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:
- e2e01e1992a026d0ea958f800fb20e00c052c427f22dbc0021d61634fbd8189c
- Size of remote file:
- 343 MB
- SHA256:
- 838443d535bac6b48933e688d4c2f859d225933827d74ccfd34c9d8ed9dc0658
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.