Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use mrisdi/emotion_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrisdi/emotion_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mrisdi/emotion_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("mrisdi/emotion_classification") model = AutoModelForImageClassification.from_pretrained("mrisdi/emotion_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mrisdi/emotion_classification: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/mrisdi/emotion_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://mrisdi/emotion_classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mrisdi/emotion_classification/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 32397f185daffe9393b22fe1930c584c74e8f05b1b36ed22b78d0227e1375459
- Size of remote file:
- 5.24 kB
- SHA256:
- e139753ee2424758e0bb4abf9e05b94d31f69024ff7763eff690b24fda043e77
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.