Image Classification
Transformers
PyTorch
TensorBoard
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use ALM-AHME/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-60-20-20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ALM-AHME/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-60-20-20 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ALM-AHME/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-60-20-20") 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("ALM-AHME/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-60-20-20") model = AutoModelForImageClassification.from_pretrained("ALM-AHME/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-60-20-20", device_map="auto") - Notebooks
- Google Colab
- Kaggle
beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-60-20-20
This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0001
- Accuracy: 1.0
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: 0.5
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1071 | 0.99 | 140 | 0.0181 | 0.993 |
| 0.2317 | 2.0 | 281 | 0.0474 | 0.983 |
| 0.0454 | 3.0 | 422 | 0.0173 | 0.9927 |
| 0.0299 | 4.0 | 563 | 0.0153 | 0.994 |
| 0.0055 | 4.97 | 700 | 0.0001 | 1.0 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Downloads last month
- 14
Evaluation results
- Accuracy on imagefolderself-reported1.000