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README.md

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+---
+license: apache-2.0
+datasets:
+- docling-project/screenparse
+tags:
+- text-generation
+- screen-parsing
+- ui-understanding
+- object-detection
+- grounding
+- web
+- screentag
+- docling
+- granite
+language:
+- en
+pipeline_tag: image-text-to-text
+library_name: transformers
+---
+
+# ScreenVLM
+
+**ScreenVLM** is a compact multimodal vision-language model for **complete screen parsing** — detecting, classifying, and localizing all UI elements on a web page screenshot. Given an image, it produces a structured **ScreenTag** representation with bounding boxes, semantic labels (55 UI element classes), and text content for every visible element.
+
+- **Developed by**: IBM Research
+- **Model type**: Multi-modal model (image+text-to-text)
+- **Language(s)**: English
+- **License**: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+- **Paper**: [ScreenParse: Moving Beyond Sparse Grounding with Complete Screen Parsing](TODO)
+- **Code**: [GitHub](TODO)
+- **Dataset**: [docling-project/screenparse](https://huggingface.co/docling-project/screenparse)
+
+## Model Summary
+
+ScreenVLM builds upon the [Idefics3](https://huggingface.co/docs/transformers/en/model_doc/idefics3) architecture with two key modifications: it uses [siglip2-base-patch16-512](https://huggingface.co/google/siglip2-base-patch16-512) as the vision encoder and a Granite 165M LLM as the language backbone. The model was trained on **ScreenParse**, a large-scale dataset of 771K web screenshots with dense UI element annotations across 55 semantic classes.
+
+### Key Features
+
+- **Complete screen parsing**: Detects all UI elements on a page, not just sparse grounding targets
+- **55 UI element classes**: Buttons, links, inputs, navigation bars, menus, images, and more
+- **ScreenTag output format**: Structured, hierarchical representation with bounding boxes and text
+- **Compact size**: ~258M parameters (714MB safetensors), enabling fast inference
+
+## Output Format
+
+ScreenVLM generates output in **ScreenTag** format — a structured representation where each UI element is wrapped in semantic tags with location tokens:
+
+```
+<screentag>
+<button><loc_10><loc_20><loc_50><loc_35>Submit</button>
+<link><loc_100><loc_200><loc_180><loc_210>Learn more</link>
+<navigation_bar><loc_0><loc_0><loc_500><loc_30>
+  <link><loc_10><loc_5><loc_60><loc_25>Home</link>
+  <link><loc_70><loc_5><loc_120><loc_25>About</link>
+</navigation_bar>
+</screentag>
+```
+
+Each `<loc_X>` token represents a coordinate in the normalized [0, 500] space. Four consecutive location tokens define `<left><top><right><bottom>` of the bounding box.
+
+## Usage
+
+### Inference with Transformers
+
+```python
+import re
+import torch
+from transformers import AutoProcessor, AutoModelForVision2Seq
+from transformers.image_utils import load_image
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+MODEL_PATH = "docling-project/ScreenVLM"
+NORM_SIZE = 500
+
+# Load image
+image = load_image("https://example.com/screenshot.png")
+
+# Initialize processor and model
+processor = AutoProcessor.from_pretrained(MODEL_PATH)
+model = AutoModelForVision2Seq.from_pretrained(
+    MODEL_PATH,
+    torch_dtype=torch.bfloat16,
+    _attn_implementation="flash_attention_2" if DEVICE == "cuda" else "sdpa",
+).to(DEVICE)
+
+# Create input
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": "Generate the screen representation for this UI:"},
+        ],
+    },
+]
+
+prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+inputs = processor(text=prompt, images=[image], return_tensors="pt").to(DEVICE)
+
+# Generate
+generated_ids = model.generate(**inputs, max_new_tokens=6192)
+prompt_length = inputs.input_ids.shape[1]
+output = processor.batch_decode(
+    generated_ids[:, prompt_length:],
+    skip_special_tokens=False,
+)[0].lstrip()
+
+# Parse ScreenTag output into structured UI elements
+def parse_screentag(text, width, height):
+    pattern = re.compile(
+        r"<(?P<tag>[a-zA-Z][a-zA-Z0-9_]*)>"
+        r"\s*<loc_(?P<l>\d+)><loc_(?P<t>\d+)><loc_(?P<r>\d+)><loc_(?P<b>\d+)>"
+        r"(?P<text>[^<]*)"
+    )
+    elements = []
+    for m in pattern.finditer(text):
+        l, t, r, b = [max(0, min(int(m.group(k)), NORM_SIZE)) for k in ("l", "t", "r", "b")]
+        if r < l: l, r = r, l
+        if b < t: t, b = b, t
+        x = l / NORM_SIZE * width
