<p align="center">
<img src="https://vector-forge.s3.eu-central-1.amazonaws.com/assets/vector-forge-logo.png" alt="Vector Forge Logo" width="110">
</p>
<p align="center">
<a href="https://www.python.org/downloads/release/python-3100/" target="_blank">
<img src="https://img.shields.io/badge/Python->=3.10-blue?logo=python" alt="Python >= 3.10">
</a>
</p>
<p align="center">
<i>🐍 Vector Forge is a Python package designed for easy transformation of various data types into feature vectors.</i>
</p>
---
<p align="center">
<a href="https://www.python.org/">
<img src="https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54"
alt="Python">
</a>
<a href="https://pytorch.org/">
<img src="https://img.shields.io/badge/PyTorch-orange.svg?&style=for-the-badge&logo=pytorch&logoColor=white" alt="PyTorch"/>
</a>
<a href="https://keras.io/">
<img src="https://img.shields.io/badge/Keras-005571?style=for-the-badge&logo=keras" alt="Keras">
</a>
</p>
---
## 💡Core ideas
🌄 For image embeddings, Vector Forge uses pre-trained networks, which means the models have already learned features
from
a large set of images called [ImageNet](https://www.image-net.org/). When we use these models in Vector Forge, we skip
the part that
identifies objects, and instead, we use
the part that understands the image features. This way, we get a bunch of numbers (a vector) representing the image,
which can be used
in many different tasks like finding similar images, clustering, classification and many more.
📄 Text embeddings are a way to convert words or sentences into numbers, making it possible for computers to understand
and
process them. In Vector Forge, the [CLIP ViT-B/32](https://huggingface.co/openai/clip-vit-base-patch32) model is
utilized to generate these embeddings. When you provide any text, be it
a single word or a sentence, to CLIP, it transforms this text into a fixed-size vector. Each vector has a consistent
length, no matter how long or short the original text is. This consistency in size is valuable, especially when
comparing different pieces of text or measuring how similar a piece of text is to an image.
## 🧩 Features
- **Image to Vector conversion**: Easily convert individual images into feature vectors by specifying your desired model
to extract meaningful representations.
- **Batch processing**: Provide a folder path to process multiple images in bulk. Select your preferred model and let
Vector Forge swiftly handle all the images in the specified directory.
- **Text to Vector transformation**: Effortlessly convert textual data into vectors. Choose your model, and Vector Forge
will transform your text input into a high-dimensional vector representation.
- **Support for multiple models**: Vector Forge supports various models for vectorization, including CLIP ViT-B/32, CLIP
ViT-L/14, Xception,
VGG16 and VGG19, to provide flexibility in handling different data types.
## ⚙️ Requirements
- [Python >= 3.10](https://www.python.org/downloads/release/python-3100/)
## 📦 Supported models
| Model Name | Implementation | Parameter Value | Supports Image | Supports Text | Embedding Size |
|:-------------------------------------------------------------------------------------------:|:------------------------------------------------------:|:----------------------:|:--------------:|:-------------:|:--------------:|
| [CLIP ViT-B/32](https://huggingface.co/openai/clip-vit-base-patch32) | [PyTorch](https://pytorch.org/) | `Models.CLIP_B_P32` | ✅ | ✅ | (512,) |
| [CLIP ViT-L/14](https://huggingface.co/openai/clip-vit-large-patch14) | [PyTorch](https://pytorch.org/) | `Models.CLIP_L_P14` | ✅ | ✅ | (768,) |
| [CLIP ViT-B/32 OpenVino™](https://huggingface.co/scaleflex/clip-vit-base-patch32-openvino) | [OpenVino™](https://docs.openvino.ai/2023.1/home.html) | `Models.CLIP_B_P32_OV` | ✅ | ✅ | (512,) |
| [CLIP ViT-L/14 OpenVino™](https://huggingface.co/scaleflex/clip-vit-large-patch14-openvino) | [OpenVino™](https://docs.openvino.ai/2023.1/home.html) | `Models.CLIP_L_P14_OV` | ✅ | ✅ | (768,) |
| [VGG16](https://arxiv.org/abs/1409.1556) | [Keras](https://keras.io/) | `Models.VGG16` | ✅ | ❌ | (512,) |
| [VGG19](https://arxiv.org/abs/1409.1556) | [Keras](https://keras.io/) | `Models.VGG19` | ✅ | ❌ | (512,) |
| [Xception](https://keras.io/api/applications/xception/) | [Keras](https://keras.io/) | `Models.Xception` | ✅ | ❌ | (2048,) |
## 🎛️ Usage
You can work on many pictures at once or just one piece of text with simple commands, making it a breeze to get your
data ready for further use or analysis
### 🔧 Installation
```shell
pip install vector_forge
```
### 🔌 Create a vectorizer
#### Import the necessary classes or functions
```python
from vector_forge import Vectorizer
```
#### Default vectorizer
By default, the vectorizer is [CLIP ViT-B/32](https://huggingface.co/openai/clip-vit-base-patch32), as it works for text
and images.
