You can deploy this model and start earning money today!

SeyedAli/
Persian-CLIP

Name: Persian-CLIP
Availability: InStock

generated_from_trainer

endpoints_compatible

Views

No views yet

persian-clip

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.7629

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: 3e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss
1.4072	0.12	100	2.1627
1.7146	0.25	200	1.6432
1.5058	0.37	300	1.4523
1.3836	0.49	400	1.4799
1.4946	0.62	500	1.3101
1.2544	0.74	600	1.2073
1.1984	0.86	700	1.1801
1.3243	0.99	800	1.1652
0.8373	1.11	900	1.0860
0.8625	1.23	1000	1.0731
0.791	1.36	1100	1.0427
0.8975	1.48	1200	1.0786
0.7767	1.6	1300	1.0248
0.9041	1.73	1400	1.0311
0.8474	1.85	1500	0.9649
0.7435	1.98	1600	0.9552
0.5126	2.1	1700	0.9909
0.4871	2.22	1800	0.9188
0.48	2.35	1900	0.9151
0.4715	2.47	2000	0.9056
0.408	2.59	2100	0.8885
0.4999	2.72	2200	0.8911
0.5169	2.84	2300	0.8727
0.3574	2.96	2400	0.8477
0.2749	3.09	2500	0.8666
0.2719	3.21	2600	0.8520
0.2779	3.33	2700	0.8379
0.3407	3.46	2800	0.8386
0.223	3.58	2900	0.8245
0.2649	3.7	3000	0.8149
0.2698	3.83	3100	0.7983
0.1863	3.95	3200	0.7959
0.1831	4.07	3300	0.7957
0.172	4.2	3400	0.7963
0.1457	4.32	3500	0.7879
0.1503	4.44	3600	0.7794
0.1783	4.57	3700	0.7788
0.166	4.69	3800	0.7753
0.1598	4.81	3900	0.7673
0.1618	4.94	4000	0.7629

Framework versions

Transformers 4.38.2
Pytorch 2.1.2+cu121
Datasets 2.10.1
Tokenizers 0.15.0

How to use?

python

# Both models generate vectors with 768 dimensions.
from transformers import CLIPVisionModel, RobertaModel, AutoTokenizer, CLIPFeatureExtractor
# download pre-trained models
vision_encoder = CLIPVisionModel.from_pretrained('SeyedAli/Persian-CLIP')
preprocessor = CLIPFeatureExtractor.from_pretrained('SeyedAli/Persian-CLIP')
text_encoder = RobertaModel.from_pretrained('SeyedAli/Persian-CLIP')
tokenizer = AutoTokenizer.from_pretrained('SeyedAli/Persian-CLIP')
# define input image and input text
text = 'something'
image = PIL.Image.open('my_favorite_image.jpg')
# compute embeddings
text_embedding = text_encoder(**tokenizer(text,
                                          return_tensors='pt')).pooler_output
image_embedding = vision_encoder(**preprocessor(image, 
                                                return_tensors='pt')).pooler_output

zero-shot-Image-Classification:

The followings are just some use cases of Persian-CLIP on 25K Unsplash images

use pip install -q git+https://github.com/sajjjadayobi/clipfa.git

python

from clipfa import CLIPDemo
import torch
# Both models generate vectors with 768 dimensions.
from transformers import CLIPVisionModel, RobertaModel, AutoTokenizer, CLIPFeatureExtractor
# download pre-trained models
vision_encoder = CLIPVisionModel.from_pretrained('SeyedAli/Persian-CLIP')
preprocessor = CLIPFeatureExtractor.from_pretrained('SeyedAli/Persian-CLIP')
text_encoder = RobertaModel.from_pretrained('SeyedAli/Persian-CLIP')
tokenizer = AutoTokenizer.from_pretrained('SeyedAli/Persian-CLIP')

demo = CLIPDemo(vision_encoder, text_encoder, tokenizer)
demo.compute_text_embeddings(['متن 3' ,'متن 2' ,'متن 1'])
demo.compute_image_embeddings(['my_favorite_image.jpg'])
demo.zero_shot(image_path='my_favorite_image.jpg') 