Update README.md

README.md

@@ -41,11 +41,11 @@ size_categories:
 ---
 # Dataset Card for Fed-ISIC-2019
 
-Federated version of ISIC-2019 Datasets ([ISIC2019 challenge](https://challenge.isic-archive.com/landing/2019/) and the [HAM1000 database](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T)).The dataset contains 6 clients whose data comes from different data centers.
+Federated version of ISIC-2019 Datasets ([ISIC2019 challenge](https://challenge.isic-archive.com/landing/2019/) and the [HAM1000 database](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T)). This implementation is derived based on the [FLamby](https://github.com/owkin/FLamby/blob/main/flamby/datasets/fed_isic2019/README.md) implementation.
 
 ## Dataset Details
 
-The dataset contains 23,247 images of skin lesions. The number of samples for train/test per each data center is displayed in the table below.
+The dataset contains 23,247 images of skin lesions divided among 6 clients representing different data centers. The number of samples for training/testing per data center is displayed in the table below:
 
 | center_id |  Train  |  Test  |
 |:---------:|:-------:|:------:|
@@ -87,6 +87,65 @@ partition_train = fds.load_partition(partition_id=0, split="train")
 partition_test = fds.load_partition(partition_id=0, split="test")
 ```
 
+```
+# Note: to keep the same results as in FLamby, please apply the following transformation
+import albumentations
+import random
+import numpy as np
+import torch
+
+
+# Train dataset transformations
+def apply_train_transforms(image_input):
+    print(image_input)
+    size = 200
+    train_transforms = albumentations.Compose(
+        [
+            albumentations.RandomScale(0.07),
+            albumentations.Rotate(50),
+            albumentations.RandomBrightnessContrast(0.15, 0.1),
+            albumentations.Flip(p=0.5),
+            albumentations.Affine(shear=0.1),
+            albumentations.RandomCrop(size, size),
+            albumentations.CoarseDropout(random.randint(1, 8), 16, 16),
+            albumentations.Normalize(always_apply=True),
+        ]
+    )
+    images = []
+    for image in image_input["image"]:
+        augmented = train_transforms(image=np.array(image))["image"]
+        transposed = np.transpose(augmented, (2, 0, 1)).astype(np.float32)
+        images.append(torch.tensor(transposed, dtype=torch.float32))
+    image_input["image"] = images
+    return image_input
+
+
+partition_train = partition_train.with_transform(apply_train_transforms,
+                                                 columns="image")
+
+# Test dataset transformations
+def apply_test_transforms(image_input):
+    print(image_input)
+    size = 200
+    test_transforms = albumentations.Compose(
+        [
+            albumentations.CenterCrop(size, size),
+            albumentations.Normalize(always_apply=True),
+        ]
+    )
+    images = []
+    for image in image_input["image"]:
+        augmented = test_transforms(image=np.array(image))["image"]
+        transposed = np.transpose(augmented, (2, 0, 1)).astype(np.float32)
+        images.append(torch.tensor(transposed, dtype=torch.float32))
+    image_input["image"] = images
+    return image_input
+
+
+partition_test = partition_test.with_transform(apply_test_transforms,
+                                                 columns="image")
+```
+
 ## Dataset Structure
 
 ### Data Instances