
<!-- 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. -->

# clip-vit-large-patch14-finetuned-clip-vit-large-patch14-mnist_linear_probe

This model is a fine-tuned version of [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9687
- Accuracy: 0.9137

## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 2.4414        | 0.9953  | 105  | 2.3838          | 0.102    |
| 2.2431        | 2.0     | 211  | 2.2100          | 0.2698   |
| 2.1251        | 2.9953  | 316  | 2.0662          | 0.4987   |
| 2.0119        | 4.0     | 422  | 1.9232          | 0.6905   |
| 1.9134        | 4.9953  | 527  | 1.8080          | 0.7645   |
| 1.8314        | 6.0     | 633  | 1.7059          | 0.8053   |
| 1.7816        | 6.9953  | 738  | 1.6175          | 0.8215   |
| 1.7076        | 8.0     | 844  | 1.5373          | 0.845    |
| 1.6632        | 8.9953  | 949  | 1.4675          | 0.8592   |
| 1.6188        | 10.0    | 1055 | 1.4062          | 0.863    |
| 1.5606        | 10.9953 | 1160 | 1.3510          | 0.8718   |
| 1.5185        | 12.0    | 1266 | 1.3031          | 0.8718   |
| 1.5007        | 12.9953 | 1371 | 1.2591          | 0.881    |
| 1.4573        | 14.0    | 1477 | 1.2201          | 0.8833   |
| 1.4474        | 14.9953 | 1582 | 1.1841          | 0.8875   |
| 1.4308        | 16.0    | 1688 | 1.1524          | 0.8925   |
| 1.4091        | 16.9953 | 1793 | 1.1246          | 0.8943   |
| 1.3683        | 18.0    | 1899 | 1.0986          | 0.8985   |
| 1.365         | 18.9953 | 2004 | 1.0752          | 0.9042   |
| 1.3635        | 20.0    | 2110 | 1.0563          | 0.9033   |
| 1.3422        | 20.9953 | 2215 | 1.0389          | 0.9043   |
| 1.3248        | 22.0    | 2321 | 1.0231          | 0.9083   |
| 1.2961        | 22.9953 | 2426 | 1.0100          | 0.9093   |
| 1.3136        | 24.0    | 2532 | 0.9986          | 0.9107   |
| 1.3067        | 24.9953 | 2637 | 0.9897          | 0.911    |
| 1.2984        | 26.0    | 2743 | 0.9818          | 0.9115   |
| 1.3045        | 26.9953 | 2848 | 0.9759          | 0.9132   |
| 1.291         | 28.0    | 2954 | 0.9717          | 0.9132   |
| 1.2731        | 28.9953 | 3059 | 0.9692          | 0.9142   |
| 1.3034        | 29.8578 | 3150 | 0.9687          | 0.9137   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1

0 1 2 3 4 5 6 7 8 9