From 13563c9b2b1386b02e4ae6a3f959ee84676ce6fa Mon Sep 17 00:00:00 2001 From: Holger Roth Date: Wed, 23 Oct 2024 21:29:17 -0400 Subject: [PATCH 1/4] update readme --- README.md | 4 +++ m3/README.md | 55 ++++++++++++++++++++++--------- m3/docs/images/gradio_app_ct.png | Bin 0 -> 243758 bytes m3/docs/images/training.png | Bin 0 -> 268456 bytes 4 files changed, 43 insertions(+), 16 deletions(-) create mode 100644 m3/docs/images/gradio_app_ct.png create mode 100644 m3/docs/images/training.png diff --git a/README.md b/README.md index f37167c..5511699 100644 --- a/README.md +++ b/README.md @@ -15,6 +15,10 @@ For details, see [here](m3/README.md). ### Local Demo +

+ +

+ #### Prerequisites 1. **Linux Operating System** diff --git a/m3/README.md b/m3/README.md index a109dcc..0d8450b 100644 --- a/m3/README.md +++ b/m3/README.md @@ -43,29 +43,37 @@ The resulting expert model output will be fed back to the VLM for generating the ## Performance ### VQA Benchmarks -| | Average | -|-------------------|---------| -| VILA-M3-3B | | -| Llama3-VILA-M3-8B | | -| VILA-M3-13B | | +| Model | Type | VQA-RAD* | SLAKE-VQA | Path-VQA | Average | +|---------------------------|----------------------|-----------|-----------|----------|----------| +| Llava-Med | Task-specific | *84.2* | *86.8* | *91.7* | *87.6* | +| Med-Gemini-1.5T | Generalist | 78.8 | **84.8** | 83.3 | 82.3 | +| Llama3-VILA-M3-3B | Generalist | 78.2 | 79.8 | 87.9 | 82.0 | +| Llama3-VILA-M3-8B | Generalist | **84.5** | 84.5 | 90.0 | **86.3** | +| Llama3-VILA-M3-13B | Generalist | 80.5 | 83.2 | **91.0** | 84.9 | ### Report Generation Benchmarks -| | Average | -|-------------------|---------| -| VILA-M3-3B | | -| Llama3-VILA-M3-8B | | -| VILA-M3-13B | | +| Model | Type | BLUE-4* | ROUGE* | +|---------------------------|----------------------|----------|----------| +| Llava-Med | Task-specific | *1.0* | *13.3* | +| Med-Gemini-1.5T | Generalist | 20.5 | 28.3 | +| Llama3-VILA-M3-3B | Generalist | 20.2 | 31.7 | +| Llama3-VILA-M3-8B | Generalist | 21.5 | **32.3** | +| Llama3-VILA-M3-13B | Generalist | **21.6** | 32.1 | ### Classification Benchmarks -| | Average | -|-------------------|---------| -| VILA-M3-3B | | -| Llama3-VILA-M3-8B | | -| VILA-M3-13B | | +| Expert info | w/o | w/o | with | with | +|---------------------------|--------------|------------|--------------|------------| +| Model | ChestX-ray14 | CheXpert | ChestX-ray14 | CheXpert | +| Med-Gemini-1.5T | 46.7 | 48.3 | - | - | +| TorchXRayVision | - | - | 50 | 51.5 | +| Llama3-VILA-M3-3B | 48.4 | 57.4 | **51.3** | 60.8 | +| Llama3-VILA-M3-8B | 45.9 | **61.4** | 50.7 | 60.4 | +| Llama3-VILA-M3-13B | **49.9** | 55.8 | 51.2 | **61.5** | ## Demo -An interactive demo is provided in ... +For and interactive demo, please access here. +The code to run the demo locally is described [here](../README.md#local-demo). ## Data preparation To prepare the datasets for training and evaluation, follow the instructions in [data_prepare](./data_prepare). @@ -73,6 +81,21 @@ To prepare the datasets for training and evaluation, follow the instructions in ## Training To replicate our fine-tuning procedure, utilize the provided scripts. +For our released checkpoints, we use a slurm cluster environment. +- VILA training code with Torch distributed +- 4 nodes with 8xA100 GPUs (80 GB each) +- Cosine learning rate decay with warmup + +

