ConvLLaVA/ConvLLaVA-pretrain-1024
Text Generation
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The dataset generation failed
Error code: DatasetGenerationError
Exception: UnicodeDecodeError
Message: 'utf-8' codec can't decode byte 0x89 in position 0: invalid start byte
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 122, in _generate_tables
pa_table = paj.read_json(
File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1995, in _prepare_split_single
for _, table in generator:
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 797, in wrapped
for item in generator(*args, **kwargs):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 145, in _generate_tables
dataset = json.load(f)
File "/usr/local/lib/python3.9/json/__init__.py", line 293, in load
return loads(fp.read(),
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1104, in read_with_retries
out = read(*args, **kwargs)
File "/usr/local/lib/python3.9/codecs.py", line 322, in decode
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x89 in position 0: invalid start byte
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1524, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1099, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
id
string | conversations
list | task_name
string | image
string |
|---|---|---|---|
multiinstruct_VQA_counting_10386713 | [
{
"from": "human",
"value": "<image>\nThis task tests your ability to count number of objects. Here is the question \"How many chairs are in the photo?\"."
},
{
"from": "gpt",
"value": "two"
}
] | VQA_counting | VQA_counting_COCO_train2014_000000076081.jpg |
vision-flan_coco+image_classification_animal+20073 | [
{
"from": "human",
"value": "<image>\nGiven an image of an animal, identify the kind of animal in the image. The picture could be of more popular animals that are visible around zoos or are sometimes domesticated at home. They could also sometimes be found in the wild.\nOptions: (a) This image contains a zebra (b) This image contains a cow (c) This image contains a horse (d) This image contains an elephant (e) This image contains a giraffe (f) This image contains a sheep (g) This image contains a cat (h) This image contains a bear (i) This image contains a bird (j) This image contains a dog"
},
{
"from": "gpt",
"value": "(j) This image contains a dog"
}
] | coco+image_classification_animal | coco+image_classification_animal_610_000000195449.jpg |
vision-flan_MVTecAD+anomaly_detection+005 | [
{
"from": "human",
"value": "<image>\nThe primary objective of this task is to accurately identify the type and cause of anomalies in the object present in the provided image. The image depicts a specific category of object and texture, and within this category, there are defect-free images as well as images exhibiting different types of defects. Your task is to carefully examine the image and meticulously identify the specific type and cause of any deviations from the normal appearance of the object or texture. Pay close attention to irregularities in lines, shading, color scheme, and level of detail. Additionally, analyze the unique characteristics of the category, including shape, color, and texture. Your focus should be on precisely identifying the particular type and cause of the anomaly. The potential anomalies to consider encompass a wide range, such as gray strokes, bent objects, holes, missing wires, and more."
},
{
"from": "gpt",
"value": "The anomaly is faulty imprint."
}
] | MVTecAD+anomaly_detection | MVTecAD+anomaly_detection_923_005.png |
vision-flan_PlotQA+visual_question_answering+3554+S6 | [
{
"from": "human",
"value": "You are provided with a chart image and a question related to the chart. Answer the question based on the information given in the chart.\n\nHow many dotlines are there ?\n<image>"
},
{
"from": "gpt",
"value": "2"
}
] | PlotQA+visual_question_answering | PlotQA+visual_question_answering_963_3554.png |
multiinstruct_VQA_object_presence_10347599 | [
{
"from": "human",
"value": "<image>\nThis task asks you to identify if an object appears in the image. Is there a banana in the picture?"
},
{
"from": "gpt",
"value": "no"
}
] | VQA_object_presence | VQA_object_presence_COCO_train2014_000000295294.jpg |
vision-flan_KVQA+image_question_answer+14341.jpg+1 | [
{
"from": "human",
"value": "<image>\nYou are provided with a picture and a question related to the picture. Your job is to correctly answer the question. Note that any references to directions (left, right, center, etc.) in the questions are from the perspective of the person depicted in the image.\n\nWho is the person in the image?"
},
{
"from": "gpt",
"value": "Thomas Danneberg"
}
] | KVQA+image_question_answer | KVQA+image_question_answer_309_14341.jpg |
multiinstruct_VQA_scene_recognition_10498944 | [
{
"from": "human",
"value": "<image>\nQuestion: Is the picture taken outdoors?\nPlease answer the question by analyzing the scene in the provided image."
