Upload README.md

README.md

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+---
+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
+
+from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
+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")
+
+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