Instructions to use rendy-k/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rendy-k/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="rendy-k/image_classification") 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("rendy-k/image_classification") model = AutoModelForImageClassification.from_pretrained("rendy-k/image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from rendy-k/image_classification: direct link, hf CLI and curl.
- Browser
- Download file 1.54 kB
-
https://huggingface.co/rendy-k/image_classification/resolve/main/README.md
- Command line
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hf download hf://rendy-k/image_classification/README.md
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curl -L -o README.md https://huggingface.co/rendy-k/image_classification/resolve/main/README.md
1.54 kB
metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: image_classification
results: []
image_classification
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: 0.1874
- Accuracy: 0.9517
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
- 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 | 52 | 0.2941 | 0.9227 |
| No log | 2.0 | 104 | 0.2064 | 0.9517 |
| No log | 3.0 | 156 | 0.2221 | 0.9372 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1