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--- |
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library_name: transformers |
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tags: |
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- image-captioning |
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- visual-question-answering |
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license: apache-2.0 |
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datasets: |
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- X2FD/LVIS-Instruct4V |
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- BAAI/SVIT |
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- HuggingFaceH4/ultrachat_200k |
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- MMInstruction/VLFeedback |
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- zhiqings/LLaVA-Human-Preference-10K |
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language: |
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- en |
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pipeline_tag: image-to-text |
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widget: |
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- src: interior.jpg |
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example_title: Detailed caption |
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output: |
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text: "The image shows a serene and well-lit bedroom with a white bed, a black bed frame, and a white comforter. There’s a gray armchair with a white cushion, a black dresser with a mirror and a vase, and a white rug on the floor. The room has a large window with white curtains, and there are several decorative items, including a picture frame, a vase with a flower, and a lamp. The room is well-organized and has a calming atmosphere." |
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- src: cat.jpg |
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example_title: Short caption |
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output: |
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text: "A white and orange cat stands on its hind legs, reaching towards a wooden table with a white teapot and a basket of red raspberries. The table is on a small wooden bench, surrounded by orange flowers. The cat’s position and action create a serene, playful scene in a garden." |
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--- |
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<img src="Captions.jpg"> |
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## Description |
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UForm-Gen2-dpo is a small generative vision-language model alined for Image Captioning and Visual Question Answering |
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on preference datasets VLFeedback and LLaVA-Human-Preference-10K using Direct Preference Optimization (DPO). |
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The model consists of two parts: |
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1. CLIP-like ViT-H/14 |
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2. [Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) |
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The model took less than one day to train on a DGX-H100 with 8x H100 GPUs. |
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Thanks to [Nebius.ai](https://nebius.ai) for providing the compute 🤗 |
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### Usage |
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The generative model can be used to caption images, answer questions about them. Also it is suitable for a multimodal chat. |
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```python |
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from transformers import AutoModel, AutoProcessor |
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model = AutoModel.from_pretrained("unum-cloud/uform-gen2-dpo", trust_remote_code=True) |
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processor = AutoProcessor.from_pretrained("unum-cloud/uform-gen2-dpo", trust_remote_code=True) |
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prompt = "Question or Instruction" |
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image = Image.open("image.jpg") |
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inputs = processor(text=[prompt], images=[image], return_tensors="pt") |
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with torch.inference_mode(): |
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output = model.generate( |
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**inputs, |
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do_sample=False, |
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use_cache=True, |
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max_new_tokens=256, |
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eos_token_id=151645, |
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pad_token_id=processor.tokenizer.pad_token_id |
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) |
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prompt_len = inputs["input_ids"].shape[1] |
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decoded_text = processor.batch_decode(output[:, prompt_len:])[0] |
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``` |
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You can check examples of different prompts in our demo space. |
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## Evaluation |
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perception reasoning OCR artwork celebrity code_reasoning color commonsense_reasoning count existence landmark numerical_calculation position posters scene text_translation |
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MME Benchmark |
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| Model | perception| reasoning | OCR | artwork | celebrity | code_reasoning | color | commonsense_reasoning | count | existence | landmark | numerical_calculation | position | posters | scene | text_translation | |
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| :---------------------------------- | --------: | --------: | -----:| ----------:| ----------:| --------------:| -----:| ---------------------:| -----:| ---------:| --------:| ---------------------:| --------:| -------:| -----:| ----------------:| |
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| uform-gen2-dpo | 1,048.75 | 224.64 | 72.50 | 97.25 | 62.65 | 67.50 | 123.33 | 57.14 | 136.67 | 195.00 | 104.00 | 50.00 | 51.67 | 59.18 | 146.50 | 50.00 | |
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| uform-gen2-qwen-500m | 863.40 | 236.43 | 57.50 | 93.00 | 67.06 | 57.50 | 78.33 | 81.43 | 53.33 | 150.00 | 98.00 | 50.00 | 50.00 | 62.93 | 153.25 | 47.50 | |
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