# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("ArrayDice/car_orientation_classification")
model = AutoModelForImageClassification.from_pretrained("ArrayDice/car_orientation_classification")Quick Links
car_orientation_classification2
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6800
- Accuracy: 0.6926
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.9933 | 1.0 | 68 | 1.9084 | 0.4099 |
| 1.4721 | 2.0 | 136 | 1.2870 | 0.5124 |
| 1.1677 | 3.0 | 204 | 1.0780 | 0.5265 |
| 0.9919 | 4.0 | 272 | 0.9454 | 0.5760 |
| 0.8392 | 5.0 | 340 | 0.8184 | 0.6926 |
| 0.7778 | 6.0 | 408 | 0.8311 | 0.6431 |
| 0.7341 | 7.0 | 476 | 0.7425 | 0.6572 |
| 0.6695 | 8.0 | 544 | 0.6800 | 0.6926 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for ArrayDice/car_orientation_classification
Base model
google/vit-base-patch16-224-in21k
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ArrayDice/car_orientation_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")