Instructions to use arpanl/Fine-Tuned_Model3_Transfer_learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arpanl/Fine-Tuned_Model3_Transfer_learning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="arpanl/Fine-Tuned_Model3_Transfer_learning") 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("arpanl/Fine-Tuned_Model3_Transfer_learning") model = AutoModelForImageClassification.from_pretrained("arpanl/Fine-Tuned_Model3_Transfer_learning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from arpanl/Fine-Tuned_Model3_Transfer_learning: direct link, hf CLI and curl.
- Browser
- Download file 1.16 kB
-
https://huggingface.co/arpanl/Fine-Tuned_Model3_Transfer_learning/resolve/main/README.md
- Command line
-
hf download hf://arpanl/Fine-Tuned_Model3_Transfer_learning/README.md
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curl -L -o README.md https://huggingface.co/arpanl/Fine-Tuned_Model3_Transfer_learning/resolve/main/README.md
1.16 kB
metadata
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: Fine-Tuned_Model3_Transfer_learning
results: []
Fine-Tuned_Model3_Transfer_learning
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset.
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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2