metadata
library_name: transformers
base_model: openai/clip-vit-large-patch14-336
tags:
- generated_from_trainer
model-index:
- name: clip-finetuned-csu-p14-336-e3l57-l
results: []
clip-finetuned-csu-p14-336-e3l57-l
This model is a fine-tuned version of openai/clip-vit-large-patch14-336 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5312
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-07
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
0.2708 | 0.0921 | 500 | 1.0631 |
0.251 | 0.1842 | 1000 | 0.9428 |
0.235 | 0.2763 | 1500 | 0.8717 |
0.1529 | 0.3685 | 2000 | 0.8318 |
0.1781 | 0.4606 | 2500 | 0.7549 |
0.1681 | 0.5527 | 3000 | 0.7218 |
0.1064 | 0.6448 | 3500 | 0.7048 |
0.1357 | 0.7369 | 4000 | 0.6962 |
0.1098 | 0.8290 | 4500 | 0.6778 |
0.1142 | 0.9211 | 5000 | 0.6657 |
0.1113 | 1.0133 | 5500 | 0.6431 |
0.0572 | 1.1054 | 6000 | 0.6367 |
0.0746 | 1.1975 | 6500 | 0.6261 |
0.0494 | 1.2896 | 7000 | 0.6245 |
0.0788 | 1.3817 | 7500 | 0.6120 |
0.0808 | 1.4738 | 8000 | 0.6011 |
0.0536 | 1.5660 | 8500 | 0.5893 |
0.0869 | 1.6581 | 9000 | 0.5916 |
0.0752 | 1.7502 | 9500 | 0.5707 |
0.0577 | 1.8423 | 10000 | 0.5678 |
0.0891 | 1.9344 | 10500 | 0.5631 |
0.0559 | 2.0265 | 11000 | 0.5548 |
0.0385 | 2.1186 | 11500 | 0.5536 |
0.0185 | 2.2108 | 12000 | 0.5519 |
0.0642 | 2.3029 | 12500 | 0.5505 |
0.0456 | 2.3950 | 13000 | 0.5444 |
0.0476 | 2.4871 | 13500 | 0.5395 |
0.027 | 2.5792 | 14000 | 0.5361 |
0.042 | 2.6713 | 14500 | 0.5356 |
0.0469 | 2.7634 | 15000 | 0.5343 |
0.0438 | 2.8556 | 15500 | 0.5332 |
0.0481 | 2.9477 | 16000 | 0.5312 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 1.12.1
- Datasets 2.21.0
- Tokenizers 0.19.1