update readme example
Browse files
README.md
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@@ -85,21 +85,21 @@ processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large-ft", trust
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
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image = Image.open(requests.get(url, stream=True).raw)
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def run_example(
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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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input_ids=inputs["input_ids"],
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pixel_values=inputs["pixel_values"],
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max_new_tokens=1024,
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num_beams=3
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)
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
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parsed_answer = processor.post_process_generation(generated_text, task=
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print(parsed_answer)
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```
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@@ -113,7 +113,7 @@ Here are the tasks `Florence-2` could perform:
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### OCR
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```python
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prompt = <OCR>
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run_example(prompt)
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```
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@@ -121,25 +121,25 @@ run_example(prompt)
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OCR with region output format:
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{'\<OCR_WITH_REGION>': {'quad_boxes': [[x1, y1, x2, y2, x3, y3, x4, y4], ...], 'labels': ['text1', ...]}}
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```python
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prompt = <OCR_WITH_REGION>
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run_example(prompt)
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```
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### Caption
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```python
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prompt = <CAPTION>
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run_example(prompt)
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```
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### Detailed Caption
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```python
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prompt = <DETAILED_CAPTION>
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run_example(prompt)
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```
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### More Detailed Caption
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```python
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prompt = <MORE_DETAILED_CAPTION>
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run_example(prompt)
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```
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@@ -150,7 +150,7 @@ OD results format:
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'labels': ['label1', 'label2', ...]} }
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```python
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prompt = <OD>
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run_example(prompt)
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```
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@@ -159,7 +159,7 @@ Dense region caption results format:
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{'\<DENSE_REGION_CAPTION>' : {'bboxes': [[x1, y1, x2, y2], ...],
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'labels': ['label1', 'label2', ...]} }
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```python
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prompt = <DENSE_REGION_CAPTION>
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run_example(prompt)
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```
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@@ -168,7 +168,7 @@ Dense region caption results format:
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{'\<REGION_PROPOSAL>': {'bboxes': [[x1, y1, x2, y2], ...],
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'labels': ['', '', ...]}}
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```python
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prompt = <REGION_PROPOSAL>
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run_example(prompt)
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```
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@@ -178,7 +178,7 @@ caption to phrase grounding task requires additional text input, i.e. caption.
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Caption to phrase grounding results format:
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{'\<CAPTION_TO_PHRASE_GROUNDING>': {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['', '', ...]}}
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```python
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task_prompt =
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results = run_example(task_prompt, text_input="A green car parked in front of a yellow building.")
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```
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
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image = Image.open(requests.get(url, stream=True).raw)
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def run_example(task_prompt, text_input=None):
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if text_input is None:
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prompt = task_prompt
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else:
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prompt = task_prompt + text_input
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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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input_ids=inputs["input_ids"],
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pixel_values=inputs["pixel_values"],
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max_new_tokens=1024,
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num_beams=3
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)
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
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parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height))
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print(parsed_answer)
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```
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### OCR
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```python
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prompt = "<OCR>"
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run_example(prompt)
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```
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OCR with region output format:
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{'\<OCR_WITH_REGION>': {'quad_boxes': [[x1, y1, x2, y2, x3, y3, x4, y4], ...], 'labels': ['text1', ...]}}
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```python
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prompt = "<OCR_WITH_REGION>"
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run_example(prompt)
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```
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### Caption
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```python
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prompt = "<CAPTION>"
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run_example(prompt)
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```
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### Detailed Caption
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```python
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prompt = "<DETAILED_CAPTION>"
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run_example(prompt)
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```
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### More Detailed Caption
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```python
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prompt = "<MORE_DETAILED_CAPTION>"
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run_example(prompt)
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```
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'labels': ['label1', 'label2', ...]} }
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```python
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prompt = "<OD>"
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run_example(prompt)
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```
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{'\<DENSE_REGION_CAPTION>' : {'bboxes': [[x1, y1, x2, y2], ...],
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'labels': ['label1', 'label2', ...]} }
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```python
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prompt = "<DENSE_REGION_CAPTION>"
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run_example(prompt)
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```
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{'\<REGION_PROPOSAL>': {'bboxes': [[x1, y1, x2, y2], ...],
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'labels': ['', '', ...]}}
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```python
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prompt = "<REGION_PROPOSAL>"
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run_example(prompt)
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```
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Caption to phrase grounding results format:
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{'\<CAPTION_TO_PHRASE_GROUNDING>': {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['', '', ...]}}
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```python
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task_prompt = "<CAPTION_TO_PHRASE_GROUNDING>"
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results = run_example(task_prompt, text_input="A green car parked in front of a yellow building.")
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```
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