diff --git a/extra/datasets/coco.py b/extra/datasets/coco.py
index b5d18886..008efae4 100644
--- a/extra/datasets/coco.py
+++ b/extra/datasets/coco.py
@@ -12,7 +12,7 @@ iou         = _mask.iou
 merge       = _mask.merge
 frPyObjects = _mask.frPyObjects
 
-BASEDIR = pathlib.Path(__file__).parent.parent / "extra" / "datasets" / "COCO"
+BASEDIR = pathlib.Path(__file__).parent / "COCO"
 BASEDIR.mkdir(exist_ok=True)
 
 def create_dict(key_row, val_row, rows): return {row[key_row]:row[val_row] for row in rows}
diff --git a/extra/datasets/imagenet.py b/extra/datasets/imagenet.py
index 5048314b..dde32a5e 100644
--- a/extra/datasets/imagenet.py
+++ b/extra/datasets/imagenet.py
@@ -5,7 +5,7 @@ import numpy as np
 from PIL import Image
 import functools, pathlib
 
-BASEDIR = pathlib.Path(__file__).parent.parent / "extra/datasets/imagenet"
+BASEDIR = pathlib.Path(__file__).parent / "imagenet"
 ci = json.load(open(BASEDIR / "imagenet_class_index.json"))
 cir = {v[0]: int(k) for k,v in ci.items()}
 
diff --git a/extra/datasets/imagenet_download.py b/extra/datasets/imagenet_download.py
index ebb017e4..7eca9dd2 100644
--- a/extra/datasets/imagenet_download.py
+++ b/extra/datasets/imagenet_download.py
@@ -14,38 +14,38 @@ def imagenet_extract(file, path, small=False):
 
 def imagenet_prepare_val():
   # Read in the labels file
-  with open(Path(__file__).parent.parent / "extra/datasets/imagenet/imagenet_2012_validation_synset_labels.txt", 'r') as f:
+  with open(Path(__file__).parent / "imagenet" / "imagenet_2012_validation_synset_labels.txt", 'r') as f:
     labels = f.read().splitlines()
   f.close()
   # Get a list of images
-  images = os.listdir(Path(__file__).parent.parent / "extra/datasets/imagenet/val")
+  images = os.listdir(Path(__file__).parent / "imagenet" / "val")
   images.sort()
   # Create folders and move files into those
   for co,dir in enumerate(labels):
-    os.makedirs(Path(__file__).parent.parent / "extra/datasets/imagenet/val" / dir, exist_ok=True)
-    os.replace(Path(__file__).parent.parent / "extra/datasets/imagenet/val" / images[co], Path(__file__).parent.parent / "extra/datasets/imagenet/val" / dir / images[co])
-  os.remove(Path(__file__).parent.parent / "extra/datasets/imagenet/imagenet_2012_validation_synset_labels.txt")
+    os.makedirs(Path(__file__).parent / "imagenet" / "val" / dir, exist_ok=True)
+    os.replace(Path(__file__).parent / "imagenet" / "val" / images[co], Path(__file__).parent / "imagenet" / "val" / dir / images[co])
+  os.remove(Path(__file__).parent / "imagenet" / "imagenet_2012_validation_synset_labels.txt")
 
 def imagenet_prepare_train():
-  images = os.listdir(Path(__file__).parent.parent / "extra/datasets/imagenet/train")
+  images = os.listdir(Path(__file__).parent / "imagenet" / "train")
   for co,tarf in enumerate(images):
     # for each tar file found. Create a folder with its name. Extract into that folder. Remove tar file
-    if Path(Path(__file__).parent.parent / "extra/datasets/imagenet/train" / images[co]).is_file():
+    if Path(Path(__file__).parent / "imagenet" / "train" / images[co]).is_file():
       images[co] = tarf[:-4] # remove .tar from extracted tar files
-      os.makedirs(Path(__file__).parent.parent / "extra/datasets/imagenet/train" / images[co], exist_ok=True)
-      imagenet_extract(Path(__file__).parent.parent / "extra/datasets/imagenet/train" / tarf, Path(__file__).parent.parent / "extra/datasets/imagenet/train" / images[co], small=True)
-      os.remove(Path(__file__).parent.parent / "extra/datasets/imagenet/train" / tarf)
+      os.makedirs(Path(__file__).parent / "imagenet" / "train" / images[co], exist_ok=True)
+      imagenet_extract(Path(__file__).parent / "imagenet" / "train" / tarf, Path(__file__).parent/ "imagenet" / "train" / images[co], small=True)
+      os.remove(Path(__file__).parent / "imagenet" / "train" / tarf)
 