},
{
"from": "gpt",
"value": "yes"
}
] | VQA_scene_recognition | VQA_scene_recognition_COCO_train2014_000000100207.jpg |
vision-flan_semart+image_timeframe+13443 | [
{
"from": "human",
"value": "Here is a picture of some fine art created in the past, I would like to know roughly what period in time it could have been made in\n<image>"
},
{
"from": "gpt",
"value": "Roughly within the time 1451-1500"
}
] | semart+image_timeframe | semart+image_timeframe_655_44686-st_paul.jpg |
multiinstruct_VQA_scene_recognition_10000640 | [
{
"from": "human",
"value": "Given a picture of certain environment, answer the following question.\nIs the picture taken indoor?\n<image>"
},
{
"from": "gpt",
"value": "yes"
}
] | VQA_scene_recognition | VQA_scene_recognition_COCO_train2014_000000524406.jpg |
vision-flan_VQA-E+visual_question_answering+119456+37237 | [
{
"from": "human",
"value": "You are provided with an image and a question related to the image. Answer the question based on the information given in the image.\n\nIs bigfoot in the picture?\n<image>"
},
{
"from": "gpt",
"value": "no"
}
] | VQA-E+visual_question_answering | VQA-E+visual_question_answering_574_COCO_train2014_000000119456.jpg |
vision-flan_PACS+cartoon_object_classification++pic_085 | [
{
"from": "human",
"value": "You will be given an image of a cartoon. Identify the main object in the image.\n<image>"
},
{
"from": "gpt",
"value": "giraffe"
}
] | PACS+cartoon_object_classification | PACS+cartoon_object_classification_207_pic_085.jpg |
vision-flan_SKETCH+object_detection+eyeglasses+6210.png | [
{
"from": "human",
"value": "<image>\nEach image is a human drawn sketch of a object. Identify the main object in the image."
},
{
"from": "gpt",
"value": "eyeglasses"
}
] | SKETCH+object_detection | SKETCH+object_detection_165_6210.png |
vision-flan_RAVEN+next_pattern+image_in_center_single_out_center_single_1409.png | [
{
"from": "human",
"value": "Each image has 8 images labeled as Image 1 to Image 8. These 8 images follow a specific pattern. Detect the pattern and select the next image in the sequence from the 8 available options.\n<image>"
},
{
"from": "gpt",
"value": "Option 2"
}
] | RAVEN+next_pattern | RAVEN+next_pattern_948_image_in_center_single_out_center_single_1409.png |
vision-flan_ImageNet-R+image_domain_classification+sketch_21 | [
{
"from": "human",
"value": "Your goal is to classify the image based on its domain, which can be 'videogame', 'painting', 'sketch', 'cartoon', 'art', 'toy', 'deviantart', 'graphic', 'sculpture', 'misc', 'embroidery', 'sticker', 'graffiti', 'origami', or 'tattoo'. Your final output should specify the identified domain of the image.\n<image>"
},
{
"from": "gpt",
"value": "sketch"
}
] | ImageNet-R+image_domain_classification | ImageNet-R+image_domain_classification_861_sketch_21.jpg |
multiinstruct_VQA_positional_reasoning_30115988 | [
{
"from": "human",
"value": "<image>\nIn this task, you need to analyze the position of objects in an image and answer the following question. What vehicle is behind the fence?"
