Instructions to use alexvmt/CBNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use alexvmt/CBNet with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://alexvmt/CBNet") - Notebooks
- Google Colab
- Kaggle
File size: 466 Bytes
c822b20 a5a71b0 b2f4af0 a5a71b0 | 1 2 3 4 5 6 7 | ---
license: mit
---
This model intends to support conservation in the Congo Basin (specifically Cameroon). CBNet is short for CongoBasinNet. It currently covers 12 species. The model was trained on about 15,000 images (source: WWF Cameroon). Accuracy is 0.902 and macro accuracy is 0.817 on a held-out test set of about 2,200 images, though performance varies by class. This model is a fine-tuned version of SpeciesNet.
The model can be used freely in AddaxAI. |