```python
vectorizer = Vectorizer()
```
#### Text to Vector
Example how to convert a text prompt to a vector.
```python
text_embedding = vectorizer.text_to_vector("Nice text!")
```
#### Image to Vector
Example how to convert to convert image from path to vector.
```python
image_embedding = vectorizer.image_to_vector("/path/to/image.jpg")
```
#### Change the vectorizer to use a different model
Example how to change the vectorizer model, in this example
to [Xception](https://keras.io/api/applications/xception/).
Keep in mind, that not all models work for for text prompts. If you want to compare image and texts, I recommend
using [CLIP ViT-B/32](https://huggingface.co/openai/clip-vit-base-patch32).
```python
from vector_forge import Vectorizer, Models
vectorizer = Vectorizer(model=Models.Xception)
```
#### Return types
In Vector Forge, you have the flexibility to choose the format in which the vectors are returned. This is controlled by
the `return_type` parameter available in the `image_to_vector` and `text_to_vector` methods of the Vectorizer
class. Here are
the available return types along with examples:
a) **return_type="numpy"**
This is the default return type. Vectors are returned as [NumPy](https://numpy.org/doc/stable/index.html) arrays.
```python
image_embedding = vectorizer.image_to_vector("/path/to/image.jpg", return_type="numpy")
# Output: array([0.0234, 0.0345, ..., 0.0456])
# Shape: (2048,) for Xception, (512,) for VGG16, VGG19 and CLIP ViT-B/32, (768, ) for CLIP ViT-L/14
```
b) **return_type="str"**
Vectors are returned as a string representation of the NumPy array.
```python
image_embedding = vectorizer.image_to_vector("/path/to/image.jpg", return_type="str")
# Output: "[0.0234, 0.0345, ..., 0.0456]"
```
c) **return_type="list"**
Vectors are returned as a list of values.
```python
image_embedding = vectorizer.image_to_vector("/path/to/image.jpg", return_type="list")
# Output: [0.0234, 0.0345, ..., 0.0456]
```
d) **return_type="2darray"**
Vectors are returned as a 2-dimensional NumPy array, where each vector is a row in the array. This format is especially
useful when you want to compute similarities or perform other vectorized operations.
```python
image_embedding = vectorizer.image_to_vector("/path/to/image.jpg", return_type="2darray")
# Output: array([[0.0234, 0.0345, ..., 0.0456]])
# Shape: (1, 2048) # for Xception, (1, 512) for VGG16, VGG19 and CLIP ViT-B/32, (1, 768) for CLIP ViT-L/14
```
#### Batch Processing for images
Vector Forge can process multiple images in a folder in one go. Just provide the folder path, and the `load_from_folder`
method will handle the rest.
```python
# Convert all valid images in a folder to vectors
for vector in vectorizer.load_from_folder("/path/to/folder"):
print(vector.shape)
```
You can specify the `return_type`, `save_to_index`, and `file_info_extractor` parameters to control the output format,
to save the file paths of processed images to an index file, and to execute a custom function on each file for
additional information extraction, respectively.
```python
# Example with return_type and save_to_index
for vector in vectorizer.load_from_folder("/path/to/folder", return_type="2darray", save_to_index="paths.txt"):
print(vector.shape)
```
```python
from vector_forge.info_extractors import get_file_info
# Example with additional information to each file
for vector, dimension in vectorizer.load_from_folder("/path/to/folder", file_info_extractor=get_file_info):
print(vector.shape)
```
#### Image preprocessing
Vector Forge provides a collection of image preprocessing functions to help prepare images for vectorization. These
functions can be found in the `image_preprocessors`.
You can also specify your own custom image preprocessing function.
