LiteRT
Keras
English
tensorflow
emotion-recognition
resnet50
ckplus
rafdb
fine-tuning
computer-vision
deep-learning
facial-expression
affective-computing
Eval Results (legacy)
Instructions to use PSewmuthu/resnet50-emotion-recognition-ckplus-rafdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use PSewmuthu/resnet50-emotion-recognition-ckplus-rafdb with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://PSewmuthu/resnet50-emotion-recognition-ckplus-rafdb") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 882aca4e629ec7928b464d2351480d5e2550ab3bb69d5c49afecb8cccbe7834a
- Size of remote file:
- 29.9 kB
- SHA256:
- fb51c2dd2a20c4033454995c178d301968888bc49eb02f4034aa714bbae7152c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.