metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-large-patch32-384-Hyper_Kvasir_Labeled_Images
results: []
vit-large-patch32-384-Hyper_Kvasir_Labeled_Images
This model is a fine-tuned version of google/vit-large-patch32-384 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3954
- Accuracy: 0.8202
- Weighted f1: 0.8151
- Micro f1: 0.8202
- Macro f1: 0.7674
- Weighted recall: 0.8202
- Micro recall: 0.8202
- Macro recall: 0.7549
- Weighted precision: 0.8141
- Micro precision: 0.8202
- Macro precision: 0.7860
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: 0.005
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.3536 | 1.0 | 649 | 0.3568 | 0.8455 | 0.8411 | 0.8455 | 0.8003 | 0.8455 | 0.8455 | 0.7863 | 0.8411 | 0.8455 | 0.8205 |
| 0.4417 | 2.0 | 1298 | 0.3954 | 0.8202 | 0.8151 | 0.8202 | 0.7674 | 0.8202 | 0.8202 | 0.7549 | 0.8141 | 0.8202 | 0.7860 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3