```python
from vector_forge.image_preprocessors import resize_image
# Create a Vectorizer instance with the resize_image function as the image preprocessor
resize_fn = lambda img: resize_image(img, width=600)
vectorizer = Vectorizer(image_preprocessor=resize_fn)
vector = vectorizer.image_to_vector(input_image='/path/to/image.jpg')
```
```python
from vector_forge.image_preprocessors import convert_to_grayscale
# Create a Vectorizer instance with the convert_to_grayscale function as the image preprocessor
vectorizer = Vectorizer(image_preprocessor=convert_to_grayscale)
vector = vectorizer.image_to_vector(input_image='/path/to/image.jpg')
```
### 🧪 A complete example
```python
from vector_forge import Vectorizer
from vector_forge.image_preprocessors import resize_image
from sklearn.metrics.pairwise import cosine_similarity
def compute_similarity(vectorizer, text, image_path):
# Convert text and image to vectors with return type '2darray'
text_embedding = vectorizer.text_to_vector(text, return_type="2darray")
image_embedding = vectorizer.image_to_vector(image_path, return_type="2darray")
# Compute cosine similarity
similarity = cosine_similarity(text_embedding, image_embedding)[0][0]
return similarity
# Create a vectorizer with the default CLIP ViT-B/32 model and a custom image preprocessor
resize_fn = lambda img: resize_image(img, width=600)
vectorizer = Vectorizer(image_preprocessor=resize_fn)
# Define text and image paths
text = "A couple of birds"
image_path_1 = "vector_forge/test_data/birds.jpg" # adapt paths accordingly
image_path_2 = "vector_forge/test_data/sample.jpg" # adapt paths accordingly
# Compute and print similarity scores
similarity_1 = compute_similarity(vectorizer, text, image_path_1)
similarity_2 = compute_similarity(vectorizer, text, image_path_2)
print(f"Similarity between text and first image: {similarity_1}")
print(f"Similarity between text and second image: {similarity_2}")
```
Complete example how to use `file_info_extractor`, which can extract some valuable information from files.
```python
from vector_forge import Vectorizer, Models
from vector_forge.info_extractors import get_colors
# Create a Vectorizer instance
vectorizer = Vectorizer(model=Models.Xception)
# Define the path to your folder containing images
folder_path = '/path/to/images'
# Process all images in the specified folder
for vector, colors in vectorizer.load_from_folder(folder_path, file_info_extractor=get_colors):
# Print the vector shape and image dimensions
print(f'Vector shape: {vector.shape}')
print(f'Image colors: {colors}')
```
## ⚠️ Disclaimer
Vector Forge is provided as-is, without warranty of any kind. Users should employ the library at their own risk. It's
important to test and validate the library's results in your specific context to ensure it meets your needs. Performance
and accuracy can vary based on data and use cases. We encourage all users to thoroughly verify the library's outputs and
consider them as one of many tools in their toolkit.
## 🔮 Future features
- [ ] Make inference APIs which hold the models in memory
### Images
- [x] Add support for VGG19
- [x] Add possibility for index creation when using `load_from_folder`
- [x] Add support for [larger CLIP model](https://huggingface.co/openai/clip-vit-large-patch14)
- [x] Optimize CLIP generation
with [OpenVino IR](https://docs.openvino.ai/2022.3/notebooks/228-clip-zero-shot-image-classification-with-output.html)
- [x] Batch support for `load_from_folder` operations
- [ ] Add support for custom type of Keras models
### Texts
- [ ] Add support for GloVe
- [ ] Add text preprocessors
Raw data
