AskUI
/

PTA-1

@@ -1,9 +1,14 @@
----
-library_name: transformers
-pipeline_tag: image-text-to-text
-tags:
-  - vision
----
 # PTA-1: Controlling Computers with Small Models
@@ -15,63 +20,6 @@ This allows low latency computer automations with local execution.
 **Model Output:** BoundingBox for Target Element
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-### Model Sources [optional]
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-## Uses
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-### Direct Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 ## How to Get Started with the Model
@@ -110,98 +58,33 @@ print(parsed_answer)
 ```
-## Training Details
-### Training Data
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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 ## Evaluation
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-### Testing Data, Factors & Metrics
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+---
+library_name: transformers
+pipeline_tag: image-text-to-text
+tags:
+- vision
+license: mit
+language:
+- en
+base_model:
+- microsoft/Florence-2-base
+---
 # PTA-1: Controlling Computers with Small Models
 **Model Output:** BoundingBox for Target Element
 ## How to Get Started with the Model
 ```
 ## Evaluation
+**Note:** This is a first version of our evaluation with 999 samples (333 samples from each dataset).
+We are still running all models on the full test sets. We are seeing +-5% deviations for a subset of the models we have already evaluated.
+| Model                                      | Parameters | Mean   | agentsea/wave-ui | AskUI/pta-text | ivelin/rico_refexp_combined |
+|--------------------------------------------|------------|--------|------------------|----------------|-----------------------------|
+| AskUI/PTA-1                                | 0.27B      | 79.98  | 90.69*           | 76.28          | 72.97*                      |
+| anthropic.claude-3-5-sonnet-20241022-v2:0  | -          | 70.37  | 82.28            | 83.18          | 45.65                       |
+| agentsea/paligemma-3b-ft-waveui-896        | 3.29B      | 57.76  | 70.57*           | 67.87          | 34.83                       |
+| Qwen/Qwen2-VL-7B-Instruct                  | 8.29B      | 57.26  | 47.45            | 60.66          | 63.66                       |
+| agentsea/paligemma-3b-ft-widgetcap-waveui-448 | 3.29B   | 53.15  | 74.17*           | 53.45          | 31.83                       |
+| microsoft/Florence-2-base                  | 0.27B      | 39.44  | 22.22            | 81.38          | 14.71                       |
+| microsoft/Florence-2-large                 | 0.82B      | 36.64  | 14.11            | 81.98          | 13.81                       |
+| EasyOCR                                    | -          | 29.43  | 3.9              | 75.08          | 9.31                        |
+| adept/fuyu-8b                              | 9.41B      | 26.83  | 5.71             | 71.47          | 3.3                         |
+| Qwen/Qwen2-VL-2B-Instruct                  | 2.21B      | 23.32  | 17.12            | 26.13          | 26.73                       |
+| Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4        | 0.90B      | 18.92  | 10.81            | 22.82          | 23.12                       |
+\* Models is known to be trained on the train split of that dataset.
+The high benchmark scores for our model are partially due to data bias.
+Therefore we expect users of the model to fine-tune it according to the data distributions of their use case.
 #### Metrics
+Click success rate is calculated as the number of clicks inside the target bounding box.
+If a model predicts a target bounding box instead of a click coordinate, its center is used as its click prediction.