 if __name__ == "__main__":
-  os.makedirs(Path(__file__).parent.parent / "extra/datasets/imagenet", exist_ok=True)
-  os.makedirs(Path(__file__).parent.parent / "extra/datasets/imagenet/val", exist_ok=True)
-  os.makedirs(Path(__file__).parent.parent / "extra/datasets/imagenet/train", exist_ok=True)
-  download_file("https://raw.githubusercontent.com/raghakot/keras-vis/master/resources/imagenet_class_index.json", Path(__file__).parent.parent / "extra/datasets/imagenet/imagenet_class_index.json")
-  download_file("https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/imagenet_2012_validation_synset_labels.txt", Path(__file__).parent.parent / "extra/datasets/imagenet/imagenet_2012_validation_synset_labels.txt")
-  download_file("https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar", Path(__file__).parent.parent / "extra/datasets/imagenet/ILSVRC2012_img_val.tar") # 7GB
-  imagenet_extract(Path(__file__).parent.parent / "extra/datasets/imagenet/ILSVRC2012_img_val.tar", Path(__file__).parent.parent / "extra/datasets/imagenet/val")
+  os.makedirs(Path(__file__).parent / "imagenet", exist_ok=True)
+  os.makedirs(Path(__file__).parent / "imagenet" / "val", exist_ok=True)
+  os.makedirs(Path(__file__).parent / "imagenet" / "train", exist_ok=True)
+  download_file("https://raw.githubusercontent.com/raghakot/keras-vis/master/resources/imagenet_class_index.json", Path(__file__).parent / "imagenet" / "imagenet_class_index.json")
+  download_file("https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/imagenet_2012_validation_synset_labels.txt", Path(__file__).parent / "imagenet"/ "imagenet_2012_validation_synset_labels.txt")
+  download_file("https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar", Path(__file__).parent / "imagenet" / "ILSVRC2012_img_val.tar") # 7GB
+  imagenet_extract(Path(__file__).parent / "imagenet" / "ILSVRC2012_img_val.tar", Path(__file__).parent / "imagenet" / "val")
   imagenet_prepare_val()
   if os.getenv('IMGNET_TRAIN', None) is not None:
-    download_file("https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar", Path(__file__).parent.parent / "extra/datasets/imagenet/ILSVRC2012_img_train.tar") #138GB!
-    imagenet_extract(Path(__file__).parent.parent / "extra/datasets/imagenet/ILSVRC2012_img_train.tar", Path(__file__).parent.parent / "extra/datasets/imagenet/train")
+    download_file("https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar", Path(__file__).parent / "imagenet" / "ILSVRC2012_img_train.tar") #138GB!
+    imagenet_extract(Path(__file__).parent / "imagenet" / "ILSVRC2012_img_train.tar", Path(__file__).parent / "imagenet" / "train")
     imagenet_prepare_train()
diff --git a/extra/datasets/kits19.py b/extra/datasets/kits19.py
index b49d3cc0..da51576f 100644
--- a/extra/datasets/kits19.py
+++ b/extra/datasets/kits19.py
@@ -9,7 +9,7 @@ import torch
 import torch.nn.functional as F
 from tinygrad.tensor import Tensor
 
-BASEDIR = Path(__file__).parent.parent.resolve() / "extra" / "datasets" / "kits19" / "data"
+BASEDIR = Path(__file__).parent / "kits19" / "data"
 
 """
 To download the dataset:
diff --git a/extra/datasets/librispeech.py b/extra/datasets/librispeech.py
index 2e85a335..434d7a05 100644
--- a/extra/datasets/librispeech.py
+++ b/extra/datasets/librispeech.py
@@ -15,7 +15,7 @@ for file in $(find * | grep flac); do ffmpeg -i $file -ar 16k "$(dirname $file)/
 
 Then this [file](https://github.com/mlcommons/inference/blob/master/speech_recognition/rnnt/dev-clean-wav.json) has to also be put in `extra/datasets/librispeech`.
 """
-BASEDIR = pathlib.Path(__file__).parent.parent / "extra/datasets/librispeech"
+BASEDIR = pathlib.Path(__file__).parent / "librispeech"
 with open(BASEDIR / "dev-clean-wav.json") as f:
   ci = json.load(f)
 
diff --git a/extra/datasets/openimages.py b/extra/datasets/openimages.py
index 641b6bd6..8e411e06 100644
--- a/extra/datasets/openimages.py
+++ b/extra/datasets/openimages.py
@@ -11,7 +11,7 @@ from tqdm import tqdm
 import pandas as pd
 import concurrent.futures
 
-BASEDIR = pathlib.Path(__file__).parent.parent / "extra/datasets/open-images-v6-mlperf"
+BASEDIR = pathlib.Path(__file__).parent / "open-images-v6-mlperf"
 BUCKET_NAME = "open-images-dataset"
 BBOX_ANNOTATIONS_URL = "https://storage.googleapis.com/openimages/v5/validation-annotations-bbox.csv"
 MAP_CLASSES_URL = "https://storage.googleapis.com/openimages/v5/class-descriptions-boxable.csv"
diff --git a/extra/datasets/squad.py b/extra/datasets/squad.py
index a69ee986..f9c1c7c9 100644
--- a/extra/datasets/squad.py
+++ b/extra/datasets/squad.py
@@ -5,7 +5,7 @@ from transformers import BertTokenizer
 import numpy as np
 from extra.utils import download_file
 
-BASEDIR = Path(__file__).parent.parent / "extra/datasets/squad"
+BASEDIR = Path(__file__).parent / "squad"
 def init_dataset():
   os.makedirs(BASEDIR, exist_ok=True)
   download_file("https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json", BASEDIR / "dev-v1.1.json")
@@ -141,7 +141,7 @@ def iterate(tokenizer, start=0):
     yield features, example
 
 if __name__ == "__main__":
-  tokenizer = BertTokenizer(str(Path(__file__).parent.parent / "weights/bert_vocab.txt"))
+  tokenizer = BertTokenizer(str(Path(__file__).parent.parent.parent / "weights" / "bert_vocab.txt"))
 
   X, Y = next(iterate(tokenizer))
   print(" ".join(X[0]["tokens"]))