+ +

+ +| # Parameters | Training time | +|---------------------|----------------------| +| 3 billion | 5.5 hours | +| 8 billion | 11.0 hours | +| 13 billion | 19.5 hours | + ## Evaluation To evaluate a model on the above benchmarks, follow the instructions in [eval](./eval/README.md) diff --git a/m3/docs/images/gradio_app_ct.png b/m3/docs/images/gradio_app_ct.png new file mode 100644 index 0000000000000000000000000000000000000000..3a6128143b92fdf9c789cb4e9e82b2cbf3e10e4c GIT binary patch literal 243758 zcmeFZbx>U25;aN)PH=Y!7Cb<3w?J@r*Wm6JEFoBMcXxLUE`z%}3_4hF`zAN?yZ6<- zRj=y3KfkIOYU-RbXHW0Fd-dwoefTObD~^PKj{pGyf%HK_R1pFKDhC4Mr8^u9_{j9l z%`Er;vbl(eyrqb^h^>{agOZ(rk%_p8jfsP~k)pUT1O#VzxQfn4LKRH@l%^UwiqU}S z{QPL-C$`vHE5(Vx#l0t!8tY~H7^_;L$ibtPG;=D}t%qE58h%V6)Xu{7nDu9^PHHlX zlH*W@>{|J3sDL*&<9Fg&lQ{5)O{_I7F^!gOfaTlW)Ds`zsv~F>F|v_fD}I#{k{-XD zVP|Tyrhm7mhuf*sdmPDooPjPPjy8_!1;JKV$Kvq`Y2WJHRm1hO*16`p!rhH zq1T%x5KmaW)jFS`gzg96=L~P)dw8r;NjK5E>mbx{(@{>V$C9F0g7w7c-xP;E^(+O$ z0?Ds5pDr`iJUee1kz;3 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literal 0 HcmV?d00001 From 07c3ff6a4520135ff8530c544aa3db770d3f7eba Mon Sep 17 00:00:00 2001 From: Holger Roth Date: Wed, 23 Oct 2024 21:43:42 -0400 Subject: [PATCH 2/4] add GREEN scores --- m3/README.md | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/m3/README.md b/m3/README.md index 0d8450b..1165929 100644 --- a/m3/README.md +++ b/m3/README.md @@ -50,15 +50,17 @@ The resulting expert model output will be fed back to the VLM for generating the | Llama3-VILA-M3-3B | Generalist | 78.2 | 79.8 | 87.9 | 82.0 | | Llama3-VILA-M3-8B | Generalist | **84.5** | 84.5 | 90.0 | **86.3** | | Llama3-VILA-M3-13B | Generalist | 80.5 | 83.2 | **91.0** | 84.9 | +*Comparisons to Llava-Med & Med-Gemini are not direct as data splits are not available. ### Report Generation Benchmarks -| Model | Type | BLUE-4* | ROUGE* | -|---------------------------|----------------------|----------|----------| -| Llava-Med | Task-specific | *1.0* | *13.3* | -| Med-Gemini-1.5T | Generalist | 20.5 | 28.3 | -| Llama3-VILA-M3-3B | Generalist | 20.2 | 31.7 | -| Llama3-VILA-M3-8B | Generalist | 21.5 | **32.3** | -| Llama3-VILA-M3-13B | Generalist | **21.6** | 32.1 | +| Model | Type | BLUE-4* | ROUGE* | GREEN* | +|---------------------------|----------------------|----------|----------|----------| +| Llava-Med | Task-specific | *1.0* | *13.3* | - | +| Med-Gemini-1.5T | Generalist | 20.5 | 28.3 | - | +| Llama3-VILA-M3-3B | Generalist | 20.2 | 31.7 | 39.4 | +| Llama3-VILA-M3-8B | Generalist | 21.5 | **32.3** | 40.0 | +| Llama3-VILA-M3-13B | Generalist | **21.6** | 32.1 | 39.3 | +*Comparisons to Llava-Med & Med-Gemini are not direct as data splits are not available. ### Classification Benchmarks | Expert info | w/o | w/o | with | with | @@ -70,7 +72,6 @@ The resulting expert model output will be fed back to the VLM for generating the | Llama3-VILA-M3-8B | 45.9 | **61.4** | 50.7 | 60.4 | | Llama3-VILA-M3-13B | **49.9** | 55.8 | 51.2 | **61.5** | - ## Demo For and interactive demo, please access here. The code to run the demo locally is described [here](../README.md#local-demo). From c59b6ad3f3760004681914fd7933890270509e3e Mon Sep 17 00:00:00 2001 From: Holger Roth Date: Wed, 23 Oct 2024 21:49:55 -0400 Subject: [PATCH 3/4] reduce screenshot size --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5511699..ac5786f 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ For details, see [here](m3/README.md). ### Local Demo

    - +

    #### Prerequisites From 445cf797750737566cfb70bd355ac398922f6a21 Mon Sep 17 00:00:00 2001 From: Holger Roth Date: Wed, 23 Oct 2024 21:53:07 -0400 Subject: [PATCH 4/4] update readme --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index ac5786f..a8ae932 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,11 @@ The repository provides a collection of vision language models, benchmarks, and ## VILA-M3 **VILA-M3** is a *vision language model* designed specifically for medical applications. -It focuses on addressing the unique challenges faced by general-purpose vision-language models when applied to the medical domain. +It focuses on addressing the unique challenges faced by general-purpose vision-language models when applied to the medical domain and integrated with existing expert segmentation and classification models. + +

    + +

    For details, see [here](m3/README.md).