# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("hrtnisri2016/image_classification")
model = AutoModelForImageClassification.from_pretrained("hrtnisri2016/image_classification")Quick Links
image_classification
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: 1.5771
- Accuracy: 0.4688
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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 20 | 1.9643 | 0.3438 |
| No log | 2.0 | 40 | 1.7819 | 0.4125 |
| No log | 3.0 | 60 | 1.6521 | 0.4562 |
| No log | 4.0 | 80 | 1.6034 | 0.4938 |
| No log | 5.0 | 100 | 1.5769 | 0.5062 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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Model tree for hrtnisri2016/image_classification
Base model
google/vit-base-patch16-224-in21kEvaluation results
- Accuracy on imagefolderself-reported0.469
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hrtnisri2016/image_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")