Instructions to use Moreza009/HF_CVcourse_FoodClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Moreza009/HF_CVcourse_FoodClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Moreza009/HF_CVcourse_FoodClassifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Moreza009/HF_CVcourse_FoodClassifier") model = AutoModelForImageClassification.from_pretrained("Moreza009/HF_CVcourse_FoodClassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Moreza009/HF_CVcourse_FoodClassifier: direct link, hf CLI and curl.
- Browser
- Download file 1.71 kB
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https://huggingface.co/Moreza009/HF_CVcourse_FoodClassifier/resolve/main/README.md
- Command line
-
hf download hf://Moreza009/HF_CVcourse_FoodClassifier/README.md
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curl -L -o README.md https://huggingface.co/Moreza009/HF_CVcourse_FoodClassifier/resolve/main/README.md
1.71 kB
metadata
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results: []
language:
- en
swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on ethz/food101 dataset. It achieves the following results on the evaluation set:
- Loss: 0.9383
- Accuracy: 0.7506
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3766 | 0.9986 | 532 | 1.1204 | 0.7072 |
| 1.2542 | 1.9972 | 1064 | 0.9383 | 0.7506 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1