# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("gitfreder/image_classification")
model = AutoModelForImageClassification.from_pretrained("gitfreder/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: 1.2566
- Accuracy: 0.5188
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-07
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 20 | 1.4431 | 0.3875 |
| No log | 2.0 | 40 | 1.4023 | 0.425 |
| No log | 3.0 | 60 | 1.3637 | 0.4437 |
| No log | 4.0 | 80 | 1.3424 | 0.4938 |
| No log | 5.0 | 100 | 1.3437 | 0.45 |
| No log | 6.0 | 120 | 1.3696 | 0.4938 |
| No log | 7.0 | 140 | 1.3172 | 0.4688 |
| No log | 8.0 | 160 | 1.2781 | 0.5125 |
| No log | 9.0 | 180 | 1.2599 | 0.5312 |
| No log | 10.0 | 200 | 1.3174 | 0.4813 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
- Downloads last month
- 2
Model tree for gitfreder/image_classification
Base model
google/vit-base-patch16-224-in21kEvaluation results
- Accuracy on imagefolderself-reported0.519
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="gitfreder/image_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")