Instructions to use onnx-community/vit-beans-demo-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use onnx-community/vit-beans-demo-ONNX with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-classification', 'onnx-community/vit-beans-demo-ONNX');
vit-beans-demo (ONNX)
This is an ONNX version of lachiania/vit-beans-demo. It was automatically converted and uploaded using this Hugging Face Space.
Usage with Transformers.js
See the pipeline documentation for image-classification: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.ImageClassificationPipeline
vit-beans-demo
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: 0.1506
- Accuracy: 0.9531
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2704 | 1.0 | 65 | 0.2383 | 0.9474 |
| 0.1638 | 2.0 | 130 | 0.1234 | 0.9774 |
| 0.1116 | 3.0 | 195 | 0.1368 | 0.9624 |
| 0.1330 | 4.0 | 260 | 0.1088 | 0.9699 |
Framework versions
- Transformers 5.17.0
- Pytorch 2.11.0+cu128
- Datasets 5.0.1
- Tokenizers 0.23.1
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Model tree for onnx-community/vit-beans-demo-ONNX
Base model
google/vit-base-patch16-224-in21k Finetuned
lachiania/vit-beans-demo