Instructions to use nhradek/cgi-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use nhradek/cgi-embedding with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://nhradek/cgi-embedding") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| Keras Embedding model that generates embeddings for CGI Classification. The model requires that the images be preprocessed by | |
| resizing to 256x256 and then converting to grayscale and then applying the Fourier transform. The process can be described as this: | |
| 1. Resize to 256x256 | |
| 2. Convert to grayscale | |
| 3. Apply the Fourier transform | |
| 4. Feed into model input | |
| The model will then transform the input transform into a 128-length embedding vector. These vectors are separable and can be used in classification models. | |
| The model doesn't enforce a specific classification model, prototypical networks, XGBoost, and other classifiers can be used to classify. For more detailed instructions refer to [Kaggle](https://www.kaggle.com/models/nhrade/cgi-embedding-detection-module). |