Update ScreenVLM for ScreenParse v2

Upload the ScreenVLM checkpoint trained with ScreenParse v2 full-element supervision and update the model card. The v1 checkpoint remains available on the v1 branch.

README.md

@@ -20,32 +20,36 @@ library_name: transformers
 
 # ScreenVLM
 
-**ScreenVLM** is a compact (316M-parameter) 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.
+**ScreenVLM** is a compact multimodal vision-language model for **complete screen parsing**: detecting, classifying, localizing, and transcribing UI elements on web page screenshots. Given an image, it produces a structured **ScreenTag** representation with bounding boxes, semantic labels across 55 UI element classes, and text content for visible elements.
+
+## News
+
+- **May 2026**: Updated `main` with the ScreenVLM checkpoint trained on ScreenParse v2. This release uses the v2 training data with more robust quality filtering, 1,447,100 high-quality screenshots, and varied viewport resolutions. The original ScreenVLM checkpoint trained on ScreenParse v1 is retained on the `v1` branch.
 
 - **Developed by**: IBM Research Zurich - ETH Zurich
 - **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)
+- **Paper**: [ScreenParse: Moving Beyond Sparse Grounding with Complete Screen Parsing](https://arxiv.org/abs/2602.14276)
+- **Code**: https://github.com/Saidgurbuz/screenparse
+- **Dataset**: [docling-project/screenparse](https://huggingface.co/datasets/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.
+ScreenVLM builds upon the [Idefics3](https://huggingface.co/docs/transformers/en/model_doc/idefics3) architecture with [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 current `main` checkpoint was trained on ScreenParse v2 full-element screen parsing supervision across 55 semantic UI 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
+- **Complete screen parsing**: Detects all visible UI elements on a page, not only sparse grounding targets
+- **55 UI element classes**: Buttons, links, inputs, navigation bars, menus, images, text, and more
+- **ScreenTag output format**: Structured representation with semantic tags, location tokens, and text content
+- **Compact size**: Single-file safetensors checkpoint suitable for 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:
+ScreenVLM generates output in **ScreenTag** format, where each UI element is wrapped in semantic tags with location tokens:
 
-```
+```html
 <screentag>
 <button><loc_10><loc_20><loc_50><loc_35>Submit</button>
 <link><loc_100><loc_200><loc_180><loc_210>Learn more</link>
@@ -56,7 +60,7 @@ ScreenVLM generates output in **ScreenTag** format — a structured representati
 </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.
+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
 
@@ -72,10 +76,8 @@ 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,
@@ -83,7 +85,6 @@ model = AutoModelForVision2Seq.from_pretrained(
     _attn_implementation="flash_attention_2" if DEVICE == "cuda" else "sdpa",
 ).to(DEVICE)
 
-# Create input
 messages = [
     {
         "role": "user",
@@ -97,7 +98,6 @@ messages = [
 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(
@@ -105,7 +105,6 @@ output = processor.batch_decode(
     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_]*)>"
@@ -115,8 +114,10 @@ def parse_screentag(text, width, height):
     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
+        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
@@ -158,7 +159,6 @@ messages = [
     },
 ]
 
-# Initialize
 llm = LLM(model=MODEL_PATH, limit_mm_per_prompt={"image": 1})
 processor = AutoProcessor.from_pretrained(MODEL_PATH)
 
@@ -168,10 +168,8 @@ sampling_params = SamplingParams(
     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)
@@ -180,53 +178,24 @@ for img_file in sorted(os.listdir(IMAGE_DIR)):
         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")
+print(f"Total: {time.time() - start:.1f}s for {len(batched_inputs)} images")
 ```
 
 ## Training
 
 ScreenVLM was trained using the [nanoVLM](https://github.com/huggingface/nanoVLM) framework with 16 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).
+**Training data**: ScreenParse v2 full-element annotations for complete screen parsing. The v2 training data contains 1,447,100 high-quality web page screenshots across varied viewport resolutions with dense UI element supervision, including bounding boxes, semantic labels, text content, interactability flags, and reading order.
+
+The original ScreenVLM checkpoint trained on ScreenParse v1 remains available with `revision="v1"`.
 
 ## Limitations
 
 - Optimized for **web page screenshots**; performance on mobile or desktop application UIs may vary
-- May struggle with very dense or highly dynamic UIs (e.g., complex dashboards with hundreds of elements)
+- May struggle with very dense or highly dynamic UIs, such as complex dashboards with hundreds of elements
+- Produces structured screen parses, but downstream applications should still validate coordinates and text before using them for high-stakes automation
 
 ## Citation
 

added_tokens.json

@@ -1,616 +1,4 @@
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