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diff --git a/formatting.py b/formatting.py
new file mode 100644
index 0000000000000000000000000000000000000000..28bd049ef06ebd888920de4151ede085d504f091
--- /dev/null
+++ b/formatting.py
@@ -0,0 +1,71 @@
+# This Python 3 environment comes with many helpful analytics libraries installed
+# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
+# For example, here's several helpful packages to load
+
+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+
+# Input data files are available in the read-only "../input/" directory
+# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
+
+import os
+for dirname, _, filenames in os.walk('/kaggle/input'):
+    for filename in filenames:
+        print(os.path.join(dirname, filename))
+
+# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
+# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
+
+
+import pandas as pd
+from fastai.vision.all import *
+from fastcore.all import *
+
+import fastai
+print(fastai.__version__)
+
+# Set the maximum image size to 10 billion pixels
+Image.MAX_IMAGE_PIXELS = 10000000000
+
+# enable pytorch fallback to cpu - will be slower but will work
+#os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'
+
+# using thumbnails while we figure out our out of memory problems
+path = Path('/kaggle/input/UBC-OCEAN/train_thumbnails')
+
+#check images count
+print(len(get_image_files(path)))
+
+df = pd.read_csv("/kaggle/input/UBC-OCEAN/train.csv")
+df.head(10)
+
+def get_x(r):
+    filename = f"{r['image_id']}.png"
+    if os.path.exists(path/filename):
+        return str(path/filename)
+    else:
+        return str("/kaggle/input/UBC-OCEAN/test_images/41.png")
+def get_y(r): return r['label']
+
+ovarianCancerDataBlock = DataBlock( 
+    blocks=(ImageBlock, CategoryBlock), 
+    splitter=RandomSplitter(valid_pct=0.2, seed=42), 
+    get_x = get_x, 
+    get_y = get_y,
+    item_tfms=Resize(460), 
+    batch_tfms=[*aug_transforms(size=224, min_scale=0.75), Normalize.from_stats(*imagenet_stats)]
+    )
+
+dls = ovarianCancerDataBlock.dataloaders(df, bs=2, num_workers=4)
+
+from fastai.callback.fp16 import *
+# make fast ai aware of resnet18 weights by moving them to the cache path for pytorch
+# check out https://forums.fast.ai/t/how-can-i-load-a-pretrained-model-on-kaggle-using-fastai/13941/24?page=2 for more info
+if not os.path.exists('/root/.cache/torch/hub/checkpoints/'):
+        os.makedirs('/root/.cache/torch/hub/checkpoints/')
+#!cp '/kaggle/input/torchvision-resnet-pretrained/resnet50-0676ba61.pth' '/root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth'
+#!cp '/kaggle/input/torchvision-resnet-pretrained/resnet18-f37072fd.pth' '/root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth'
+learn = vision_learner(dls, resnet18, metrics=[accuracy, error_rate]).to_fp16()
+#force gpu
+learn.model.cuda()
+learn.fine_tune(5, freeze_epochs=3)
\ No newline at end of file
diff --git a/loadImagesFromSVS.py b/loadImagesFromSVS.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf32dc890a6e8c40206fd804ebe6273b16ea617d
--- /dev/null
+++ b/loadImagesFromSVS.py
@@ -0,0 +1,61 @@
+import os
+import slideio
+from PIL import Image
+
+
+####################Convert SVS files to JPEG files####################
+# Set the path to the svs files
+svs_path = "/Users/josedelgado/Downloads/"
+
+# Set the path to the output directory
+output_path = "/Users/josedelgado/Documents/GitHub/microbiology/Images"
+
+# Set the size of the representative image
+size = (224, 224)
+
+# Loop through the svs files
+for root, dirs, files in os.walk(svs_path):
+    for filename in files:
+        if filename.endswith(".svs"):
+            print("processing " + os.path.join(root, filename)  )
+            # Open the svs file
+            slide = slideio.open_slide(os.path.join(root, filename))
+
+            # Get the number of scenes in the slide
+            num_scenes = slide.num_scenes
+ 
+            for i in range(num_scenes):
+
+                # Get the scene
+                scene = slide.get_scene(i)
+
+                # Check if the scene is not None
+                if scene is not None:
+                    # Get the metadata of the scene
+                    metadata_str = scene.get_raw_metadata()
+                    print(metadata_str)
+
+                    # Loop through the channels
+                    for j in range(scene.num_channels):
+
+                        # Read the channel datay
+                        channel_data = scene.read_block(channel_indices=[j], size=(8000,0))
+
+                        # Convert the channel data to a PIL image
+                        image = Image.fromarray(channel_data)
+
+                        # Set the output filename
+                        output_filename = os.path.splitext(filename)[0] + f"_scene_{i}_channel_{j}.jpeg"
+
+                        # Set the output directory
+                        output_dir = os.path.join(output_path, os.path.relpath(root, svs_path))
+
+                        # Create the output directory if it doesn't exist