+        y = t / NORM_SIZE * height
+        w = (r - l) / NORM_SIZE * width
+        h = (b - t) / NORM_SIZE * height
+        elements.append({
+            "label": m.group("tag"),
+            "bbox": (x, y, w, h),
+            "text": m.group("text").strip() or None,
+        })
+    return elements
+
+elements = parse_screentag(output, *image.size)
+for el in elements:
+    print(f"{el['label']:20s} bbox=({int(el['bbox'][0]):4d},{int(el['bbox'][1]):4d},{int(el['bbox'][2]):4d},{int(el['bbox'][3]):4d})  text={el['text']!r}")
+```
+
+### Batch Inference with vLLM
+
+```python
+import os
+import re
+import time
+from vllm import LLM, SamplingParams
+from transformers import AutoProcessor
+from PIL import Image
+
+MODEL_PATH = "docling-project/ScreenVLM"
+IMAGE_DIR = "screenshots/"
+PROMPT_TEXT = "Generate the screen representation for this UI:"
+NORM_SIZE = 500
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": PROMPT_TEXT},
+        ],
+    },
+]
+
+# Initialize
+llm = LLM(model=MODEL_PATH, limit_mm_per_prompt={"image": 1})
+processor = AutoProcessor.from_pretrained(MODEL_PATH)
+
+sampling_params = SamplingParams(
+    temperature=0.0,
+    max_tokens=6192,
+    skip_special_tokens=False,
+)
+
+# Build batch
+batched_inputs = []
+image_sizes = []
+
+for img_file in sorted(os.listdir(IMAGE_DIR)):
+    if img_file.lower().endswith((".png", ".jpg", ".jpeg")):
+        img_path = os.path.join(IMAGE_DIR, img_file)
+        image = Image.open(img_path).convert("RGB")
+        prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+        batched_inputs.append({"prompt": prompt, "multi_modal_data": {"image": image}})
+        image_sizes.append((img_file, image.size))
+
+# Run batch inference
+start = time.time()
+outputs = llm.generate(batched_inputs, sampling_params=sampling_params)
+
+# Parse ScreenTag output into structured UI elements
+def parse_screentag(text, width, height):
+    pattern = re.compile(
+        r"<(?P<tag>[a-zA-Z][a-zA-Z0-9_]*)>"
+        r"\s*<loc_(?P<l>\d+)><loc_(?P<t>\d+)><loc_(?P<r>\d+)><loc_(?P<b>\d+)>"
+        r"(?P<text>[^<]*)"
+    )
+    elements = []
+    for m in pattern.finditer(text):
+        l, t, r, b = [max(0, min(int(m.group(k)), NORM_SIZE)) for k in ("l", "t", "r", "b")]
+        if r < l: l, r = r, l
+        if b < t: t, b = b, t
+        x = l / NORM_SIZE * width
+        y = t / NORM_SIZE * height
+        w = (r - l) / NORM_SIZE * width
+        h = (b - t) / NORM_SIZE * height
+        elements.append({
+            "label": m.group("tag"),
+            "bbox": (x, y, w, h),
+            "text": m.group("text").strip() or None,
+        })
+    return elements
+
+for output, (name, (w, h)) in zip(outputs, image_sizes):
+    screentag = output.outputs[0].text
+    elements = parse_screentag(screentag, w, h)
+    print(f"--- {name} ({len(elements)} elements) ---")
+    for el in elements:
+        print(f"  {el['label']:20s} bbox=({int(el['bbox'][0]):4d},{int(el['bbox'][1]):4d},{int(el['bbox'][2]):4d},{int(el['bbox'][3]):4d})  text={el['text']!r}")
+
+print(f"\nTotal: {time.time() - start:.1f}s for {len(batched_inputs)} images")
+```
+
+## Training
+
+ScreenVLM was trained using the [nanoVLM](https://github.com/huggingface/nanoVLM) framework on IBM's Blue Vela supercomputing cluster with NVIDIA H100 GPUs.
+
+**Training data**: [ScreenParse](https://huggingface.co/docling-project/screenparse) — 771K web page screenshots with dense annotations across 55 UI element classes, including bounding boxes, semantic labels, text content, interactability flags, and reading order. Annotations were generated through automated DOM extraction, IoU-based filtering, and VLM-based refinement (Qwen3-VL-8B).
+
+## Limitations
+
+- Optimized for **web page screenshots**; performance on mobile or desktop application UIs may vary
+- Coordinate predictions are approximate — fine-grained pixel-level precision is not guaranteed
+- May struggle with very dense or highly dynamic UIs (e.g., complex dashboards with hundreds of elements)
+- Not designed for general image understanding — use [Granite Vision](https://huggingface.co/collections/ibm-granite/granite-vision-models-67b3bd4ff90c915ba4cd2800) for general-purpose vision tasks
+
+## Citation
+
+```bibtex
+@inproceedings{screenparse2026,
+  title={ScreenParse: Moving Beyond Sparse Grounding with Complete Screen Parsing},
+  author={TODO},
+  booktitle={Proceedings of the 43rd International Conference on Machine Learning (ICML)},
+  year={2026}
+}
+```

