Instructions to use chandra1976/vit-finetuned-chessman2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use chandra1976/vit-finetuned-chessman2 with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("google/vit-base-patch16-224-in21k") model = PeftModel.from_pretrained(base_model, "chandra1976/vit-finetuned-chessman2") - Transformers
How to use chandra1976/vit-finetuned-chessman2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="chandra1976/vit-finetuned-chessman2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("chandra1976/vit-finetuned-chessman2") model = AutoModelForImageClassification.from_pretrained("chandra1976/vit-finetuned-chessman2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
vit-finetuned-chessman2
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1620
- Accuracy: 0.9636
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
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.5954 | 1.0 | 28 | 1.3698 | 0.6545 |
| 0.9802 | 2.0 | 56 | 0.7982 | 0.8 |
| 0.4706 | 3.0 | 84 | 0.4171 | 0.9455 |
| 0.2342 | 4.0 | 112 | 0.2657 | 0.9455 |
| 0.1416 | 5.0 | 140 | 0.2017 | 0.9636 |
| 0.1059 | 6.0 | 168 | 0.1813 | 0.9636 |
| 0.0872 | 7.0 | 196 | 0.1655 | 0.9636 |
| 0.0749 | 8.0 | 224 | 0.1620 | 0.9636 |
Framework versions
- PEFT 0.19.1
- Transformers 4.57.6
- Pytorch 2.10.0
- Datasets 5.0.0
- Tokenizers 0.22.2
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Model tree for chandra1976/vit-finetuned-chessman2
Base model
google/vit-base-patch16-224-in21kEvaluation results
- Accuracy on imagefolderself-reported0.964