Instructions to use alirzb/S2_M1_R2_ViT_42618530 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alirzb/S2_M1_R2_ViT_42618530 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="alirzb/S2_M1_R2_ViT_42618530") 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("alirzb/S2_M1_R2_ViT_42618530") model = AutoModelForImageClassification.from_pretrained("alirzb/S2_M1_R2_ViT_42618530", device_map="auto") - Notebooks
- Google Colab
- Kaggle
S2_M1_R2_ViT_42618530
This model is a fine-tuned version of google/vit-base-patch16-224 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0018
- Accuracy: 0.9987
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0088 | 1.0 | 237 | 0.0385 | 0.9887 |
| 0.0067 | 2.0 | 474 | 0.0155 | 0.9962 |
| 0.0015 | 3.0 | 711 | 0.0038 | 0.9987 |
| 0.0001 | 4.0 | 948 | 0.0011 | 0.9987 |
| 0.0001 | 5.0 | 1185 | 0.0018 | 0.9987 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.13.3
- Downloads last month
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Model tree for alirzb/S2_M1_R2_ViT_42618530
Base model
google/vit-base-patch16-224