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"description": "<p align=\"center\">\n <img src=\"https://vector-forge.s3.eu-central-1.amazonaws.com/assets/vector-forge-logo.png\" alt=\"Vector Forge Logo\" width=\"110\">\n</p>\n<p align=\"center\">\n <a href=\"https://www.python.org/downloads/release/python-3100/\" target=\"_blank\">\n <img src=\"https://img.shields.io/badge/Python->=3.10-blue?logo=python\" alt=\"Python >= 3.10\">\n </a>\n</p>\n<p align=\"center\">\n <i>\ud83d\udc0d Vector Forge is a Python package designed for easy transformation of various data types into feature vectors.</i>\n</p>\n\n---\n\n<p align=\"center\">\n <a href=\"https://www.python.org/\">\n <img src=\"https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54\"\n alt=\"Python\">\n </a>\n <a href=\"https://pytorch.org/\">\n <img src=\"https://img.shields.io/badge/PyTorch-orange.svg?&style=for-the-badge&logo=pytorch&logoColor=white\" alt=\"PyTorch\"/>\n </a>\n <a href=\"https://keras.io/\">\n <img src=\"https://img.shields.io/badge/Keras-005571?style=for-the-badge&logo=keras\" alt=\"Keras\">\n </a>\n</p>\n\n---\n\n## \ud83d\udca1Core ideas\n\n\ud83c\udf04 For image embeddings, Vector Forge uses pre-trained networks, which means the models have already learned features\nfrom\na large set of images called [ImageNet](https://www.image-net.org/). When we use these models in Vector Forge, we skip\nthe part that\nidentifies objects, and instead, we use\nthe part that understands the image features. This way, we get a bunch of numbers (a vector) representing the image,\nwhich can be used\nin many different tasks like finding similar images, clustering, classification and many more.\n\n\ud83d\udcc4 Text embeddings are a way to convert words or sentences into numbers, making it possible for computers to understand\nand\nprocess them. In Vector Forge, the [CLIP ViT-B/32](https://huggingface.co/openai/clip-vit-base-patch32) model is\nutilized to generate these embeddings. When you provide any text, be it\na single word or a sentence, to CLIP, it transforms this text into a fixed-size vector. Each vector has a consistent\nlength, no matter how long or short the original text is. This consistency in size is valuable, especially when\ncomparing different pieces of text or measuring how similar a piece of text is to an image.\n\n## \ud83e\udde9 Features\n\n- **Image to Vector conversion**: Easily convert individual images into feature vectors by specifying your desired model\n to extract meaningful representations.\n- **Batch processing**: Provide a folder path to process multiple images in bulk. Select your preferred model and let\n Vector Forge swiftly handle all the images in the specified directory.\n- **Text to Vector transformation**: Effortlessly convert textual data into vectors. Choose your model, and Vector Forge\n will transform your text input into a high-dimensional vector representation.\n- **Support for multiple models**: Vector Forge supports various models for vectorization, including CLIP ViT-B/32, CLIP\n ViT-L/14, Xception,\n VGG16 and VGG19, to provide flexibility in handling different data types.\n\n## \u2699\ufe0f Requirements\n\n- [Python >= 3.10](https://www.python.org/downloads/release/python-3100/)\n\n## \ud83d\udce6 Supported models\n\n| Model Name | Implementation | Parameter Value | Supports Image | Supports Text | Embedding Size |\n|:-------------------------------------------------------------------------------------------:|:------------------------------------------------------:|:----------------------:|:--------------:|:-------------:|:--------------:|\n| [CLIP ViT-B/32](https://huggingface.co/openai/clip-vit-base-patch32) | [PyTorch](https://pytorch.org/) | `Models.CLIP_B_P32` | \u2705 | \u2705 | (512,) |\n| [CLIP ViT-L/14](https://huggingface.co/openai/clip-vit-large-patch14) | [PyTorch](https://pytorch.org/) | `Models.CLIP_L_P14` | \u2705 | \u2705 | (768,) |\n| [CLIP ViT-B/32 OpenVino\u2122](https://huggingface.co/scaleflex/clip-vit-base-patch32-openvino) | [OpenVino\u2122](https://docs.openvino.ai/2023.1/home.html) | `Models.CLIP_B_P32_OV` | \u2705 | \u2705 | (512,) |\n| [CLIP ViT-L/14 OpenVino\u2122](https://huggingface.co/scaleflex/clip-vit-large-patch14-openvino) | [OpenVino\u2122](https://docs.openvino.ai/2023.1/home.html) | `Models.CLIP_L_P14_OV` | \u2705 | \u2705 | (768,) |\n| [VGG16](https://arxiv.org/abs/1409.1556) | [Keras](https://keras.io/) | `Models.VGG16` | \u2705 | \u274c | (512,) |\n| [VGG19](https://arxiv.org/abs/1409.1556) | [Keras](https://keras.io/) | `Models.VGG19` | \u2705 | \u274c | (512,) |\n| [Xception](https://keras.io/api/applications/xception/) | [Keras](https://keras.io/) | `Models.Xception` | \u2705 | \u274c | (2048,) |\n\n## \ud83c\udf9b\ufe0f Usage\n\nYou can work on many pictures at once or just one piece of text with simple commands, making it a breeze to get your\ndata ready for further use or analysis\n\n### \ud83d\udd27 Installation\n\n```shell\npip install vector_forge\n```\n\n### \ud83d\udd0c Create a vectorizer\n\n#### Import the necessary classes or functions\n\n```python\nfrom vector_forge import Vectorizer\n```\n\n#### Default vectorizer\n\nBy default, the vectorizer is [CLIP ViT-B/32](https://huggingface.co/openai/clip-vit-base-patch32), as it works for text\nand images.