Update README.md

README.md

@@ -10,6 +10,16 @@ pipeline_tag: visual-question-answering
 
 Pretrain stage only, 4630 epochs
 
+# Introduction
+
+We use the powerful [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) to create a super small image-text-to-text model.
+
+The goal is to make it possible to run LLaVA models on edge devices (with few gigabytes of memory).
+
+For LLM and vision tower, we choose [OpenELM-270M-Instruct](apple/OpenELM-270M-Instruct) and [facebook/dinov2-small](facebook/dinov2-small), respectively.
+
+[POPE](https://tinyllava-factory.readthedocs.io/en/latest/Evaluation.html#pope):
+
 | Category    | # Samples | TP | FP | TN | FN | Accuracy | Precision | Recall | F1 Score | Yes Ratio |
 |-----------------|---------------|--------|--------|--------|--------|--------------|---------------|------------|--------------|---------------|
 | Adversarial     | 3000          | 1312   | 1250   | 250    | 188    | 0.521        | 0.512        | 0.875      | 0.646        | 0.854         |
@@ -17,6 +27,14 @@ Pretrain stage only, 4630 epochs
 | Random          | 2910          | 1312   | 1185   | 225    | 188    | 0.528        | 0.525        | 0.875      | 0.656        | 0.858         |
 
 
+[TEXTVQA](https://tinyllava-factory.readthedocs.io/en/latest/Evaluation.html#textvqa)
+
+Samples 5000, Accuracy 0% (:-|)
+
+[SCIENCEQA](https://tinyllava-factory.readthedocs.io/en/latest/Evaluation.html#scienceqa)
+
+Samples 4241, Correct: -, Accuracy: -%, IMG-Accuracy: -%
+
 [MMMU](https://tinyllava-factory.readthedocs.io/en/latest/Evaluation.html#mmmu)
 
 | Category                        | # Samples | Accuracy |