_path):
-        os.makedirs(dst_folder_path)
-    selected_frames = select_frames(file_path)
-    for frame in tqdm(selected_frames):
-        # Copy the selected frame to the destination folder
-        src_file_path = os.path.join(src_folder_path, frame)
-        dst_file_path = os.path.join(dst_folder_path, frame)
-        copyfile(src_file_path, dst_file_path)
diff --git a/filters/unzipper.py b/filters/unzipper.py
deleted file mode 100644
index 63fa1ce..0000000
--- a/filters/unzipper.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import os
-import tarfile
-import zipfile
-import send2trash
-from tqdm import tqdm
-
-def check_files(directory):
-    corrupted = []
-    for filename in tqdm(os.listdir(directory), desc="Checking files"):
-        path = os.path.join(directory, filename)
-        if filename.endswith('.zip'):
-            with zipfile.ZipFile(path, 'r') as zip_ref:
-                if zip_ref.testzip():
-                    corrupted.append(filename)
-        elif filename.endswith(('.tar', '.tar.gz', '.tar.bz2')):
-            try:
-                with tarfile.open(path, 'r'):
-                    pass
-            except tarfile.ReadError:
-                corrupted.append(filename)
-    if corrupted:
-        print(f'Corrupted files: {", ".join(corrupted)}')
-        return False
-    return True
-
-def extract_files(directory):
-    for filename in tqdm(os.listdir(directory), desc="Extracting files"):
-        path = os.path.join(directory, filename)
-        extracted_dir = os.path.splitext(filename)[0]
-        extracted_path = os.path.join(directory, extracted_dir)
-        if filename.endswith('.zip'):
-            with zipfile.ZipFile(path, 'r') as zip_ref:
-                os.makedirs(extracted_path, exist_ok=True)
-                zip_ref.extractall(extracted_path)
-        elif filename.endswith(('.tar', '.tar.gz', '.tar.bz2')):
-            with tarfile.open(path, 'r') as tar_ref:
-                os.makedirs(extracted_path, exist_ok=True)
-                tar_ref.extractall(extracted_path)
-
-def delete_archives(directory):
-    for filename in tqdm(os.listdir(directory), desc="Deleting archives"):
-        if filename.endswith(('.tar', '.tar.gz', '.tar.bz2')):
-            file_path = os.path.join(directory, filename)
-            send2trash.send2trash(file_path)
-
-def process_directories(base_directory):
-    for sub_directory in tqdm(os.listdir(base_directory), desc="Processing directories"):
-        sub_dir_path = os.path.join(base_directory, sub_directory, 'rgb_frames')
-        if os.path.exists(sub_dir_path) and os.path.isdir(sub_dir_path):
-            print(f"Processing {sub_dir_path}")
-            if check_files(sub_dir_path):
-                extract_files(sub_dir_path)
-                delete_archives(sub_dir_path)
-                print(f"Unzipped and Cleaned {sub_dir_path}!")
-        else:
-            print("Could not find the folders correctly")
-
-# Main input directory
-main_directory = '../EPIC-KITCHENS'
-process_directories(main_directory)