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--- |
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library_name: transformers |
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pipeline_tag: image-text-to-text |
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tags: |
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- computer use |
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license: mit |
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language: |
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- en |
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base_model: |
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- microsoft/Florence-2-base |
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--- |
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# PTA-1: Controlling Computers with Small Models |
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PTA (Prompt-to-Automation) is a vision language model for computer & phone automation, based on Florence-2. |
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With only 270M parameters it outperforms much larger models in GUI text and element localization. |
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This enables low-latency computer automation with local execution. |
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▶️ Try the demo at: [AskUI/PTA-1](https://huggingface.co/spaces/AskUI/PTA-1) |
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**Model Input:** Screenshot + description_of_target_element |
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**Model Output:** BoundingBox for Target Element |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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*Requirements:* torch, timm, einops, Pillow, transformers |
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```python |
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import torch |
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from PIL import Image |
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from transformers import AutoProcessor, AutoModelForCausalLM |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 |
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model = AutoModelForCausalLM.from_pretrained("AskUI/PTA-1", torch_dtype=torch_dtype, trust_remote_code=True).to(device) |
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processor = AutoProcessor.from_pretrained("AskUI/PTA-1", trust_remote_code=True) |
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task_prompt = "<OPEN_VOCABULARY_DETECTION>" |
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prompt = task_prompt + "description of the target element" |
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image = Image.open("path to screenshot").convert("RGB") |
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inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype) |
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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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do_sample=False, |
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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="<OPEN_VOCABULARY_DETECTION>", image_size=(image.width, image.height)) |
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print(parsed_answer) |
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``` |
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## Evaluation |
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**Note:** This is a first version of our evaluation, based on 999 samples (333 samples from each dataset). |
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We are still running all models on the full test sets, and we are seeing ±5% deviations for a subset of the models we have already evaluated. |
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| Model | Parameters | Mean | agentsea/wave-ui | AskUI/pta-text | ivelin/rico_refexp_combined | |
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|--------------------------------------------|------------|--------|------------------|----------------|-----------------------------| |
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| AskUI/PTA-1 | 0.27B | 79.98 | 90.69* | 76.28 | 72.97* | |
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| anthropic.claude-3-5-sonnet-20241022-v2:0 | - | 70.37 | 82.28 | 83.18 | 45.65 | |
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| agentsea/paligemma-3b-ft-waveui-896 | 3.29B | 57.76 | 70.57* | 67.87 | 34.83 | |
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| Qwen/Qwen2-VL-7B-Instruct | 8.29B | 57.26 | 47.45 | 60.66 | 63.66 | |
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| agentsea/paligemma-3b-ft-widgetcap-waveui-448 | 3.29B | 53.15 | 74.17* | 53.45 | 31.83 | |
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| microsoft/Florence-2-base | 0.27B | 39.44 | 22.22 | 81.38 | 14.71 | |
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| microsoft/Florence-2-large | 0.82B | 36.64 | 14.11 | 81.98 | 13.81 | |
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| EasyOCR | - | 29.43 | 3.9 | 75.08 | 9.31 | |
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| adept/fuyu-8b | 9.41B | 26.83 | 5.71 | 71.47 | 3.3 | |
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| Qwen/Qwen2-VL-2B-Instruct | 2.21B | 23.32 | 17.12 | 26.13 | 26.73 | |
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| Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4 | 0.90B | 18.92 | 10.81 | 22.82 | 23.12 | |
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\* Models is known to be trained on the train split of that dataset. |
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The high benchmark scores for our model are partially due to data bias. |
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Therefore, we expect users of the model to fine-tune it according to the data distributions of their use case. |
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#### Metrics |
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Click success rate is calculated as the number of clicks inside the target bounding box relative to all clicks. |
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If a model predicts a target bounding box instead of a click coordinate, its center is used as its click prediction. |