+                        if not os.path.exists(output_dir):
+                            os.makedirs(output_dir)
+
+                        # Save the image in the ImageNet file folder format
+                        output_file = os.path.join(output_dir, output_filename)
+                        image.save(output_file)
+                else:
+                    print(f"Scene {i} is None for file {filename}")
\ No newline at end of file
diff --git a/makeImageTiles.py b/makeImageTiles.py
new file mode 100644
index 0000000000000000000000000000000000000000..39a57a286fa7701c9c77394c7736f86b8e2b8b96
--- /dev/null
+++ b/makeImageTiles.py
@@ -0,0 +1,62 @@
+
+
+def make_tiles(image_path, tile_size=1024, max_tiles=64, avg_thr=230, std_thr=15, clip_edge=0.05):
+
+    image = pyvips.Image.new_from_file(image_path, access='sequential')
+
+    # cropping
+    offset_x = int(image.width * clip_edge)
+    offset_y = int(image.height * clip_edge)
+    w = int(image.width * (1-clip_edge*2))
+    h = int(image.height * (1-clip_edge*2))
+    image = image.crop(offset_x, offset_y, w, h)
+
+    # padding
+    pad_w = (tile_size - image.width%tile_size)%tile_size
+    pad_h = (tile_size - image.height%tile_size)%tile_size
+    image = image.embed(
+        pad_w//2, pad_h//2,
+        image.width+pad_w, image.height+pad_h,
+        extend="mirror")
+
+    # Get the scanning position of the image
+    x_pos_list = []
+    y_pos_list = []
+    for y in range(0, image.height, tile_size):
+        for x in range(0, image.width, tile_size):
+            x_pos_list.append(x)
+            y_pos_list.append(y)
+
+    # Get the cropping position of the image
+    selected_x_pos_list = []
+    selected_y_pos_list = []
+    avg_list = []
+    for x, y in zip(x_pos_list, y_pos_list):
+        tile = image.crop(x, y, tile_size, tile_size)
+        avg = tile.avg()
+        std = tile.deviate()
+        if avg < avg_thr and std > std_thr:
+            selected_x_pos_list.append(x)
+            selected_y_pos_list.append(y)
+            avg_list.append(avg)
+
+    # Sort by ascending order of average brightness
+    sorted_idx = np.argsort(np.array(avg_list))
+    selected_x_pos_array = np.array(selected_x_pos_list)[sorted_idx][:max_tiles]
+    selected_y_pos_array = np.array(selected_y_pos_list)[sorted_idx][:max_tiles]
+
+    # crop
+    images = []
+    for x, y in zip(selected_x_pos_array, selected_y_pos_array):
+        tile = image.crop(x, y, tile_size, tile_size)
+        img = tile.numpy()
+        images.append(img)
+
+    if len(images) > 0:
+        images = np.stack(images)
+
+    del image
+    gc.collect()
+
+    return images
+
diff --git a/train.py b/train.py
deleted file mode 100644
index fdbf0bc649a72908c1c72647d0bef7c27c8f3737..0000000000000000000000000000000000000000
--- a/train.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import os
-import slideio
-from PIL import Image
-
-
-####################Convert SVS files to JPEG files####################
-# Set the path to the svs files
-svs_path = "/Users/josedelgado/Downloads/"
-
-# Set the path to the output directory
-output_path = "/Users/josedelgado/Documents/GitHub/microbiology/Images"
-
-# Set the size of the representative image
-size = (224, 224)
-
-# Loop through the svs files
-for root, dirs, files in os.walk(svs_path):
-    for filename in files:
-        if filename.endswith(".svs"):
-            print("processing " + os.path.join(root, filename)  )
-            # Open the svs file
-            slide = slideio.open_slide(os.path.join(root, filename))
-
-            # Read a representative image from the slide
-            scene = slide.get_scene(0)
-            print("region size: " + str(scene.size))
-
-            # Convert the image to RGB format
-            image = scene.read_block(size=(500,0))
-
-            # Set the output filename
-            output_filename = os.path.splitext(filename)[0] + ".jpeg"
-
-            # Set the output directory
-            output_dir = os.path.join(output_path, os.path.relpath(root, svs_path))
-
-            # Create the output directory if it doesn't exist
-            if not os.path.exists(output_dir):
-                os.makedirs(output_dir)
-
-            # Convert the numpy array to a PIL image
-            image = Image.fromarray(image)
-
-            # Save the image in the ImageNet file folder format
-            output_file = os.path.join(output_dir, output_filename)
-            image.save(output_file)
\ No newline at end of file
diff --git a/trainFastAI.py b/trainFastAI.py
new file mode 100644
index 0000000000000000000000000000000000000000..28bd049ef06ebd888920de4151ede085d504f091
--- /dev/null
+++ b/trainFastAI.py
@@ -0,0 +1,71 @@
+# This Python 3 environment comes with many helpful analytics libraries installed
+# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
+# For example, here's several helpful packages to load
+
+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+
+# Input data files are available in the read-only "../input/" directory
+# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
