Update README.md
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README.md
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@@ -207,6 +207,135 @@ Here is the annotated image:
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<a href="https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" target="_blank"><img src="https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" width="500"></a>
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## Running the Flask Server
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_flask_kosmos2.py_ shows the implementation of a Flask server for the model.
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It allowes the model to be approached as a REST API.
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<a href="https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" target="_blank"><img src="https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" width="500"></a>
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## Different tasks
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This model is capable of performing different tasks through changing the prompts.
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First, let's define a function to run a prompt.
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```python
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import requests
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from PIL import Image
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from transformers import AutoProcessor, AutoModelForVision2Seq
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model = AutoModelForVision2Seq.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True)
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processor = AutoProcessor.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True)
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url = "https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/snowman.png"
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image = Image.open(requests.get(url, stream=True).raw)
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def run_example(prompt):
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inputs = processor(text=prompt, images=image, return_tensors="pt")
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generated_ids = model.generate(
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pixel_values=inputs["pixel_values"],
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input_ids=inputs["input_ids"][:, :-1],
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attention_mask=inputs["attention_mask"][:, :-1],
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img_features=None,
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img_attn_mask=inputs["img_attn_mask"][:, :-1],
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use_cache=True,
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max_new_tokens=64,
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)
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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_processed_text = processor.post_process_generation(generated_text, cleanup_and_extract=False)
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processed_text, entities = processor.post_process_generation(generated_text)
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print(processed_text)
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print(entities)
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print(_processed_text)
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```
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Here are the tasks `Kosmos-2` could perform:
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### Multimodal Grounding
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#### • Phrase Grounding
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```python
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prompt = "<grounding><phrase> a snowman</phrase>"
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run_example(prompt)
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# a snowman is warming himself by the fire
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# [('a snowman', (0, 9), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('the fire', (32, 40), [(0.203125, 0.015625, 0.453125, 0.859375)])]
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# <grounding><phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> is warming himself by<phrase> the fire</phrase><object><patch_index_0006><patch_index_0878></object>
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```
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#### • Referring Expression Comprehension
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```python
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prompt = "<grounding><phrase> a snowman</phrase>"
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run_example(prompt)
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# a snowman is warming himself by the fire
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# [('a snowman', (0, 9), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('the fire', (32, 40), [(0.203125, 0.015625, 0.453125, 0.859375)])]
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# <grounding><phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> is warming himself by<phrase> the fire</phrase><object><patch_index_0006><patch_index_0878></object>
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```
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### Multimodal Referring
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#### • Referring expression generation
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```python
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prompt = "<grounding><phrase> It</phrase><object><patch_index_0044><patch_index_0863></object> is"
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run_example(prompt)
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# It is snowman in a hat and scarf
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# [('It', (0, 2), [(0.390625, 0.046875, 0.984375, 0.828125)])]
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# <grounding><phrase> It</phrase><object><patch_index_0044><patch_index_0863></object> is snowman in a hat and scarf
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```
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### Perception-Language Tasks
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#### • Grounded VQA
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```python
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prompt = "<grounding> Question: What is special about this image? Answer:"
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run_example(prompt)
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# Question: What is special about this image? Answer: The image features a snowman sitting by a campfire in the snow.
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# [('a snowman', (71, 80), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a campfire', (92, 102), [(0.109375, 0.640625, 0.546875, 0.984375)])]
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# <grounding> Question: What is special about this image? Answer: The image features<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> sitting by<phrase> a campfire</phrase><object><patch_index_0643><patch_index_1009></object> in the snow.
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```
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#### • Grounded VQA with multimodal referring via bounding boxes
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```python
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prompt = "<grounding> Question: Where is<phrase> the fire</phrase><object><patch_index_0005><patch_index_0911></object> next to? Answer:"
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run_example(prompt)
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# Question: Where is the fire next to? Answer: Near the snowman.
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# [('the fire', (19, 27), [(0.171875, 0.015625, 0.484375, 0.890625)]), ('the snowman', (50, 61), [(0.390625, 0.046875, 0.984375, 0.828125)])]
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# <grounding> Question: Where is<phrase> the fire</phrase><object><patch_index_0005><patch_index_0911></object> next to? Answer: Near<phrase> the snowman</phrase><object><patch_index_0044><patch_index_0863></object>.
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```
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### Grounded Image captioning
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#### • Brief
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```python
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prompt = "<grounding> An image of"
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run_example(prompt)
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# An image of a snowman warming himself by a campfire.
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# [('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a campfire', (41, 51), [(0.109375, 0.640625, 0.546875, 0.984375)])]
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# <grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a campfire</phrase><object><patch_index_0643><patch_index_1009></object>.
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```
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#### • Detailed
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```python
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prompt = "<grounding> Describe this image in detail:"
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run_example(prompt)
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# Describe this image in detail: The image features a snowman sitting by a campfire in the snow. He is wearing a hat, scarf, and gloves, with a pot nearby and a cup
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# [('a campfire', (71, 81), [(0.171875, 0.015625, 0.484375, 0.984375)]), ('a hat', (109, 114), [(0.515625, 0.046875, 0.828125, 0.234375)]), ('scarf', (116, 121), [(0.515625, 0.234375, 0.890625, 0.578125)]), ('gloves', (127, 133), [(0.515625, 0.390625, 0.640625, 0.515625)]), ('a pot', (140, 145), [(0.078125, 0.609375, 0.265625, 0.859375)])]
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# <grounding> Describe this image in detail: The image features a snowman sitting by<phrase> a campfire</phrase><object><patch_index_0005><patch_index_1007></object> in the snow. He is wearing<phrase> a hat</phrase><object><patch_index_0048><patch_index_0250></object>,<phrase> scarf</phrase><object><patch_index_0240><patch_index_0604></object>, and<phrase> gloves</phrase><object><patch_index_0400><patch_index_0532></object>, with<phrase> a pot</phrase><object><patch_index_0610><patch_index_0872></object> nearby and<phrase> a cup</phrase><object>
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```
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## Running the Flask Server
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_flask_kosmos2.py_ shows the implementation of a Flask server for the model.
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It allowes the model to be approached as a REST API.
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