readme updated

README.md

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+---
+tags:
+- image-captioning
+license: apache-2.0
+
+---
+
+# nlpconnect/vit-gpt2-image-captioning
+
+This is an image captioning model training by @ydshieh in flax, this is pytorch version of https://huggingface.co/ydshieh/vit-gpt2-coco-en-ckpts model.
+
+
+# Sample running code
+
+```python
+
+from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
+
+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']
+
+```
+