added_tokens.json

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chat_template.jinja

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+{%- for message in messages -%}
+{{- '<|start_of_role|>' + message['role'] + '<|end_of_role|>' -}}
+{%- if message['content'] is string -%}
+{{- message['content'] -}}
+{%- else -%}
+{%- for part in message['content'] -%}
+{%- if part['type'] == 'text' -%}
+{{- part['text'] -}}
+{%- elif part['type'] == 'image' -%}
+{{- '<image>' -}}
+{%- endif -%}
+{%- endfor -%}
+{%- endif -%}
+{{- '<|end_of_text|>
+' -}}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+{{- '<|start_of_role|>assistant' -}}
+{%- if controls -%}{{- ' ' + controls | tojson() -}}{%- endif -%}
+{{- '<|end_of_role|>' -}}
+{%- endif -%}

config.json

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+{
+  "architectures": [
+    "Idefics3ForConditionalGeneration"
+  ],
+  "bos_token_id": 100264,
+  "eos_token_id": 100257,
+  "image_token_id": 100270,
+  "model_type": "idefics3",
+  "pad_token_id": 100257,
+  "scale_factor": 4,
+  "text_config": {
+    "_name_or_path": "/proj/docling-vision/models/granitev06_hf_ai4k_sft_data_v4",
+    "architectures": [
+      "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 100264,
+    "eos_token_id": 100257,
+    "head_dim": 64,
+    "hidden_act": "silu",
+    "hidden_size": 576,
+    "initializer_range": 0.02,
+    "intermediate_size": 1536,
+    "max_position_embeddings": 8192,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 9,
+    "num_hidden_layers": 30,
+    "num_key_value_heads": 3,
+    "pad_token_id": 100257,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": null,
+    "rope_theta": 100000.0,
+    "torch_dtype": "bfloat16",
+    "use_cache": false,
+    "vocab_size": 100965
+  },
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.2",
+  "use_cache": true,
+  "vision_config": {
+    "attention_dropout": 0.0,
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 768,
+    "image_size": 512,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "layer_norm_eps": 1e-06,
+    "max_image_size": {
+      "longest_edge": 512
+    },
+    "model_type": "idefics3_vision",
+    "num_attention_heads": 12,
+    "num_channels": 3,
+    "num_hidden_layers": 12,
+    "patch_size": 16,
+    "size": {
+      "longest_edge": 512
+    }
+  },
+  "vocab_size": 100965
+}

generation_config.json

@@ -0,0 +1,8 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 100264,
+  "eos_token_id": 100257,
+  "pad_token_id": 100257,
+  "transformers_version": "4.55.2",
+  "use_cache": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:459614601e12b3e547fc8412104ab6a915bd28f08ba6ab766e166e23573e686c
+size 748422736

preprocessor_config.json

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+{
+  "do_convert_rgb": null,
+  "do_image_splitting": true,
+  "do_normalize": true,
+  "do_pad": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "Idefics3ImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "max_image_size": {
+    "longest_edge": 512
+  },
+  "processor_class": "Idefics3Processor",
+  "resample": 1,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "longest_edge": 2048
+  }
+}

processor_config.json

@@ -0,0 +1,4 @@
+{
+  "image_seq_len": 64,
+  "processor_class": "Idefics3Processor"
+}

