Update README.md

README.md

@@ -3,27 +3,8 @@ tags:
 - image-to-text
 - image-captioning
 license: apache-2.0
-widget:
-- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg
-  example_title: Savanna
-- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
-  example_title: Football Match
-- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg
-  example_title: Airport
 ---
 
-# nlpconnect/vit-gpt2-image-captioning
-
-This is an image captioning model trained by @ydshieh in [flax ](https://github.com/huggingface/transformers/tree/main/examples/flax/image-captioning) this is pytorch version of [this](https://huggingface.co/ydshieh/vit-gpt2-coco-en-ckpts).
-
-
-# The Illustrated Image Captioning using transformers
-
-![](https://ankur3107.github.io/assets/images/vision-encoder-decoder.png)
-
-* https://ankur3107.github.io/blogs/the-illustrated-image-captioning-using-transformers/
-
-
 # Sample running code
 
 ```python
@@ -32,9 +13,9 @@ from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTok
 import torch
 from PIL import Image
 
-model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+model = VisionEncoderDecoderModel.from_pretrained("jaimin/image_caption")
+feature_extractor = ViTFeatureExtractor.from_pretrained("jaimin/image_caption")
+tokenizer = AutoTokenizer.from_pretrained("jaimin/image_caption")
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
@@ -63,8 +44,6 @@ def predict_step(image_paths):
   return preds
 
 
-predict_step(['doctor.e16ba4e4.jpg']) # ['a woman in a hospital bed with a woman in a hospital bed']
-
 ```
 
 # Sample running code using transformers pipeline
@@ -73,18 +52,9 @@ predict_step(['doctor.e16ba4e4.jpg']) # ['a woman in a hospital bed with a woman
 
 from transformers import pipeline
 
-image_to_text = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
-
-image_to_text("https://ankur3107.github.io/assets/images/image-captioning-example.png")
+image_to_text = pipeline("image-to-text", model="jaimin/image_caption")
 
-# [{'generated_text': 'a soccer game with a player jumping to catch the ball '}]
 
 
-```
-
 
-# Contact for any help
-* https://huggingface.co/ankur310794
-* https://twitter.com/ankur310794
-* http://github.com/ankur3107
-* https://www.linkedin.com/in/ankur310794
+```