Instructions to use bswift/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bswift/test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bswift/test") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, VisionTransformerForImageClassification processor = AutoImageProcessor.from_pretrained("bswift/test") model = VisionTransformerForImageClassification.from_pretrained("bswift/test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"batch_size": 10, "epochs": 1, "model": "vit_large_patch16", "base_learning_rate": 0.005, "layer_decay": 0.65, "weight_decay": 0.05, "drop_path_rate": 0.2, "input_size": 256, "num_classes": 9, "task": "./models/ch-observations-12-12-2023-200530/", "output_dir": "X:\\code\\UIaEYE\\data\\Cherry Health\\images\\segmented\\dataset-clean and trimmed-tags-12-02-2023-022946\\observations\\_datasets\\_dataset_12-10-2023-161414\\training_data\\outputs\\_artifacts_12-12-2023-200530", "world_size": 1, "finetune": "D:\\\\data\\\\RetFound\\\\weights\\\\RETFound_cfp_weights.pth", "rmbg": true} |