Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use rendy-k/face_emotion_recognizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rendy-k/face_emotion_recognizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="rendy-k/face_emotion_recognizer") 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("rendy-k/face_emotion_recognizer") model = AutoModelForImageClassification.from_pretrained("rendy-k/face_emotion_recognizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rendy-k/face_emotion_recognizer: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/rendy-k/face_emotion_recognizer/resolve/main/training_args.bin
- Command line
-
hf download hf://rendy-k/face_emotion_recognizer/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rendy-k/face_emotion_recognizer/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- fa2179b68f83e921af20aa8f15b1254be3927f7c17d07260c58f78f121e770e6
- Size of remote file:
- 4.73 kB
- SHA256:
- 12f067f9a57abaa09b480c31b9361b95414120f057af66a40cf72e277d76a5a9
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