Image Classification
Transformers
Safetensors
beit
knowledge_distillation
vision
Generated from Trainer
Instructions to use c14kevincardenas/beit-base-patch16-224_alpha0.5_temp3.0_t3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use c14kevincardenas/beit-base-patch16-224_alpha0.5_temp3.0_t3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="c14kevincardenas/beit-base-patch16-224_alpha0.5_temp3.0_t3") 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("c14kevincardenas/beit-base-patch16-224_alpha0.5_temp3.0_t3") model = AutoModelForImageClassification.from_pretrained("c14kevincardenas/beit-base-patch16-224_alpha0.5_temp3.0_t3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b51a8091cfa891f8b5689d025592712725d6fd59fbf4288ee5c38d4f31ffcaa
- Size of remote file:
- 5.18 kB
- SHA256:
- 415c0c40600e90862179f97c83bde13d0dada020afa2cdb9339e0457d5669777
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.