Created using Colab

Author: carsen-stringer

notebooks/train_Cellpose-SAM.ipynb

@@ -3,8 +3,8 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "view-in-github"
+        "id": "view-in-github",
+        "colab_type": "text"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/MouseLand/cellpose/blob/main/notebooks/train_Cellpose-SAM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -40,7 +40,9 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "5-Pn33KNqmE6"
+      },
       "source": [
         "### Mount your google drive\n",
         "\n",
@@ -69,7 +71,9 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "oK-WIju4qmE7"
+      },
       "source": [
         "\n",
         "Then click on \"Folder\" icon on the Left, press the refresh button. Your Google Drive folder should now be available here as \"gdrive\".\n",
@@ -114,7 +118,9 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "CnKdFgZTqmE9"
+      },
       "source": [
         "Check GPU and instantiate model - will download weights."
       ]
@@ -137,17 +143,19 @@
         "io.logger_setup() # run this to get printing of progress\n",
         "\n",
         "#Check if colab notebook instance has GPU access\n",
-        "if core.use_gpu()==False: \n",
+        "if core.use_gpu()==False:\n",
         "  raise ImportError(\"No GPU access, change your runtime\")\n",
-        "  \n",
+        "\n",
         "model = models.CellposeModel(gpu=True)"
       ]
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "plEha5EaqmE9"
+      },
       "source": [
-        "Input directory with your images:"
+        "Input directory with your images (if you have them, otherwise use sample images):"
       ]
     },
     {
@@ -184,17 +192,21 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "0JnV_E_OqmE9"
+      },
       "source": [
         "### Sample images (optional)\n",
         "\n",
-        "You can use our sample images instead of mounting your google drive "
+        "You can use our sample images instead of mounting your google drive"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "sG96J_V8qmE-"
+      },
       "outputs": [],
       "source": [
         "from natsort import natsorted\n",
@@ -214,18 +226,22 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "dJFJG-mkqmE-"
+      },
       "source": [
         "## Train new model"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "r0umDFliqmE-"
+      },
       "outputs": [],
       "source": [
-        "from cellpose import train \n",
+        "from cellpose import train\n",
         "\n",
         "model_name = \"new_model\"\n",
         "\n",
@@ -238,23 +254,24 @@
         "# get files\n",
         "output = io.load_train_test_data(train_dir, test_dir, mask_filter=masks_ext)\n",
         "train_data, train_labels, _, test_data, test_labels, _ = output\n",
+        "# (not passing test data into function to speed up training)\n",
         "\n",
-        "new_model_path, train_losses, test_losses = train.train_seg(model.net, \n",
-        "                                                            train_data=train_data, \n",
-        "                                                            train_labels=train_labels, \n",
-        "                                                            test_data=test_data,\n",
-        "                                                            test_labels=test_labels,\n",
+        "new_model_path, train_losses, test_losses = train.train_seg(model.net,\n",
+        "                                                            train_data=train_data,\n",
+        "                                                            train_labels=train_labels,\n",
         "                                                            batch_size=batch_size,\n",
         "                                                            n_epochs=n_epochs,\n",
-        "                                                            learning_rate=learning_rate, \n",
-        "                                                            weight_decay=weight_decay, \n",
-        "                                                            nimg_per_epoch=min(8, len(files)),\n",
+        "                                                            learning_rate=learning_rate,\n",
+        "                                                            weight_decay=weight_decay,\n",
+        "                                                            nimg_per_epoch=max(2, len(train_data)), # can change this\n",
         "                                                            model_name=model_name)\n"
       ]
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "gj0EdXtcqmE-"
+      },
       "source": [
         "## Evaluate on test data (optional)\n",
         "\n",
@@ -264,12 +281,14 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "Y2Gv4KnSqmE-"
+      },
       "outputs": [],
       "source": [
-        "from cellpose import metrics \n",
+        "from cellpose import metrics\n",
         "\n",
-        "model = models.CellposeModel(gpu=True, \n",
+        "model = models.CellposeModel(gpu=True,\n",
         "                             pretrained_model=new_model_path)\n",
         "\n",
         "# run model on test images\n",
@@ -283,15 +302,19 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "OddRFdtEqmE-"
+      },
       "source": [
         "plot masks"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "9MUrvy5JqmE-"
+      },
       "outputs": [],
       "source": [
         "plt.figure(figsize=(12,8), dpi=150)\n",
@@ -323,8 +346,8 @@
   "metadata": {
     "accelerator": "GPU",
     "colab": {
-      "include_colab_link": true,
-      "provenance": []
+      "provenance": [],
+      "include_colab_link": true
     },
     "kernelspec": {
       "display_name": "cellpose",
@@ -346,4 +369,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}