},
{
"from": "gpt",
"value": "van"
}
] | VQA_positional_reasoning | VQA_positional_reasoning_COCO_train2014_000000493341.jpg |
vision-flan_semart+image_school+1298 | [
{
"from": "human",
"value": "I would like to know what school of art this painting could be from. Some possible schools of art are Italian, German, or Spanish among others\n<image>"
},
{
"from": "gpt",
"value": "This painting comes from the school of French art "
}
] | semart+image_school | semart+image_school_323_29414-greville.jpg |
vision-flan_visdial+answer_question_6+17483 | [
{
"from": "human",
"value": "Here is a conversation between 2 people. They are talking about a picture and discussing some questions and answers about it. Read the conversation and then answer as the next person.\n\nPerson1: how many people?\nPerson2: there are too many to count\nPerson1: are they on a field?\nPerson2: i believe so\nPerson1: what color is the kite?\nPerson2: seems to have a multi color head and the rest of it is green and white it is very long\nPerson1: any other kites?\nPerson2: no just the 1\nPerson1: any grass?\nPerson2: can't really tell seems more like trees and mountains\nPerson1: are the mountains large or small?\nPerson2: small\nQuestion: is it day or night?\n<image>"
},
{
"from": "gpt",
"value": "Person2: day"
}
] | visdial+answer_question_6 | visdial+answer_question_6_92_COCO_train2014_000000519844.jpg |
vision-flan_semart+image_description+20061 | [
{
"from": "human",
"value": "Here is a picture of fine art, can you please give me a long description describing all of its details?\n<image>"
},
{
"from": "gpt",
"value": "Here is a description: In this painting the approach is unusually stark for the Le Nain brothers. Instead of a landscape background, there is a two-storey house belonging to the peasants, whose relative prosperity is indicated by the glass in the windows (all over Europe at this time many of the poorer classes lived in conditions far more primitive than those recorded by the Le Nain brothers).This unassuming picture is one of the most perceptive paintings to be produced in the 1640s. As in the Forge, the treatment of the low-life subject is given a totally unexpected dignity. The boy on the right and the old man next to him stare through us into space, and together they counterbalance the large area of pale stone of the house behind them. Into their expressions the artist have distilled a timelessness as far removed from anecdote as possible. Whereas in Georges de La Tour this timelessness is easier to understand because of the spiritual content of his subjects, in the depiction of a peasant's face it is rare for the artist not to be interested in telling a story, but simply to be observing what he sees. This approach, which was to preoccupy many of the most important painters of the nineteenth century, from Courbet to the Impressionists, was an anachronism in the seventeenth century and the reason why the Le Nain brothers were so untypical of artists of their time"
}
] | semart+image_description | semart+image_description_124_21709-cottaged.jpg |
vision-flan_VisDA-2017+object_classification_train_skateboard_4571 | [
{
"from": "human",
"value": "<image>\nYou are given an image which contains a 3D rendered object. Your goal is to identify the category of the object present in the image from the given options.\nOptions: (a) aeroplane (b) truck (c) horse (d) car (e) person (f) train (g) skateboard (h) motorcycle (i) bus (j) bicycle"
},
{
"from": "gpt",
"value": "(g) skateboard"
}
] | VisDA-2017+object_classification_train | VisDA-2017+object_classification_train_622_src_1_04225987_d041334fe322ed211082f2ea630bf69e__129_236_165.png |
vision-flan_visdial+answer_question_2+66046 | [
{
"from": "human",
"value": "Here is a short conversation between a human and an AI assistant. They are talking about a particular picture. They are discussing some questions and answers about it. Pay attention to the conversation and then answer the question at the end.\n\nHuman: is there only 1 elephant?\nAssistant: close up of 1 but maybe another 1 behind it\nHuman: is the elephant in the wild?\nAssistant: unable to tell\nQuestion: is there grass on ground?\n<image>"
},
{
"from": "gpt",
"value": "Assistant: can't see the ground"
}
] | visdial+answer_question_2 | visdial+answer_question_2_478_COCO_train2014_000000164565.jpg |
vision-flan_ObjectNet+Object_classfication+96831ef67e2d4ff | [
{
"from": "human",
"value": "<image>\nYour task is to recognize the object depicted in the given image. The object can be any item commonly used in our everyday lives, such as kitchen tools, food items, stationery, clothing, and more. To correctly identify the object, carefully observe its color, shape, and size characteristics."
},
{
"from": "gpt",
"value": "Plastic wrap"
}
] | ObjectNet+Object_classfication | ObjectNet+Object_classfication_854_96831ef67e2d4ff.png |
vision-flan_FGVC_Aircraft+Aircraft_Classification+1373008 | [
{
"from": "human",
"value": "<image>\nYour objective is to analyze an aircraft image and provide the manufacturer, family, and variant of the aircraft in the specified order: manufacturer; family; variant (e.g., Airbus; A300; A300B4).\nManufacturer: The manufacturer refers to the company that designs, builds, and assembles the aircraft, possessing the expertise and experience in the aviation industry necessary for production and delivery.\nFamily: A family represents a collection of aircraft models produced by the same manufacturer, sharing common characteristics, design principles, or technological platforms.\nVariant: A variant indicates a variation of a particular aircraft model, often incorporating specific modifications, improvements, or customizations compared to the base model."
