Paper | Detailed Caption | Model Weight | Model Weight in wisemodel
----- **Monkey** brings a training-efficient approach to effectively improve the input resolution capacity up to 896 x 1344 pixels without pretraining from the start. To bridge the gap between simple text labels and high input resolution, we propose a multi-level description generation method, which automatically provides rich information that can guide the model to learn the contextual association between scenes and objects. With the synergy of these two designs, our model achieved excellent results on multiple benchmarks. By comparing our model with various LMMs, including GPT4V, our model demonstrates promising performance in image captioning by paying attention to textual information and capturing fine details within the images; its improved input resolution also enables remarkable performance in document images with dense text. ## Spotlights - **Contextual associations.** Our method demonstrates a superior ability to infer the relationships between targets more effectively when answering questions, which results in delivering more comprehensive and insightful results. - **Support resolution up to 1344 x 896.** Surpassing the standard 448 x 448 resolution typically employed for LMMs, this significant increase in resolution augments the ability to discern and understand unnoticeable or tightly clustered objects and dense text. - **Enhanced general performance.** We carried out testing across 16 diverse datasets, leading to impressive performance by our Monkey model in tasks such as Image Captioning, General Visual Question Answering, Text-centric Visual Question Answering, and Document-oriented Visual Question Answering. ## Environment ```python conda create -n monkey python=3.9 conda activate monkey git clone https://github.com/Yuliang-Liu/Monkey.git cd ./Monkey pip install -r requirements.txt ``` ## Demo Before 14/11/2023, we have observed that for some random pictures Monkey can achieve more accurate results than GPT4V. We also provide the source code and the model weight for the original demo, allowing you to customize certain parameters for a more unique experience. The specific operations are as follows: 1. Make sure you have configured the [environment](#environment). 2. You can choose to use the demo offline or online: - **Offline:** - Download the [Model Weight](http://huggingface.co/echo840/Monkey). - Modify `DEFAULT_CKPT_PATH="pathto/Monkey"` in the `demo.py` file to your model weight path. - Run the demo using the following command: ``` python demo.py ``` - **Online:** - Run the demo and download model weights online with the following command: ``` python demo.py -c echo840/Monkey ``` ## Dataset We have open-sourced the data generated by the multi-level description generation method. You can download it at [Detailed Caption](https://huggingface.co/datasets/echo840/Detailed_Caption). ## Evaluate We offer evaluation code for 14 Visual Question Answering (VQA) datasets in the `evaluate_vqa.py` file, facilitating a quick verification of results. The specific operations are as follows: 1. Make sure you have configured the [environment](#environment). 2. Modify `sys.path.append("pathto/Monkey")` to your model weight path. 3. Prepare the datasets required for evaluation. 4. Run the evaluation code. Take ESTVQA as an example: - Prepare data according to the following directory structure: ``` ├── data | ├── estvqa | ├── test_image | ├── {image_path0} | ├── {image_path1} | · | · | ├── estvqa.jsonl ``` - Example of the format of each line of the annotated `.jsonl` file: ``` {"image": "data/estvqa/test_image/011364.jpg", "question": "What is this store?", "answer": "pizzeria", "question_id": 0} ``` - Modify the dictionary `ds_collections`: ``` ds_collections = { 'estvqa_test': { 'test': 'data/estvqa/estvqa.jsonl', 'metric': 'anls', 'max_new_tokens': 100, }, ... } ``` - Run the following command: ``` bash eval/eval.sh 'EVAL_PTH' 'SAVE_NAME' ``` ## Train We also offer Monkey's model definition and training code, which you can explore above. You can execute the training code through executing `finetune_ds_debug.sh`. **ATTENTION:** Specify the path to your training data, which should be a json file consisting of a list of conversations. ## Inference ```python from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "echo840/Monkey" model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map='cuda', trust_remote_code=True).eval() tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True) tokenizer.padding_side = 'left' tokenizer.pad_token_id = tokenizer.eod_id img_path = "" question = "" query = f'