Fascinating. I wonder if there is any example for fine-tuning for segmentation involved. If so, the way we collate the data should be different. I have one question about the timeline at 15 minutes and 30 seconds. I noticed a part of the code that splits the data set into train and test. But after split it says `train_ds = split_ds["test"]` shouldn't be "train"?. I think that might be a mistake. What do you think? Very interesting content, especially if the model has the general knowledge to get into a game like your McDonald's example. This definitely has great applications in medical and education fields as well. Thank you for the content.
just look at the output from the model when you do segmentation and copy that. Yes you will need to to update the collate function. The "test" part is correct because it is just setting it to train on a very small number of examples, in a real training yes use the 'train' with is 95% of the data as opposed to 5% on the test.
@@samwitteveenai Thx, I get it now, the "test" is just for the demo in this colab. Although It would've been clearer if they used a subset of like 100 rows from the train split. I experimented a bit, the model is super friendly to fine-tuning. Whatever they did, it made this model really easy to tune. We're in a time where "tune-friendly" actually makes sense.
honestly its a while since I played with LLaVA and mostly I have just used it on Ollama, so not sure how it compares. Phi3-Vision is also worth checking out. I may make a video on that as well
Do you know how good the dataset should be in terms of completeness for fine tuning? I have lots of images-texts of clothes, but in some there are more details than others, so I guess during training the model will be confused. Ex. There are thousands of images of dresses with only the color, and thousands of images with color + other details
Do you know if they are going to release a model for real time video sentiment analysis? I thought there was a demo of that by either Google or OpenAI?
