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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: persian-clip |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# persian-clip |
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7629 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.4072 | 0.12 | 100 | 2.1627 | |
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| 1.7146 | 0.25 | 200 | 1.6432 | |
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| 1.5058 | 0.37 | 300 | 1.4523 | |
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| 1.3836 | 0.49 | 400 | 1.4799 | |
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| 1.4946 | 0.62 | 500 | 1.3101 | |
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| 1.2544 | 0.74 | 600 | 1.2073 | |
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| 1.1984 | 0.86 | 700 | 1.1801 | |
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| 1.3243 | 0.99 | 800 | 1.1652 | |
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| 0.8373 | 1.11 | 900 | 1.0860 | |
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| 0.8625 | 1.23 | 1000 | 1.0731 | |
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| 0.791 | 1.36 | 1100 | 1.0427 | |
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| 0.8975 | 1.48 | 1200 | 1.0786 | |
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| 0.7767 | 1.6 | 1300 | 1.0248 | |
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| 0.9041 | 1.73 | 1400 | 1.0311 | |
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| 0.8474 | 1.85 | 1500 | 0.9649 | |
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| 0.7435 | 1.98 | 1600 | 0.9552 | |
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| 0.5126 | 2.1 | 1700 | 0.9909 | |
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| 0.4871 | 2.22 | 1800 | 0.9188 | |
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| 0.48 | 2.35 | 1900 | 0.9151 | |
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| 0.4715 | 2.47 | 2000 | 0.9056 | |
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| 0.408 | 2.59 | 2100 | 0.8885 | |
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| 0.4999 | 2.72 | 2200 | 0.8911 | |
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| 0.5169 | 2.84 | 2300 | 0.8727 | |
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| 0.3574 | 2.96 | 2400 | 0.8477 | |
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| 0.2749 | 3.09 | 2500 | 0.8666 | |
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| 0.2719 | 3.21 | 2600 | 0.8520 | |
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| 0.2779 | 3.33 | 2700 | 0.8379 | |
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| 0.3407 | 3.46 | 2800 | 0.8386 | |
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| 0.223 | 3.58 | 2900 | 0.8245 | |
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| 0.2649 | 3.7 | 3000 | 0.8149 | |
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| 0.2698 | 3.83 | 3100 | 0.7983 | |
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| 0.1863 | 3.95 | 3200 | 0.7959 | |
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| 0.1831 | 4.07 | 3300 | 0.7957 | |
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| 0.172 | 4.2 | 3400 | 0.7963 | |
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| 0.1457 | 4.32 | 3500 | 0.7879 | |
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| 0.1503 | 4.44 | 3600 | 0.7794 | |
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| 0.1783 | 4.57 | 3700 | 0.7788 | |
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| 0.166 | 4.69 | 3800 | 0.7753 | |
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| 0.1598 | 4.81 | 3900 | 0.7673 | |
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| 0.1618 | 4.94 | 4000 | 0.7629 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.10.1 |
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- Tokenizers 0.15.0 |
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### How to use? |
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```python |
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# Both models generate vectors with 768 dimensions. |
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from transformers import CLIPVisionModel, RobertaModel, AutoTokenizer, CLIPFeatureExtractor |
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# download pre-trained models |
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vision_encoder = CLIPVisionModel.from_pretrained('SeyedAli/Persian-CLIP') |
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preprocessor = CLIPFeatureExtractor.from_pretrained('SeyedAli/Persian-CLIP') |
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text_encoder = RobertaModel.from_pretrained('SeyedAli/Persian-CLIP') |
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tokenizer = AutoTokenizer.from_pretrained('SeyedAli/Persian-CLIP') |
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# define input image and input text |
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text = 'something' |
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image = PIL.Image.open('my_favorite_image.jpg') |
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# compute embeddings |
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text_embedding = text_encoder(**tokenizer(text, |
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return_tensors='pt')).pooler_output |
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image_embedding = vision_encoder(**preprocessor(image, |
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return_tensors='pt')).pooler_output |
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``` |
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### zero-shot-Image-Classification: |
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The followings are just some use cases of Persian-CLIP on 25K Unsplash images |
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* use pip install -q git+https://github.com/sajjjadayobi/clipfa.git |
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```python |
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from clipfa import CLIPDemo |
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import torch |
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# Both models generate vectors with 768 dimensions. |
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from transformers import CLIPVisionModel, RobertaModel, AutoTokenizer, CLIPFeatureExtractor |
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# download pre-trained models |
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vision_encoder = CLIPVisionModel.from_pretrained('SeyedAli/Persian-CLIP') |
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preprocessor = CLIPFeatureExtractor.from_pretrained('SeyedAli/Persian-CLIP') |
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text_encoder = RobertaModel.from_pretrained('SeyedAli/Persian-CLIP') |
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tokenizer = AutoTokenizer.from_pretrained('SeyedAli/Persian-CLIP') |
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demo = CLIPDemo(vision_encoder, text_encoder, tokenizer) |
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demo.compute_text_embeddings(['ู
ุชู 3' ,'ู
ุชู 2' ,'ู
ุชู 1']) |
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demo.compute_image_embeddings(['my_favorite_image.jpg']) |
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demo.zero_shot(image_path='my_favorite_image.jpg') |
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``` |
