# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Valencio/LLM_course_ViT_model_image_classification")
model = AutoModelForImageClassification.from_pretrained("Valencio/LLM_course_ViT_model_image_classification", device_map="auto")Quick Links
LLM_course_ViT_model_image_classification
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.3072
- Accuracy: 0.459
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: 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
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.0023 | 1.0 | 63 | 2.8601 | 0.389 |
| 2.5716 | 2.0 | 126 | 2.4576 | 0.42 |
| 2.3300 | 3.0 | 189 | 2.3072 | 0.459 |
Framework versions
- Transformers 5.10.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for Valencio/LLM_course_ViT_model_image_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="Valencio/LLM_course_ViT_model_image_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")