Instructions to use Farideh/dataset_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Farideh/dataset_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Farideh/dataset_model") 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("Farideh/dataset_model") model = AutoModelForImageClassification.from_pretrained("Farideh/dataset_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Farideh/dataset_model: direct link, hf CLI and curl.
- Browser
- Download file 1.38 kB
-
https://huggingface.co/Farideh/dataset_model/resolve/main/README.md
- Command line
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hf download hf://Farideh/dataset_model/README.md
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curl -L -o README.md https://huggingface.co/Farideh/dataset_model/resolve/main/README.md
1.38 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: dataset_model
results: []
dataset_model
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.4921
- eval_accuracy: 0.8647
- eval_runtime: 12.5977
- eval_samples_per_second: 79.221
- eval_steps_per_second: 5.001
- epoch: 21.99
- step: 1364
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2