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---
base_model: openai/clip-vit-base-patch32
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: ktp-crop-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: 0.9864864864864865
---
<!-- 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-crop-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: 0.1223
- Accuracy: 0.9865
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.96 | 6 | 0.8954 | 0.5270 |
| 0.7112 | 1.92 | 12 | 0.6729 | 0.5405 |
| 0.7112 | 2.88 | 18 | 0.6407 | 0.7297 |
| 0.4413 | 4.0 | 25 | 0.1279 | 0.9459 |
| 0.0935 | 4.96 | 31 | 0.1436 | 0.9730 |
| 0.0935 | 5.92 | 37 | 0.0021 | 1.0 |
| 0.0697 | 6.88 | 43 | 0.2862 | 0.9459 |
| 0.161 | 8.0 | 50 | 0.0843 | 0.9595 |
| 0.161 | 8.96 | 56 | 0.2255 | 0.9459 |
| 0.0061 | 9.92 | 62 | 0.4678 | 0.9054 |
| 0.0061 | 10.88 | 68 | 0.3299 | 0.9189 |
| 0.0309 | 12.0 | 75 | 0.5189 | 0.9189 |
| 0.0025 | 12.96 | 81 | 0.0850 | 0.9865 |
| 0.0025 | 13.92 | 87 | 0.0720 | 0.9865 |
| 0.0042 | 14.88 | 93 | 0.0745 | 0.9865 |
| 0.0002 | 16.0 | 100 | 0.0869 | 0.9865 |
| 0.0002 | 16.96 | 106 | 0.0895 | 0.9865 |
| 0.0001 | 17.92 | 112 | 0.1127 | 0.9865 |
| 0.0001 | 18.88 | 118 | 0.1219 | 0.9865 |
| 0.0 | 19.2 | 120 | 0.1223 | 0.9865 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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