+
+import os
+for dirname, _, filenames in os.walk('/kaggle/input'):
+    for filename in filenames:
+        print(os.path.join(dirname, filename))
+
+# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
+# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
+
+
+import pandas as pd
+from fastai.vision.all import *
+from fastcore.all import *
+
+import fastai
+print(fastai.__version__)
+
+# Set the maximum image size to 10 billion pixels
+Image.MAX_IMAGE_PIXELS = 10000000000
+
+# enable pytorch fallback to cpu - will be slower but will work
+#os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'
+
+# using thumbnails while we figure out our out of memory problems
+path = Path('/kaggle/input/UBC-OCEAN/train_thumbnails')
+
+#check images count
+print(len(get_image_files(path)))
+
+df = pd.read_csv("/kaggle/input/UBC-OCEAN/train.csv")
+df.head(10)
+
+def get_x(r):
+    filename = f"{r['image_id']}.png"
+    if os.path.exists(path/filename):
+        return str(path/filename)
+    else:
+        return str("/kaggle/input/UBC-OCEAN/test_images/41.png")
+def get_y(r): return r['label']
+
+ovarianCancerDataBlock = DataBlock( 
+    blocks=(ImageBlock, CategoryBlock), 
+    splitter=RandomSplitter(valid_pct=0.2, seed=42), 
+    get_x = get_x, 
+    get_y = get_y,
+    item_tfms=Resize(460), 
+    batch_tfms=[*aug_transforms(size=224, min_scale=0.75), Normalize.from_stats(*imagenet_stats)]
+    )
+
+dls = ovarianCancerDataBlock.dataloaders(df, bs=2, num_workers=4)
+
+from fastai.callback.fp16 import *
+# make fast ai aware of resnet18 weights by moving them to the cache path for pytorch
+# check out https://forums.fast.ai/t/how-can-i-load-a-pretrained-model-on-kaggle-using-fastai/13941/24?page=2 for more info
+if not os.path.exists('/root/.cache/torch/hub/checkpoints/'):
+        os.makedirs('/root/.cache/torch/hub/checkpoints/')
+#!cp '/kaggle/input/torchvision-resnet-pretrained/resnet50-0676ba61.pth' '/root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth'
+#!cp '/kaggle/input/torchvision-resnet-pretrained/resnet18-f37072fd.pth' '/root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth'
+learn = vision_learner(dls, resnet18, metrics=[accuracy, error_rate]).to_fp16()
+#force gpu
+learn.model.cuda()
+learn.fine_tune(5, freeze_epochs=3)
\ No newline at end of file
diff --git a/trainPytorch.py b/trainPytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..599621023250d651750fd437f17ccff5bb2d4672
--- /dev/null
+++ b/trainPytorch.py
@@ -0,0 +1,88 @@
+#try this with pytorch directly
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchvision import models
+from torch.utils.data import DataLoader
+from torchvision.transforms import transforms
+from PIL import Image as PILImage
+import pandas as pd
+import os
+
+# Define the path to the train thumbnails
+trainPath = Path('/kaggle/input/UBC-OCEAN/train_images')
+
+# Define the transform to apply to each image
+transform = transforms.Compose([
+    transforms.Resize(460),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(*imagenet_stats)
+])
+
+
+# Define a function to load an image from a file
+def load_image(filename):
+    image = PILImage.open(filename)
+    image = transform(image)
+    return image
+
+# Define a function to load the train dataset
+def load_train_dataset():
+    # Load the train CSV file
+    df = pd.read_csv("/kaggle/input/UBC-OCEAN/train.csv")
+    # Create a list of tuples containing the image path and label
+    image_list = [(os.path.join(trainPath, str(row['image_id']) + '.png'), row['label']) for index, row in df.iterrows()]
+    # Create a dataset from the list of image tuples
+    dataset = [(load_image(f), l) for f, l in image_list]
+    return dataset
+
+# Load the train dataset
+train_dataset = load_train_dataset()
+
+# Create a dataloader for the train dataset
+train_dataloader = DataLoader(train_dataset, batch_size=16, shuffle=True)
+
+# Define the model architecture
+model = models.resnet18(pretrained=True)
+num_ftrs = model.fc.in_features
+model.fc = nn.Linear(num_ftrs, 2)
+
+# Move the model to the GPU
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+# Define the loss function and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
+
+# Train the model
+model.train()
+for epoch in range(5):
+    running_loss = 0.0
+    for i, batch in enumerate(train_dataloader):
+        # Get the inputs and labels
+        inputs, labels = batch
+        inputs, labels = inputs.to(device), labels.to(device)
+
+        # Zero the parameter gradients
+        optimizer.zero_grad()
+
+        # Forward pass
+        outputs = model(inputs)
+        loss = criterion(outputs, labels)
+
+        # Backward pass and optimization
+        loss.backward()
+        optimizer.step()
+
+        # Print statistics
+        running_loss += loss.item()
+        if i % 100 == 99:
+            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
+            running_loss = 0.0
+
+print('Finished training')
+
+# Save the model
+torch.save(model.state_dict(), 'ovarianCancerModel.pth')
\ No newline at end of file