},
{
"from": "gpt",
"value": "Panavia; Tornado; Tornado"
}
] | FGVC_Aircraft+Aircraft_Classification | FGVC_Aircraft+Aircraft_Classification_349_1373008.jpg |
vision-flan_A-OKVQA+answer_rationales_matching+000000070048 | [
{
"from": "human",
"value": "<image>\nAnswer the question and provide an explanation.\n\nThe question is: Where does this person appear to be dining?"
},
{
"from": "gpt",
"value": "The answer is plane because The person is eating from a plane tray."
}
] | A-OKVQA+answer_rationales_matching | A-OKVQA+answer_rationales_matching_637_000000070048.jpg |
vision-flan_Yoga-82+yoga_pose_recognition+IMG_4497-1 | [
{
"from": "human",
"value": "What is the name of the yoga pose?\n<image>"
},
{
"from": "gpt",
"value": "The yoga pose is Cat Cow Pose or Marjaryasana."
}
] | Yoga-82+yoga_pose_recognition | Yoga-82+yoga_pose_recognition_200_IMG_4497-1.jpg |
multiinstruct_GQA_04149840_2360989 | [
{
"from": "human",
"value": "<image>\nOn which side is the toilet, the left or the right?"
},
{
"from": "gpt",
"value": "right"
}
] | GQA | GQA_2360989.jpg |
vision-flan_VIQUAE+question_answer+512px-Dardanelles1_wza.jpg+821480 | [
{
"from": "human",
"value": "With the help of this image, can you answer the question given in the input text?\n\nWho nightly swam this strait to meet his lover?\n<image>"
},
{
"from": "gpt",
"value": "Leander (disambiguation)"
}
] | VIQUAE+question_answer | VIQUAE+question_answer_136_512px-Dardanelles1_wza.jpg |
multiinstruct_VQA_color_20050186 | [
{
"from": "human",
"value": "<image>\nQuestion: What color is the woman's shirt?\n\nAnswer the above question by first finding the object in the image and identify its color."
},
{
"from": "gpt",
"value": "pink"
}
] | VQA_color | VQA_color_COCO_train2014_000000030100.jpg |
vision-flan_MemeCap+meme_captioning+yizd7j_1 | [
{
"from": "human",
"value": "Generate a caption for the given meme.\n\nTitle: Credit to u/Luke5353 for the lower image\n<image>"
},
{
"from": "gpt",
"value": "Meme poster is trying to convey that he will simp for a certain type of woman."
}
] | MemeCap+meme_captioning | MemeCap+meme_captioning_621_memes_yizd7j.png |
vision-flan_cinic-10+object_presence_animal+61398 | [
{
"from": "human",
"value": "<image>\nThe given image can contain some animals; they can be animals typically found in the wild or domesticated animals. The picture could also contain something that does not fit this description. Your job is to identify if the subject of the image is an animal or not."
},
{
"from": "gpt",
"value": "The object is an animal"
}
] | cinic-10+object_presence_animal | cinic-10+object_presence_animal_471_cifar10-train-3554.png |
vision-flan_VQA-E+image_captioning+464166+144456 | [
{
"from": "human",
"value": "<image>\nYou are provided with an image. Explain what is going on in the image."
