Instructions to use darklorddad/Model-Focalnet-Base-82 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use darklorddad/Model-Focalnet-Base-82 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="darklorddad/Model-Focalnet-Base-82") 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("darklorddad/Model-Focalnet-Base-82") model = AutoModelForImageClassification.from_pretrained("darklorddad/Model-Focalnet-Base-82", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 870 Bytes
e7ed158 | 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 | {
"data_path": "Model-Focalnet-Base-/autotrain-data",
"model": "microsoft/focalnet-base",
"username": "local",
"lr": 0.00005,
"epochs": 100,
"batch_size": 32,
"warmup_ratio": 0.1,
"gradient_accumulation": 3,
"optimizer": "adamw_torch",
"scheduler": "linear",
"weight_decay": 0.01,
"max_grad_norm": 1.0,
"seed": 42,
"train_split": "train",
"valid_split": "validation",
"logging_steps": -1,
"project_name": "Model-Focalnet-Base-",
"auto_find_batch_size": false,
"mixed_precision": "bf16",
"save_total_limit": 1,
"token": null,
"push_to_hub": true,
"eval_strategy": "epoch",
"image_column": "autotrain_image",
"target_column": "autotrain_label",
"log": "tensorboard",
"early_stopping_patience": 5,
"early_stopping_threshold": 0.01
} |