diff --git a/feature_eval/mini_batch_logistic_regression_evaluator.ipynb b/feature_eval/mini_batch_logistic_regression_evaluator.ipynb
index f64e920..faa96aa 100644
--- a/feature_eval/mini_batch_logistic_regression_evaluator.ipynb
+++ b/feature_eval/mini_batch_logistic_regression_evaluator.ipynb
@@ -24,10 +24,7 @@
       "provenance": [],
       "include_colab_link": true
     },
-    "accelerator": "GPU",
-    "widgets": {
-      "application/vnd.jupyter.widget-state+json": {}
-    }
+    "accelerator": "GPU"
   },
   "cells": [
     {
@@ -228,7 +225,7 @@
         "id": "BfIPl0G6_RrT"
       },
       "source": [
-        "def get_stl10_data_loaders(download, shuffle=False, batch_size=128):\n",
+        "def get_stl10_data_loaders(download, shuffle=False, batch_size=256):\n",
         "  train_dataset = datasets.STL10('./data', split='train', download=download,\n",
         "                                  transform=transforms.ToTensor())\n",
         "\n",
@@ -242,7 +239,7 @@
         "                            num_workers=10, drop_last=False, shuffle=shuffle)\n",
         "  return train_loader, test_loader\n",
         "\n",
-        "def get_cifar10_data_loaders(download, shuffle=False, batch_size=128):\n",
+        "def get_cifar10_data_loaders(download, shuffle=False, batch_size=256):\n",
         "  train_dataset = datasets.CIFAR10('./data', train=True, download=download,\n",
         "                                  transform=transforms.ToTensor())\n",
         "\n",
@@ -282,7 +279,7 @@
         "elif config.arch == 'resnet50':\n",
         "  model = torchvision.models.resnet50(pretrained=False, num_classes=10).to(device)"
       ],
-      "execution_count": 11,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -302,7 +299,7 @@
         "      state_dict[k[len(\"backbone.\"):]] = state_dict[k]\n",
         "  del state_dict[k]"
       ],
-      "execution_count": 12,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -314,7 +311,7 @@
         "log = model.load_state_dict(state_dict, strict=False)\n",
         "assert log.missing_keys == ['fc.weight', 'fc.bias']"
       ],
-      "execution_count": 13,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -337,7 +334,7 @@
         "  train_loader, test_loader = get_stl10_data_loaders(download=True)\n",
         "print(\"Dataset:\", config.dataset_name)"
       ],
-      "execution_count": 14,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -387,7 +384,7 @@
         "parameters = list(filter(lambda p: p.requires_grad, model.parameters()))\n",
         "assert len(parameters) == 2  # fc.weight, fc.bias"
       ],
-      "execution_count": 15,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -399,7 +396,7 @@
         "optimizer = torch.optim.Adam(model.parameters(), lr=3e-4, weight_decay=0.0008)\n",
         "criterion = torch.nn.CrossEntropyLoss().to(device)"
       ],
-      "execution_count": 16,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -424,7 +421,7 @@
         "            res.append(correct_k.mul_(100.0 / batch_size))\n",
         "        return res"
       ],
-      "execution_count": 17,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -470,7 +467,7 @@
         "  top5_accuracy /= (counter + 1)\n",
         "  print(f\"Epoch {epoch}\\tTop1 Train accuracy {top1_train_accuracy.item()}\\tTop1 Test accuracy: {top1_accuracy.item()}\\tTop5 test acc: {top5_accuracy.item()}\")"
       ],
-      "execution_count": 18,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -588,7 +585,7 @@
       "source": [
         ""
       ],
-      "execution_count": 18,
+      "execution_count": null,
       "outputs": []
     }
   ]