Image Classification
Transformers
TensorBoard
Safetensors
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use ricardoSLabs/fer_plus_V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ricardoSLabs/fer_plus_V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ricardoSLabs/fer_plus_V2") 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("ricardoSLabs/fer_plus_V2") model = AutoModelForImageClassification.from_pretrained("ricardoSLabs/fer_plus_V2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ricardoSLabs/fer_plus_V2: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/ricardoSLabs/fer_plus_V2/resolve/main/training_args.bin
- Command line
-
hf download hf://ricardoSLabs/fer_plus_V2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ricardoSLabs/fer_plus_V2/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 94f639eab7c97e1cdaaf1a6b24eee597d897b2e51d839c5eb47cdfffee920f91
- Size of remote file:
- 5.3 kB
- SHA256:
- b5afa78a367805e005d5d21f946aca9dce440e2edd8a59e29d7b124518ceed4f
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