Instructions to use mlahr/font-identifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlahr/font-identifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mlahr/font-identifier") 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("mlahr/font-identifier") model = AutoModelForImageClassification.from_pretrained("mlahr/font-identifier") - Notebooks
- Google Colab
- Kaggle
File size: 1,224 Bytes
59284c7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | {
"best_global_step": 14,
"best_metric": 0.06818181818181818,
"best_model_checkpoint": "font-identifier/checkpoint-14",
"epoch": 1.0,
"eval_steps": 500,
"global_step": 14,
"is_hyper_param_search": false,
"is_local_process_zero": true,
"is_world_process_zero": true,
"log_history": [
{
"epoch": 0.7272727272727273,
"grad_norm": 9.430075645446777,
"learning_rate": 6.428571428571429e-06,
"loss": 3.3213,
"step": 10
},
{
"epoch": 1.0,
"eval_accuracy": 0.06818181818181818,
"eval_loss": 3.2453081607818604,
"eval_runtime": 2.4333,
"eval_samples_per_second": 90.411,
"eval_steps_per_second": 5.753,
"step": 14
}
],
"logging_steps": 10,
"max_steps": 700,
"num_input_tokens_seen": 0,
"num_train_epochs": 50,
"save_steps": 500,
"stateful_callbacks": {
"TrainerControl": {
"args": {
"should_epoch_stop": false,
"should_evaluate": false,
"should_log": false,
"should_save": true,
"should_training_stop": false
},
"attributes": {}
}
},
"total_flos": 8891925806776320.0,
"train_batch_size": 16,
"trial_name": null,
"trial_params": null
}
|