\n\n```python\nvectorizer = Vectorizer() \n```\n\n#### Text to Vector\n\nExample how to convert a text prompt to a vector.\n\n```python\ntext_embedding = vectorizer.text_to_vector(\"Nice text!\")\n```\n\n#### Image to Vector\n\nExample how to convert to convert image from path to vector.\n\n```python\nimage_embedding = vectorizer.image_to_vector(\"/path/to/image.jpg\")\n```\n\n#### Change the vectorizer to use a different model\n\nExample how to change the vectorizer model, in this example\nto [Xception](https://keras.io/api/applications/xception/). \nKeep in mind, that not all models work for for text prompts. If you want to compare image and texts, I recommend\nusing [CLIP ViT-B/32](https://huggingface.co/openai/clip-vit-base-patch32).\n\n```python\nfrom vector_forge import Vectorizer, Models\n\nvectorizer = Vectorizer(model=Models.Xception)\n```\n\n#### Return types\n\nIn Vector Forge, you have the flexibility to choose the format in which the vectors are returned. This is controlled by\nthe `return_type` parameter available in the `image_to_vector` and `text_to_vector` methods of the Vectorizer\nclass. Here are\nthe available return types along with examples:\n\na) **return_type=\"numpy\"**\n\nThis is the default return type. Vectors are returned as [NumPy](https://numpy.org/doc/stable/index.html) arrays.\n\n```python\nimage_embedding = vectorizer.image_to_vector(\"/path/to/image.jpg\", return_type=\"numpy\")\n# Output: array([0.0234, 0.0345, ..., 0.0456])\n# Shape: (2048,) for Xception, (512,) for VGG16, VGG19 and CLIP ViT-B/32, (768, ) for CLIP ViT-L/14\n```\n\nb) **return_type=\"str\"**\n\nVectors are returned as a string representation of the NumPy array.\n\n```python\nimage_embedding = vectorizer.image_to_vector(\"/path/to/image.jpg\", return_type=\"str\")\n# Output: \"[0.0234, 0.0345, ..., 0.0456]\"\n```\n\nc) **return_type=\"list\"**\n\nVectors are returned as a list of values.\n\n```python\nimage_embedding = vectorizer.image_to_vector(\"/path/to/image.jpg\", return_type=\"list\")\n# Output: [0.0234, 0.0345, ..., 0.0456]\n```\n\nd) **return_type=\"2darray\"**\n\nVectors are returned as a 2-dimensional NumPy array, where each vector is a row in the array. This format is especially\nuseful when you want to compute similarities or perform other vectorized operations.\n\n```python\nimage_embedding = vectorizer.image_to_vector(\"/path/to/image.jpg\", return_type=\"2darray\")\n# Output: array([[0.0234, 0.0345, ..., 0.0456]])\n# Shape: (1, 2048) # for Xception, (1, 512) for VGG16, VGG19 and CLIP ViT-B/32, (1, 768) for CLIP ViT-L/14\n```\n\n#### Batch Processing for images\n\nVector Forge can process multiple images in a folder in one go. Just provide the folder path, and the `load_from_folder`\nmethod will handle the rest.\n\n```python\n# Convert all valid images in a folder to vectors\nfor vector in vectorizer.load_from_folder(\"/path/to/folder\"):\n print(vector.shape)\n```\n\nYou can specify the `return_type`, `save_to_index`, and `file_info_extractor` parameters to control the output format,\nto save the file paths of processed images to an index file, and to execute a custom function on each file for\nadditional information extraction, respectively.\n\n```python\n# Example with return_type and save_to_index\nfor vector in vectorizer.load_from_folder(\"/path/to/folder\", return_type=\"2darray\", save_to_index=\"paths.txt\"):\n print(vector.shape)\n```\n\n```python\nfrom vector_forge.info_extractors import get_file_info\n\n# Example with additional information to each file\nfor vector, dimension in vectorizer.load_from_folder(\"/path/to/folder\", file_info_extractor=get_file_info):\n print(vector.shape)\n```\n\n#### Image preprocessing\n\nVector Forge provides a collection of image preprocessing functions to help prepare images for vectorization. These\nfunctions can be found in the `image_preprocessors`.