Image Classification
Transformers
PyTorch
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use kensvin/emotion_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kensvin/emotion_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kensvin/emotion_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("kensvin/emotion_classification") model = AutoModelForImageClassification.from_pretrained("kensvin/emotion_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aecbf06e30510693fadea93c7e7d18638f17db454fdaf94445ac7f06e5907e25
- Size of remote file:
- 343 MB
- SHA256:
- a4d36d492ad0052b3154f78e6d60698a983aecbc5227e161eaf0e09d536611c9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.