Instructions to use gary2002/output_dir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gary2002/output_dir with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="gary2002/output_dir") 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("gary2002/output_dir") model = AutoModelForImageClassification.from_pretrained("gary2002/output_dir", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from gary2002/output_dir: direct link, hf CLI and curl.
- Browser
- Download file 1.54 kB
-
https://huggingface.co/gary2002/output_dir/resolve/main/README.md
- Command line
-
hf download hf://gary2002/output_dir/README.md
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curl -L -o README.md https://huggingface.co/gary2002/output_dir/resolve/main/README.md
1.54 kB
metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: output_dir
results: []
output_dir
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset. It achieves the following results on the evaluation set:
- Loss: 0.8449
- Accuracy: 0.9877
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: 2e-05
- train_batch_size: 10
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 73 | 0.8449 | 0.9877 |
| No log | 2.0 | 146 | 0.5911 | 0.9877 |
| No log | 3.0 | 219 | 0.5122 | 0.9877 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2