\nYou can also specify your own custom image preprocessing function.\n\n```python\nfrom vector_forge.image_preprocessors import resize_image\n\n# Create a Vectorizer instance with the resize_image function as the image preprocessor\nresize_fn = lambda img: resize_image(img, width=600)\nvectorizer = Vectorizer(image_preprocessor=resize_fn)\nvector = vectorizer.image_to_vector(input_image='/path/to/image.jpg')\n```\n\n```python\nfrom vector_forge.image_preprocessors import convert_to_grayscale\n\n# Create a Vectorizer instance with the convert_to_grayscale function as the image preprocessor\nvectorizer = Vectorizer(image_preprocessor=convert_to_grayscale)\nvector = vectorizer.image_to_vector(input_image='/path/to/image.jpg')\n```\n\n### \ud83e\uddea A complete example\n\n```python\nfrom vector_forge import Vectorizer\nfrom vector_forge.image_preprocessors import resize_image\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n\ndef compute_similarity(vectorizer, text, image_path):\n # Convert text and image to vectors with return type '2darray'\n text_embedding = vectorizer.text_to_vector(text, return_type=\"2darray\")\n image_embedding = vectorizer.image_to_vector(image_path, return_type=\"2darray\")\n\n # Compute cosine similarity\n similarity = cosine_similarity(text_embedding, image_embedding)[0][0]\n return similarity\n\n\n# Create a vectorizer with the default CLIP ViT-B/32 model and a custom image preprocessor\nresize_fn = lambda img: resize_image(img, width=600)\nvectorizer = Vectorizer(image_preprocessor=resize_fn)\n\n# Define text and image paths\ntext = \"A couple of birds\"\nimage_path_1 = \"vector_forge/test_data/birds.jpg\" # adapt paths accordingly\nimage_path_2 = \"vector_forge/test_data/sample.jpg\" # adapt paths accordingly\n\n# Compute and print similarity scores\nsimilarity_1 = compute_similarity(vectorizer, text, image_path_1)\nsimilarity_2 = compute_similarity(vectorizer, text, image_path_2)\n\nprint(f\"Similarity between text and first image: {similarity_1}\")\nprint(f\"Similarity between text and second image: {similarity_2}\")\n```\n\nComplete example how to use `file_info_extractor`, which can extract some valuable information from files.\n\n```python\nfrom vector_forge import Vectorizer, Models\nfrom vector_forge.info_extractors import get_colors\n\n# Create a Vectorizer instance\nvectorizer = Vectorizer(model=Models.Xception)\n\n# Define the path to your folder containing images\nfolder_path = '/path/to/images'\n\n# Process all images in the specified folder\nfor vector, colors in vectorizer.load_from_folder(folder_path, file_info_extractor=get_colors):\n # Print the vector shape and image dimensions\n print(f'Vector shape: {vector.shape}')\n print(f'Image colors: {colors}')\n```\n\n## \u26a0\ufe0f Disclaimer\nVector Forge is provided as-is, without warranty of any kind. Users should employ the library at their own risk. It's\nimportant to test and validate the library's results in your specific context to ensure it meets your needs. Performance\nand accuracy can vary based on data and use cases. We encourage all users to thoroughly verify the library's outputs and\nconsider them as one of many tools in their toolkit.\n\n## \ud83d\udd2e Future features\n\n- [ ] Make inference APIs which hold the models in memory\n\n### Images\n\n- [x] Add support for VGG19\n- [x] Add possibility for index creation when using `load_from_folder`\n- [x] Add support for [larger CLIP model](https://huggingface.co/openai/clip-vit-large-patch14)\n- [x] Optimize CLIP generation\n with [OpenVino IR](https://docs.openvino.ai/2022.3/notebooks/228-clip-zero-shot-image-classification-with-output.html)\n- [x] Batch support for `load_from_folder` operations\n- [ ] Add support for custom type of Keras models\n\n### Texts\n\n- [ ] Add support for GloVe\n- [ ] Add text preprocessors\n",
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