special_tokens_map.json

@@ -0,0 +1,666 @@
+{
+  "Alert_close": "</Alert>",
+  "Alert_open": "<Alert>",
+  "App_Icon_close": "</App_Icon>",
+  "App_Icon_open": "<App_Icon>",
+  "Avatar_close": "</Avatar>",
+  "Avatar_open": "<Avatar>",
+  "Badge_close": "</Badge>",
+  "Badge_open": "<Badge>",
+  "Bottom_navigation_close": "</Bottom_navigation>",
+  "Bottom_navigation_open": "<Bottom_navigation>",
+  "Breadcrumb_close": "</Breadcrumb>",
+  "Breadcrumb_open": "<Breadcrumb>",
+  "Button_close": "</Button>",
+  "Button_open": "<Button>",
+  "Calendar_close": "</Calendar>",
+  "Calendar_open": "<Calendar>",
+  "Carousel_close": "</Carousel>",
+  "Carousel_open": "<Carousel>",
+  "Chart_close": "</Chart>",
+  "Chart_open": "<Chart>",
+  "Checkbox_close": "</Checkbox>",
+  "Checkbox_open": "<Checkbox>",
+  "Code_snippet_close": "</Code_snippet>",
+  "Code_snippet_open": "<Code_snippet>",
+  "Column_Browser_close": "</Column_Browser>",
+  "Column_Browser_open": "<Column_Browser>",
+  "ContextMenu_close": "</ContextMenu>",
+  "ContextMenu_open": "<ContextMenu>",
+  "Date-Time_picker_close": "</Date-Time_picker>",
+  "Date-Time_picker_open": "<Date-Time_picker>",
+  "DockMenu_close": "</DockMenu>",
+  "DockMenu_open": "<DockMenu>",
+  "EditMenu_close": "</EditMenu>",
+  "EditMenu_open": "<EditMenu>",
+  "File_Icon_close": "</File_Icon>",
+  "File_Icon_open": "<File_Icon>",
+  "Heading_close": "</Heading>",
+  "Heading_open": "<Heading>",
+  "Image_close": "</Image>",
+  "Image_open": "<Image>",
+  "Link_close": "</Link>",
+  "Link_open": "<Link>",
+  "List_Item_close": "</List_Item>",
+  "List_Item_open": "<List_Item>",
+  "List_close": "</List>",
+  "List_open": "<List>",
+  "Logo_close": "</Logo>",
+  "Logo_open": "<Logo>",
+  "Menu_close": "</Menu>",
+  "Menu_open": "<Menu>",
+  "Navigation_Bar_close": "</Navigation_Bar>",
+  "Navigation_Bar_open": "<Navigation_Bar>",
+  "Notification_close": "</Notification>",
+  "Notification_open": "<Notification>",
+  "Page_control_close": "</Page_control>",
+  "Page_control_open": "<Page_control>",
+  "Pagination_close": "</Pagination>",
+  "Pagination_open": "<Pagination>",
+  "Picker_close": "</Picker>",
+  "Picker_open": "<Picker>",
+  "PopUp_Menu_close": "</PopUp_Menu>",
+  "PopUp_Menu_open": "<PopUp_Menu>",
+  "Progress_bar_close": "</Progress_bar>",
+  "Progress_bar_open": "<Progress_bar>",
+  "Radiobox_close": "</Radiobox>",
+  "Radiobox_open": "<Radiobox>",
+  "Rating_Indicator_close": "</Rating_Indicator>",
+  "Rating_Indicator_open": "<Rating_Indicator>",
+  "Screen_close": "</Screen>",
+  "Screen_open": "<Screen>",
+  "Scroll_close": "</Scroll>",
+  "Scroll_open": "<Scroll>",
+  "Search_Bar_close": "</Search_Bar>",
+  "Search_Bar_open": "<Search_Bar>",
+  "Search_Field_close": "</Search_Field>",
+  "Search_Field_open": "<Search_Field>",
+  "Select_close": "</Select>",
+  "Select_open": "<Select>",
+  "Side_Bar_close": "</Side_Bar>",
+  "Side_Bar_open": "<Side_Bar>",
+  "Slider_close": "</Slider>",
+  "Slider_open": "<Slider>",
+  "Status_Bar_close": "</Status_Bar>",
+  "Status_Bar_open": "<Status_Bar>",
+  "Steppers_close": "</Steppers>",
+  "Steppers_open": "<Steppers>",
+  "Switch_close": "</Switch>",
+  "Switch_open": "<Switch>",
+  "Tab_Bar_close": "</Tab_Bar>",
+  "Tab_Bar_open": "<Tab_Bar>",
+  "Tab_close": "</Tab>",
+  "Tab_open": "<Tab>",
+  "Table_close": "</Table>",
+  "Table_open": "<Table>",
+  "Text_Input_close": "</Text_Input>",
+  "Text_Input_open": "<Text_Input>",
+  "Text_close": "</Text>",
+  "Text_open": "<Text>",
+  "Toggles_close": "</Toggles>",
+  "Toggles_open": "<Toggles>",
+  "Toolbar_close": "</Toolbar>",
+  "Toolbar_open": "<Toolbar>",
+  "Tooltip_close": "</Tooltip>",
+  "Tooltip_open": "<Tooltip>",
+  "Utility_Button_close": "</Utility_Button>",
+  "Utility_Button_open": "<Utility_Button>",
+  "Video_close": "</Video>",
+  "Video_open": "<Video>",
+  "Window_close": "</Window>",
+  "Window_open": "<Window>",
+  "additional_special_tokens": [
+    {
+      "content": "<fake_token_around_image>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<image>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<end_of_utterance>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    }
+  ],
+  "bos_token": {
+    "content": "<|start_of_role|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "end_of_role": "<|end_of_role|>",
+  "end_of_text": "<|end_of_text|>",
+  "eos_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "fake_token_around_image": "<fake_token_around_image>",
+  "global-img": "<global-img>",
+  "image_token": "<image>",
+  "loc_0": "<loc_0>",
+  "loc_1": "<loc_1>",
+  "loc_10": "<loc_10>",
+  "loc_100": "<loc_100>",
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