Instructions to use Anthony-Pulla/study with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Anthony-Pulla/study with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Anthony-Pulla/study") 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("Anthony-Pulla/study") model = AutoModelForImageClassification.from_pretrained("Anthony-Pulla/study", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Anthony-Pulla/study: direct link, hf CLI and curl.
- Browser
- Download file 1.38 kB
-
https://huggingface.co/Anthony-Pulla/study/resolve/main/README.md
- Command line
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hf download hf://Anthony-Pulla/study/README.md
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curl -L -o README.md https://huggingface.co/Anthony-Pulla/study/resolve/main/README.md
1.38 kB
metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: study
results: []
study
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0349
- Accuracy: 0.9850
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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1312 | 3.85 | 500 | 0.0349 | 0.9850 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2