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hybrid-cnn-vit

Image Classification
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hybrid-cnn-vit
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---
license: apache-2.0
base_model: google/vit-hybrid-base-bit-384
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hybrid-cnn-vit
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8707767328456983
---

<!-- 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. -->

# hybrid-cnn-vit

This model is a fine-tuned version of [google/vit-hybrid-base-bit-384](https://huggingface.co/google/vit-hybrid-base-bit-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3384
- Accuracy: 0.8708

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5277        | 1.0   | 202  | 0.3903          | 0.8210   |
| 0.4623        | 2.0   | 404  | 0.3478          | 0.8415   |
| 0.4497        | 3.0   | 606  | 0.3334          | 0.8520   |
| 0.4074        | 4.0   | 808  | 0.3397          | 0.8460   |
| 0.3552        | 5.0   | 1010 | 0.3227          | 0.8624   |
| 0.3637        | 6.0   | 1212 | 0.3230          | 0.8617   |
| 0.3316        | 7.0   | 1414 | 0.3189          | 0.8673   |
| 0.31          | 8.0   | 1616 | 0.3804          | 0.8492   |
| 0.2324        | 9.0   | 1818 | 0.3382          | 0.8662   |
| 0.234         | 10.0  | 2020 | 0.3384          | 0.8708   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2