Upload README.md

README.md

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+---
+tags:
+- image-classification
+library_name: generic
+---
+
+## Example
+
+The model is by no means a state-of-the-art model, but nevertheless
+produces reasonable image captioning results. It was mainly fine-tuned 
+as a proof-of-concept for the 🤗 FlaxVisionEncoderDecoder Framework.
+
+The model can be used as follows:
+
+**In PyTorch**
+```python
+
+import torch
+import requests
+from PIL import Image
+from transformers import ViTFeatureExtractor, AutoTokenizer, VisionEncoderDecoderModel
+
+
+loc = "ydshieh/vit-gpt2-coco-en"
+
+feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
+tokenizer = AutoTokenizer.from_pretrained(loc)
+model = VisionEncoderDecoderModel.from_pretrained(loc)
+model.eval()
+
+
+def predict(image):
+
+    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
+
+    with torch.no_grad():
+        output_ids = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True).sequences
+
+    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+    preds = [pred.strip() for pred in preds]
+
+    return preds
+
+
+# We will verify our results on an image of cute cats
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+with Image.open(requests.get(url, stream=True).raw) as image:
+    preds = predict(image)
+
+print(preds)
+# should produce
+# ['a cat laying on top of a couch next to another cat']
+
+```
+
+**In Flax**
+```python
+
+import jax
+import requests
+from PIL import Image
+from transformers import ViTFeatureExtractor, AutoTokenizer, FlaxVisionEncoderDecoderModel
+
+
+loc = "ydshieh/vit-gpt2-coco-en"
+
+feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
+tokenizer = AutoTokenizer.from_pretrained(loc)
+model = FlaxVisionEncoderDecoderModel.from_pretrained(loc)
+
+gen_kwargs = {"max_length": 16, "num_beams": 4}
+
+
+# This takes sometime when compiling the first time, but the subsequent inference will be much faster
+@jax.jit
+def generate(pixel_values):
+    output_ids = model.generate(pixel_values, **gen_kwargs).sequences
+    return output_ids
+    
+    
+def predict(image):
+
+    pixel_values = feature_extractor(images=image, return_tensors="np").pixel_values
+    output_ids = generate(pixel_values)
+    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+    preds = [pred.strip() for pred in preds]
+    
+    return preds
+    
+    
+# We will verify our results on an image of cute cats
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+with Image.open(requests.get(url, stream=True).raw) as image:
+    preds = predict(image)
+    
+print(preds)
+# should produce
+# ['a cat laying on top of a couch next to another cat']
+
+```