Minor updates

app.ipynb

@@ -289,7 +289,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "85fde69a",
+   "id": "7c0cb370",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T04:27:09.939658Z",
@@ -622,7 +622,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ad4bede",
+   "id": "332b1429",
    "metadata": {},
    "source": [
     "# POST request to the hugging face predict api\n",
@@ -633,7 +633,7 @@
   {
    "cell_type": "code",
    "execution_count": 143,
-   "id": "ddb8aa69",
+   "id": "8869cb8a",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T05:29:27.245504Z",
@@ -648,7 +648,7 @@
   {
    "cell_type": "code",
    "execution_count": 144,
-   "id": "6066d804",
+   "id": "acaf162c",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T05:29:27.362935Z",
@@ -663,7 +663,7 @@
   {
    "cell_type": "code",
    "execution_count": 145,
-   "id": "d3d71149",
+   "id": "6f598efd",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T05:29:27.800728Z",
@@ -679,7 +679,7 @@
   {
    "cell_type": "code",
    "execution_count": 146,
-   "id": "517bf904",
+   "id": "95c63c9f",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T05:29:29.585186Z",
@@ -703,7 +703,7 @@
   {
    "cell_type": "code",
    "execution_count": 147,
-   "id": "e3b370a8",
+   "id": "2c84271c",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T05:29:29.593646Z",
@@ -737,7 +737,7 @@
   {
    "cell_type": "code",
    "execution_count": 148,
-   "id": "52f7360a",
+   "id": "fac35007",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T05:29:29.601302Z",
@@ -763,7 +763,7 @@
   {
    "cell_type": "code",
    "execution_count": 149,
-   "id": "56be3e29",
+   "id": "fd35acd7",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T05:29:29.606583Z",
@@ -789,7 +789,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8f570a34",
+   "id": "8093355c",
    "metadata": {},
    "outputs": [],
    "source": []

superheroes_classifier.ipynb

@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "e873812b",
+   "id": "e98cd38d",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:06:52.716364Z",
@@ -52,7 +52,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d155aeb1",
+   "id": "f2fe3ae3",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:06:54.702370Z",
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b6e09ee",
+   "id": "467c0889",
    "metadata": {},
    "source": [
     "# Gathering data"
@@ -94,7 +94,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "59162abd",
+   "id": "6f2d5dbd",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:06:54.774183Z",
@@ -111,7 +111,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "2f0688e0",
+   "id": "f2fedfea",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:06:56.282729Z",
@@ -165,7 +165,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "fbb9f5ea",
+   "id": "c6e4a01d",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:06:56.286927Z",
@@ -180,7 +180,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "b3a8b18d",
+   "id": "6a736161",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:07:01.747219Z",
@@ -2969,7 +2969,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "b04b3d45",
+   "id": "c19541fd",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:06:59.396377Z",
@@ -2996,7 +2996,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "22a31842",
+   "id": "fbf14a6a",
    "metadata": {},
    "source": [
     "## Image types"
@@ -3005,7 +3005,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "d4821528",
+   "id": "b68fe0bf",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:30:32.938436Z",
@@ -3021,7 +3021,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "52a74fb2",
+   "id": "9e868f5a",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:30:35.673421Z",
@@ -3042,7 +3042,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "5aab6121",
+   "id": "2778b270",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:30:36.660798Z",
@@ -3069,7 +3069,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "976c31b9",
+   "id": "d71ddf1a",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:30:38.036216Z",
@@ -3096,7 +3096,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "3fd40f34",
+   "id": "52e4d38a",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:30:38.866094Z",
@@ -3111,7 +3111,7 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "id": "c5555163",
+   "id": "9bbaccd7",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:44:43.293961Z",
@@ -3133,7 +3133,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2bf52b5c",
+   "id": "4e082969",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:02:08.375996Z",
@@ -3147,7 +3147,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "92a14936",
+   "id": "fc6e8e26",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:30:40.288477Z",
@@ -3167,7 +3167,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "9a72a63c",
+   "id": "97ef66fe",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:30:40.643649Z",
@@ -3182,7 +3182,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "5340fc94",
+   "id": "0f919a9f",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:30:43.264370Z",
@@ -3207,7 +3207,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16fc1033",
+   "id": "0ad8e6a6",
    "metadata": {},
    "source": [
     "# Training our model, and using it to clean our data"
@@ -3216,7 +3216,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "1cd25412",
+   "id": "76248673",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:30:48.769656Z",
@@ -3234,7 +3234,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "03bf1b6c",
+   "id": "57a56877",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:32:49.315953Z",
@@ -3430,7 +3430,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "ed4e8c59",
+   "id": "f4d3f720",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:33:20.181617Z",
@@ -3531,7 +3531,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "dd894684",
+   "id": "716948be",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:33:27.387435Z",
@@ -3594,7 +3594,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "1e98fea8",
+   "id": "861140f6",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:33:41.090450Z",
@@ -3700,7 +3700,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "2fedaeee",
+   "id": "861ddf7e",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:30:19.391467Z",
@@ -3715,7 +3715,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e8e56c8c",
+   "id": "84c80eb8",
    "metadata": {},
    "source": [
     "We can see that amongst our \"black bears\" is an image that contains two bears: one grizzly, one black. So, we should choose `<Delete>` in the menu under this image. `ImageClassifierCleaner` doesn't actually do the deleting or changing of labels for you; it just returns the indices of items to change. So, for instance, to delete (`unlink`) all images selected for deletion, we would run:\n",
@@ -3737,7 +3737,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a5ca6f1",
+   "id": "bd736ab0",
    "metadata": {},
    "source": [
     "> note: No Need for Big Data: After cleaning the dataset using these steps, we generally are seeing 100% accuracy on this task. We even see that result when we download a lot fewer images than the 150 per class we're using here. As you can see, the common complaint that _you need massive amounts of data to do deep learning_ can be a very long way from the truth!"
@@ -3745,7 +3745,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be93c87b",
+   "id": "8b174e6a",
    "metadata": {},
    "source": [
     "# Turning Your Model into an Online Application"
@@ -3754,7 +3754,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "98295f29",
+   "id": "a2d75bf4",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:33:44.896505Z",
@@ -3919,7 +3919,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "id": "1fc798b1",
+   "id": "00db3bdf",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-06-04T03:35:21.029154Z",