Update README.md

README.md

@@ -1,90 +1,14 @@
----
-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
----
+What value will be printed?
 
-# nlpconnect/vit-gpt2-image-captioning
+code snippet image
+(Assume necessary headers are included)
 
-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).
+This code will always print 3, because the thread t is started before the value of y is modified.
 
+This code won’t compile.
 
-# The Illustrated Image Captioning using transformers
+This code will always print 7, because the thread t is joined after both modifications of y.
 
-![](https://ankur3107.github.io/assets/images/vision-encoder-decoder.png)
+This code will print 3, 6 or 7 (depending on thread scheduling by the operating system)
 
-* https://ankur3107.github.io/blogs/the-illustrated-image-captioning-using-transformers/
-
-
-# Sample running code
-
-```python
-
-from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
-import torch
-from PIL import Image
-
-model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-
-
-max_length = 16
-num_beams = 4
-gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
-def predict_step(image_paths):
-  images = []
-  for image_path in image_paths:
-    i_image = Image.open(image_path)
-    if i_image.mode != "RGB":
-      i_image = i_image.convert(mode="RGB")
-
-    images.append(i_image)
-
-  pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values
-  pixel_values = pixel_values.to(device)
-
-  output_ids = model.generate(pixel_values, **gen_kwargs)
-
-  preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
-  preds = [pred.strip() for pred in preds]
-  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
-
-```python
-
-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")
-
-# [{'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
+This code has undefined behavior.