diff --git "a/images.ipynb" "b/images.ipynb"
--- "a/images.ipynb"
+++ "b/images.ipynb"
@@ -29,7 +29,7 @@
},
"outputs": [],
"source": [
- "!pip install jupyter sentence_transformers pandas qdrant_client pyarrow datasets"
+ "!pip install jupyter sentence_transformers pandas qdrant_client"
]
},
{
@@ -48,17 +48,17 @@
"cell_type": "code",
"source": [
"import pandas as pd\n",
- "data = pd.read_csv('open-images-dataset-validation.tsv', sep='\\t', header=None).reset_index()\n",
+ "data = pd.read_csv('images.tsv', sep='\\t', header=None).reset_index()\n",
"print(data.shape, data.head(), sep=\"\\n\")"
],
"metadata": {
"id": "j97T0MIBeEDe",
- "outputId": "37b2e3bb-e4fa-48c5-d54b-901e6fb552a6",
+ "outputId": "edeb755a-1880-42a5-c7d8-570ff185c6b8",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
- "execution_count": 2,
+ "execution_count": 12,
"outputs": [
{
"output_type": "stream",
@@ -112,7 +112,7 @@
"metadata": {
"id": "cK_63ubnieI6"
},
- "execution_count": 8,
+ "execution_count": 13,
"outputs": []
},
{
@@ -136,10 +136,305 @@
"model = SentenceTransformer(\"clip-ViT-B-32\")"
],
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- "id": "pHYk-KdmlJxz"
+ "id": "pHYk-KdmlJxz",
+ "outputId": "26d28cf6-4878-4b34-b8e4-a88f68195514",
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},
- "execution_count": 30,
- "outputs": []
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:72: UserWarning: \n",
+ "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+ "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+ "You will be able to reuse this secret in all of your notebooks.\n",
+ "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ".gitattributes: 0%| | 0.00/690 [00:00\n",
+ "  |  |
\n",
+ "  |  |
\n",
+ "'''))"
+ ],
+ "metadata": {
+ "id": "s5VFEKoMjDm7",
+ "outputId": "a2bb2f95-865f-4e03-cd93-5acc0a7b452b",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 891
+ }
+ },
+ "execution_count": 22,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "text/html": [
+ "\n",
+ "  |  |
\n",
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\n",
+ "
"
+ ]
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "We have successfully tested out our image search engine over a set of 500 images."
+ ],
+ "metadata": {
+ "id": "gOu1S1J8ql2g"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Visualizing the Vector Space\n",
+ "The vector embeddings of each image are stored in a vector space each seperated by some distance and angle, in our case we use the cosine similarity metric which measures based on the similarity of the angles between two vectors.\n",
+ "\n",
+ "Here's a visualization of the vector space of our 500 images.\n",
+ "\n",
+ "![](\"https://raw.githubusercontent.com/Snehil-Shah/MultiModal-Semantic-Search-Engine/b144c8b037067a8a5bf5ee5cdf3f89198fb488f4/assets/Visualization.png\")
"
+ ],
+ "metadata": {
+ "id": "UF7jNZxUq1YA"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Encoding the Rest\n",
+ "Now we will upsert the embeddings of the entire remaining dataset."
+ ],
+ "metadata": {
+ "id": "3LuQMbMps8j6"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "points = []\n",
+ "\n",
+ "for i in range(500, 15000):\n",
+ " img = download_file(data.iloc[i,1])\n",
+ " if (img):\n",
+ " embedding = model.encode(Image.open(str(img)))\n",
+ " points.append({\n",
+ " \"id\":i,\n",
+ " \"vector\":embedding,\n",
+ " \"payload\":{\"url\":data.iloc[i,1]}\n",
+ " })\n",
+ " if((i+1)%1000 == 0):\n",
+ " print(f\"{i+1} images encoded & upserted..\")\n",
+ " upsert_to_db(points)\n",
+ " points = []\n",
+ "\n",
+ "print(\"\\nEmbeddings upserted to vector database.\")"
+ ],
+ "metadata": {
+ "id": "UWTzzgW0s5w-",
+ "outputId": "b4768f82-7ef2-463b-93fe-53ca2179de94",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": 23,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1000 images encoded & upserted..\n",
+ "2000 images encoded & upserted..\n",
+ "3000 images encoded & upserted..\n",
+ "4000 images encoded & upserted..\n",
+ "5000 images encoded & upserted..\n",
+ "6000 images encoded & upserted..\n",
+ "7000 images encoded & upserted..\n",
+ "8000 images encoded & upserted..\n",
+ "9000 images encoded & upserted..\n",
+ "10000 images encoded & upserted..\n",
+ "11000 images encoded & upserted..\n",
+ "12000 images encoded & upserted..\n",
+ "13000 images encoded & upserted..\n",
+ "14000 images encoded & upserted..\n",
+ "15000 images encoded & upserted..\n",
+ "\n",
+ "Embeddings upserted to vector database.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Done! We have successfully encoded a whopping 15k images onto a semantic vector space!"
+ ],
+ "metadata": {
+ "id": "WuVJaSJnV-Ht"
+ }
}
],
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