Instructions to use Jingni/my_first_food_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jingni/my_first_food_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Jingni/my_first_food_model") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Jingni/my_first_food_model") model = AutoModelForImageClassification.from_pretrained("Jingni/my_first_food_model") - Notebooks
- Google Colab
- Kaggle
my_first_food_model
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: 1.9013
- Accuracy: 0.965
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.584 | 1.0 | 25 | 2.8238 | 0.9475 |
| 2.2086 | 2.0 | 50 | 2.0773 | 0.95 |
| 1.941 | 3.0 | 75 | 1.9013 | 0.965 |
Framework versions
- Transformers 4.38.0
- Pytorch 2.1.2
- Datasets 2.17.1
- Tokenizers 0.15.2
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Model tree for Jingni/my_first_food_model
Base model
google/vit-base-patch16-224-in21k