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---
base_model: openai/clip-vit-base-patch32
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: ktp-not-ktp-clip
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.989010989010989
---
<!-- 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. -->
# ktp-not-ktp-clip
This model is a fine-tuned version of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1267
- Accuracy: 0.9890
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 7 | 0.5809 | 0.6374 |
| No log | 2.0 | 14 | 1.3401 | 0.6703 |
| 0.5558 | 3.0 | 21 | 0.6458 | 0.7692 |
| 0.5558 | 4.0 | 28 | 0.3785 | 0.8681 |
| 0.1701 | 5.0 | 35 | 0.3004 | 0.9451 |
| 0.1701 | 6.0 | 42 | 0.2204 | 0.9560 |
| 0.142 | 7.0 | 49 | 0.1483 | 0.9341 |
| 0.142 | 8.0 | 56 | 0.1386 | 0.9670 |
| 0.1002 | 9.0 | 63 | 0.7714 | 0.8681 |
| 0.1002 | 10.0 | 70 | 0.2285 | 0.9341 |
| 0.0956 | 11.0 | 77 | 0.1162 | 0.9780 |
| 0.0956 | 12.0 | 84 | 0.1104 | 0.9780 |
| 0.0004 | 13.0 | 91 | 0.1722 | 0.9780 |
| 0.0004 | 14.0 | 98 | 0.2109 | 0.9780 |
| 0.0209 | 15.0 | 105 | 0.3321 | 0.9560 |
| 0.0209 | 16.0 | 112 | 0.0785 | 0.9780 |
| 0.0209 | 17.0 | 119 | 0.1525 | 0.9670 |
| 0.0014 | 18.0 | 126 | 0.1436 | 0.9670 |
| 0.0014 | 19.0 | 133 | 0.2278 | 0.9670 |
| 0.0002 | 20.0 | 140 | 0.3035 | 0.9560 |
| 0.0002 | 21.0 | 147 | 0.1239 | 0.9780 |
| 0.001 | 22.0 | 154 | 0.1211 | 0.9890 |
| 0.001 | 23.0 | 161 | 0.1253 | 0.9890 |
| 0.0 | 24.0 | 168 | 0.1265 | 0.9890 |
| 0.0 | 25.0 | 175 | 0.1267 | 0.9890 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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