},
{
"from": "gpt",
"value": "A black and white picture of women sitting on a bench eating while cats watch. "
}
] | VQA-E+image_captioning | VQA-E+image_captioning_974_COCO_train2014_000000464166.jpg |
vision-flan_coco+image_classification_furniture+16793 | [
{
"from": "human",
"value": "<image>\nGiven an image of a piece of furniture in a house, identify the type of furniture. It is usually used to make the house look better and can be made of different kinds of material.\nOptions: (a) This image contains a potted plant (b) This image contains a couch (c) This image contains a toilet (d) This image contains a dining table (e) This image contains a bed (f) This image contains a chair"
},
{
"from": "gpt",
"value": "(f) This image contains a chair"
}
] | coco+image_classification_furniture | coco+image_classification_furniture_953_000000401004.jpg |
vision-flan_CLEVR_CoGenT+Question_Answering+CLEVR_trainA_034794.png | [
{
"from": "human",
"value": "The input for this task is an image of 3D-rendered objects and a question that fall into different categories. The questions fall into five classes of tasks: Exist, Count, Compare Integer, Query Attribute, and Compare Attribute. The task here is to answer the question.\n\nThe metallic object that is both in front of the tiny metal cylinder and to the left of the yellow block has what shape?\n<image>"
},
{
"from": "gpt",
"value": "cylinder"
}
] | CLEVR_CoGenT+Question_Answering | CLEVR_CoGenT+Question_Answering_528_CLEVR_trainA_034794.png |
vision-flan_NOCAPS+image_caption+4fe7e6a68490affb.jpg | [
{
"from": "human",
"value": "Give me a list of captions for this image.\n<image>"
},
{
"from": "gpt",
"value": "Caption 1: A man feeding a mule with goats and mini horses surrounding the mule. Caption 2: A man in a hat is playing with a mule's muzzle. Caption 3: A man is hand feeding a mule with smaller animals eating nearby. Caption 4: An elderly man feeding the brown mule outside. Caption 5: A man hand feeding a donkey on a farm. Caption 6: Caring for the farm animals, the man allows the mule to have a sniff. Caption 7: A man in a hat standing in front of a donkey behind a fence. Caption 8: A man is feeding a piece of bread to the mule. Caption 9: An older man in a baseball hat is feeding a donkey a slice of bread. Caption 10: An old man feeding a donkey or mule. "
}
] | NOCAPS+image_caption | NOCAPS+image_caption_856_4fe7e6a68490affb.jpg |
vision-flan_VisDA-2017+object_classification_validation_car_6805 | [
{
"from": "human",
"value": "You are given an image which contains an object. Your goal is to identify the category of the object present in the image from the given options.\nOptions: (a) train (b) knife (c) bus (d) skateboard (e) truck (f) bicycle (g) person (h) motorcycle (i) car (j) horse\n<image>"
},
{
"from": "gpt",
"value": "(i) car"
}
] | VisDA-2017+object_classification_validation | VisDA-2017+object_classification_validation_96_car_345211.jpg |
vision-flan_ImageNet-C+image_classification_general+1891075 | [
{
"from": "human",
"value": "<image>\nIn this task, identify the type of corruption given a corrupted image. It could be digitally altered, contain natural distortions or contain other corruptions\nOptions: (a) The corruption type is digital (b) The corruption type is weather (c) The corruption type is blur (d) The corruption type is noise"
},
{
"from": "gpt",
"value": "(a) The corruption type is digital"
}
] | ImageNet-C+image_classification_general | ImageNet-C+image_classification_general_653_ILSVRC2012_val_00008246.JPEG |
vision-flan_Dark-Zurich+time_classification+GOPR0376_frame_000195_rgb_anon | [
{
"from": "human",
"value": "<image>\nIdentify the time of the day when the image is captured. Options are: daytime, nighttime, twilight."
},
{
"from": "gpt",
"value": "The time of the day is nighttime."
}
] | Dark-Zurich+time_classification | Dark-Zurich+time_classification_123_GOPR0376_frame_000195_rgb_anon.png |
vision-flan_Clevr+Question_Answering+CLEVR_train_037260.png | [
{
"from": "human",
"value": "The input for this task is an image of 3D-rendered objects and a question that fall into different categories. The questions fall into five classes of tasks: Exist, Count, Compare Integer, Query Attribute, and Compare Attribute. The task here is to answer the question.\n\nWhat color is the metal thing?\n<image>"
},
{
"from": "gpt",
"value": "gray"
}
] | Clevr+Question_Answering | Clevr+Question_Answering_889_CLEVR_train_037260.png |
vision-flan_RAVEN+next_pattern+image_left_center_single_right_center_single_474.png | [
{
"from": "human",
"value": "Each image has 8 images labeled as Image 1 to Image 8. These 8 images follow a specific pattern. Detect the pattern and select the next image in the sequence from the 8 available options.\n<image>"
},
{
"from": "gpt",
"value": "Option 8"
}
] | RAVEN+next_pattern | RAVEN+next_pattern_394_image_left_center_single_right_center_single_474.png |
vision-flan_CLEVR_CoGenT+VQA_context+CLEVR_trainA_062532.png | [
{
"from": "human",
"value": "<image>\nYou are given some question and answer pairs as context, and you will answer the question at the end based on the image.\n\nThere is a ball that is to the left of the large brown metal ball; what material is it? metal\nIs there any other thing that is the same color as the small shiny ball? yes\nWhat number of big objects are either green metallic objects or purple things? 1\nAre there any purple shiny objects in front of the big brown shiny ball? no\nHow many other objects are the same size as the cyan matte thing? 5\nIs the number of large red metal things less than the number of small yellow metallic blocks? no\nWhat is the material of the brown sphere that is in front of the large object to the left of the big brown metal object? metal\nHow many objects are large balls that are in front of the small metallic ball or brown matte things? 3\n\nDoes the purple thing in front of the brown metallic ball have the same shape as the object that is behind the large red metal thing?"
