# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Dimasnoufal/image_strawbery-peach_classifier")
model = AutoModelForImageClassification.from_pretrained("Dimasnoufal/image_strawbery-peach_classifier")Quick Links
image_strawbery-peach_classifier
This model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0386
- Accuracy: 0.9939
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: 6e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 48 | 0.1272 | 0.9635 |
| No log | 2.0 | 96 | 0.0626 | 0.9878 |
| No log | 3.0 | 144 | 0.0865 | 0.9757 |
| No log | 4.0 | 192 | 0.0386 | 0.9939 |
| No log | 5.0 | 240 | 0.0354 | 0.9939 |
| No log | 6.0 | 288 | 0.0519 | 0.9848 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for Dimasnoufal/image_strawbery-peach_classifier
Base model
google/vit-base-patch16-224
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dimasnoufal/image_strawbery-peach_classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")