Instructions to use sirdug/Hand-Writing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sirdug/Hand-Writing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sirdug/Hand-Writing") 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("sirdug/Hand-Writing") model = AutoModelForImageClassification.from_pretrained("sirdug/Hand-Writing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_params.json from sirdug/Hand-Writing: direct link, hf CLI and curl.
- Browser
- Download file 698 Bytes
-
https://huggingface.co/sirdug/Hand-Writing/resolve/main/training_params.json
- Command line
-
hf download hf://sirdug/Hand-Writing/training_params.json
-
curl -L -o training_params.json https://huggingface.co/sirdug/Hand-Writing/resolve/main/training_params.json
698 Bytes
| { | |
| "data_path": "mnist", | |
| "model": "google/vit-base-patch16-224", | |
| "username": "sirdug", | |
| "lr": 5e-05, | |
| "epochs": 3, | |
| "batch_size": 8, | |
| "warmup_ratio": 0.1, | |
| "gradient_accumulation": 1, | |
| "optimizer": "adamw_torch", | |
| "scheduler": "linear", | |
| "weight_decay": 0.0, | |
| "max_grad_norm": 1.0, | |
| "seed": 42, | |
| "train_split": "train", | |
| "valid_split": null, | |
| "logging_steps": -1, | |
| "project_name": "autotrain-trpk5-x0yvm", | |
| "auto_find_batch_size": false, | |
| "mixed_precision": "fp16", | |
| "save_total_limit": 1, | |
| "push_to_hub": true, | |
| "evaluation_strategy": "epoch", | |
| "image_column": "image", | |
| "target_column": "label", | |
| "log": "tensorboard" | |
| } |