},
{
"from": "gpt",
"value": "no"
}
] | CLEVR_CoGenT+VQA_context | CLEVR_CoGenT+VQA_context_420_CLEVR_trainA_062532.png |
vision-flan_fairface+image_classification_gender+1048 | [
{
"from": "human",
"value": "<image>\nHere is a picture of a person. Based only upon this picture, what would you guess this person's gender is?"
},
{
"from": "gpt",
"value": "The person's gender is Male"
}
] | fairface+image_classification_gender | fairface+image_classification_gender_620_1048.jpg |
vision-flan_infographicvqa+question_answer+10356 | [
{
"from": "human",
"value": "Here is an infographic representing some data in a visual form. Please answer my question using the data in the image\n\nWhich university has the highest application acceptance rate in the U.S.?\n<image>"
},
{
"from": "gpt",
"value": "University of California, Berkeley (UCB)"
}
] | infographicvqa+question_answer | infographicvqa+question_answer_355_31703.jpeg |
multiinstruct_visualgenome_VG_2324670_970254 | [
{
"from": "human",
"value": "<image>\nWhere was this picture taken?"
},
{
"from": "gpt",
"value": "The bathroom"
}
] | visualgenome_vqa | visualgenome_vqa_2324670.jpg |
multiinstruct_wikihow_wikihow_text_image_step_order_835976_check-a-microwave-for-leaks | [
{
"from": "human",
"value": "<image>\nThe overall goal is to Detecting Leaks Directly. You are at \"Darken the room.\" step. Is the image the next or the previous step?\n\nOptions: next previous"
},
{
"from": "gpt",
"value": "next"
}
] | wikihow_text_image_step_order | wikihow_text_image_step_order_method_0_step_2.jpg |
vision-flan_iconqa+choose_txt+21845 | [
{
"from": "human",
"value": "<image>\nI have a question about what is happening in this picture, can you please give me an answer?\n\nThe first picture is a train. Which picture is second?"
},
{
"from": "gpt",
"value": "The answer is plane"
}
] | iconqa+choose_txt | iconqa+choose_txt_881_image.png |
vision-flan_NOCAPS+image_caption+8234903a8adf6d0a.jpg | [
{
"from": "human",
"value": "Give me a list of captions for this image.\n<image>"
},
{
"from": "gpt",
"value": "Caption 1: A very large white aircraft surrounded by snow. Caption 2: The passenger runway is attached to the side of the plane. Caption 3: The airplane has the passenger tunnels attached to its doors. Caption 4: an airplane on an icy field with a bridge attached to it. Caption 5: A commercial jet is mounted to the platform at an airport. Caption 6: A large aircraft is pulled up to the tarmac. Caption 7: a plane with the ramp attached to it in a snowy airfield. Caption 8: An airplane is parked at the gate on snowy airfield. Caption 9: A airplane is flying in the sky above a building. Caption 10: A plane is parked in the airport, two movables jet bridge is on the plane. "
}
] | NOCAPS+image_caption | NOCAPS+image_caption_174_8234903a8adf6d0a.jpg |
vision-flan_Clevr+Question_Answer_Matching+CLEVR_train_001720.png | [
{
"from": "human",
"value": "You will be given an Image of 3D-rendered objects, a number of Questions and same number of Answers. The task here is to match the questions to the right answers according to the image you see. The format of the output shoud be something like: Q1A3,Q2A5,Q3A2,Q4A1,Q5A1\n\nQ1: What is the size of the object that is the same color as the metallic sphere?\nQ2: There is a cylinder that is the same material as the tiny ball; what is its size?\nQ3: Are there any other things that are the same material as the block?\nQ4: What is the shape of the tiny metallic thing that is the same color as the tiny matte thing?\nQ5: There is a object that is both behind the large thing and in front of the blue ball; what shape is it?\nQ6: There is a block that is the same size as the blue metallic thing; what is its color?\nQ7: How many other gray objects are made of the same material as the large object?\nQ8: Is there a tiny matte cube that has the same color as the tiny ball?\nQ9: Are the blue thing that is to the left of the big gray metal cylinder and the gray thing made of the same material?\nA1: blue\nA2: small\nA3: large\nA4: 0\nA5: yes\nA6: cube\nA7: sphere\nA8: no\n\n<image>"
