|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- imagefolder |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: vit-large-patch32-384-Hyper_Kvasir_Labeled_Images |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# vit-large-patch32-384-Hyper_Kvasir_Labeled_Images |
|
|
|
This model is a fine-tuned version of [google/vit-large-patch32-384](https://huggingface.co/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 |
|
|
