transformers pytorch swin image-classification generated_from_trainer dataset:nctcrche100_k base_model:microsoft/swin-base-patch4-window7-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- nctcrche100_k
metrics:
- accuracy
model-index:
- name: histoSwin-base
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: nctcrche100_k
      type: nctcrche100_k
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9708913649025069
---

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# histoSwin-base

This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the nctcrche100_k dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1260
- Accuracy: 0.9709

## Model description

More information needed

## Intended uses & limitations

For TMA classification.

## 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: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0386        | 1.0   | 1562 | 0.1740          | 0.9592   |
| 0.05          | 2.0   | 3125 | 0.2313          | 0.9524   |
| 0.0324        | 3.0   | 4687 | 0.2606          | 0.9604   |
| 0.005         | 4.0   | 6250 | 0.1724          | 0.9660   |
| 0.0037        | 5.0   | 7810 | 0.1260          | 0.9709   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
