where is the yaml config file? about oscar HOT 16 CLOSED

Yes I have managed to train and evaluate the model on my own custom dataset, I don't have test set in my dataset, I have only train and val set. so the test.yaml file points to the val set.

img: val.img.tsv
hw: val.hw.tsv
label: val.label.tsv
feature: val.feature.tsv
caption: val_caption.json

this is my test.yaml and val.yaml fil content.
But first you need to create the .tsv and .json files. If you need help in this regard I can point you the issues which helped me to prepare those files

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We have saved all running scripts to: https://github.com/microsoft/Oscar/blob/master/MODEL_ZOO.md

Is this what you are looking for?

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the train_yaml is from our dataset, you can download it: https://github.com/microsoft/Oscar/blob/master/DOWNLOAD.md

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To get the image captioning dataset, run this:
wget https://biglmdiag.blob.core.windows.net/oscar/datasets/coco_caption.zip

Then train.yaml is a yaml file specifies the data like features and labels used in training. It comes with the dataset.

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MIN_BOXES=0, MAX_BOXES=100
conf_thresh = 0.2 or 0.4, I forget the exact value, great probability it is 0.2

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The run_captioning.py file has the train_yaml config but I have not found this.

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The file for generating the downloaded files?

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Recently, I tried to generate the COCO caption feature files according to your format. The model you used is the bottom-up-attention,right? https://github.com/peteanderson80/bottom-up-attention
If so, there exists some hyperparameters that I can not understand. I found most of the images in you feature tsv contain 20~30 bounding boxes. So if convenient,could you share the MIN_BOXES , MAX_BOXES as well as the conf_thresh hyperparameter?

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The bottom up model is trained in Visual Genome dataset,right?

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Yes

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I want to test the model with my own images and now I generated the features and labels. however when I run the script oscar/run_captioning.py It needs test.yaml. Do I have to download the whole training data to test the model on my own images?
This is what I run on my terminal:
python oscar/run_captioning.py
--do_test --do_eval --test_yaml test.yaml
--per_gpu_eval_batch_size 64 --num_beams 5
--max_gen_length 20 --eval_model_dir oscar/checkpoint-29-66420

from oscar.

I want to test the model with my own images and now I generated the features and labels. however when I run the script oscar/run_captioning.py It needs test.yaml. Do I have to download the whole training data to test the model on my own images?
This is what I run on my terminal:
python oscar/run_captioning.py
--do_test --do_eval --test_yaml test.yaml
--per_gpu_eval_batch_size 64 --num_beams 5
--max_gen_length 20 --eval_model_dir oscar/checkpoint-29-66420

@DesaleF Were you able to figure out the test.yaml file to test the model on your own images? I'm running into the same issue with not knowing how create test.yaml

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Can some one tell me how to get only a part of the dataset for training ? 22GB to huge. Also, how to do inference on a downloaded pretrained model ? how to get the test.yaml file ?

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DesaleF

Hey it would be really nice if you could share those links or an example of what you created. I need to do the same :/

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Can some one tell me how to get only a part of the dataset for training ? 22GB to huge. Also, how to do inference on a downloaded pretrained model ? how to get the test.yaml file ?

Also just to confirm, do we need the whole download to run inference? I am not sure if the tokenizer is already in the repo or if its in that massive download.

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Yes I have managed to train and evaluate the model on my own custom dataset, I don't have test set in my dataset, I have only train and val set. so the test.yaml file points to the val set.

img: val.img.tsv
hw: val.hw.tsv
label: val.label.tsv
feature: val.feature.tsv
caption: val_caption.json

this is my test.yaml and val.yaml fil content. But first you need to create the .tsv and .json files. If you need help in this regard I can point you the issues which helped me to prepare those files

How to get label.tsv? Thanks!

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