},
{
"from": "gpt",
"value": "Q1A2\nQ2A3\nQ3A8\nQ4A7\nQ5A6\nQ6A1\nQ7A4\nQ8A5\nQ9A5\n"
}
] | Clevr+Question_Answer_Matching | Clevr+Question_Answer_Matching_258_CLEVR_train_001720.png |
vision-flan_PACS+horse_image_category_classification+pic_051.jpg | [
{
"from": "human",
"value": "You will be given an image of a horse. The image could be of different categories like painting, cartoon, photograph, or sketch. Identify the image category.\nOptions: (a) Sketch (b) Photograph (c) Art painting (d) Cartoon\n<image>"
},
{
"from": "gpt",
"value": "(c) Art painting"
}
] | PACS+horse_image_category_classification | PACS+horse_image_category_classification_89_pic_051.jpg |
vision-flan_CONCADIA+image_description+100438.jpg | [
{
"from": "human",
"value": "Can you describe the visual appearance of this image?\n<image>"
},
{
"from": "gpt",
"value": "Kishore Lulla stands facing towards the camera"
}
] | CONCADIA+image_description | CONCADIA+image_description_321_100438.jpg |
vision-flan_FGVC_Aircraft+Aircraft_Classification_Family+1371730 | [
{
"from": "human",
"value": "From the image provided to you, guess the family of the aircraft.\nHint: Family: A family represents a collection of aircraft models produced by the same manufacturer, sharing common characteristics, design principles, or technological platforms.\n<image>"
},
{
"from": "gpt",
"value": "DHC-1"
}
] | FGVC_Aircraft+Aircraft_Classification_Family | FGVC_Aircraft+Aircraft_Classification_Family_950_1371730.jpg |
multiinstruct_VQA_positional_reasoning_10225909 | [
{
"from": "human",
"value": "<image>\nQuestion: What is to the right of person?\n\n Please answer the question by reasoning about the positions of objects."
},
{
"from": "gpt",
"value": "banana"
}
] | VQA_positional_reasoning | VQA_positional_reasoning_COCO_train2014_000000509700.jpg |
vision-flan_Cars+car_classification+03393 | [
{
"from": "human",
"value": "In this task, based on the given image dataset of different cars, you have to identify the model + car make + Year of Make of a car in the image among a total of 196 categories such as Audi A5 Coupe 2012, BMW 3 Series Sedan 2012, Bentley Arnage Sedan 2009,... Pay attention to details such as the size, logo, type of the car to identify the model. So by looking at a car image, Give your answer in the following format: Model of the Car++Make of the Car++Year of Make\n<image>"
},
{
"from": "gpt",
"value": "AM General Hummer SUV 2000"
}
] | Cars+car_classification | Cars+car_classification_784_03393.jpg |
vision-flan_PACS+photo_object_classification++n02374451_9892 | [
{
"from": "human",
"value": "<image>\nThe input is a photograph of an object. Identify the main object in the image."
},
{
"from": "gpt",
"value": "horse"
}
] | PACS+photo_object_classification | PACS+photo_object_classification_698_n02374451_9892.jpg |
vision-flan_MemeCap+image_captioning+z72yp6_1 | [
{
"from": "human",
"value": "You are given a meme and your goal is to generate a caption that describes the image. Ignore the text in the meme.\n\nTitle: Me and my friend had a little discussion, This might have an interesting outcome\n<image>"
},
{
"from": "gpt",
"value": "two faces of Sean. one is wearing glasses and crying with red eyes and the other face has blonde hair blonde beard?"
}
] | MemeCap+image_captioning | MemeCap+image_captioning_801_memes_z72yp6.png |
vision-flan_visdial+answer_question_6+15047 | [
{
"from": "human",
"value": "Here is a conversation between 2 people. They are talking about a picture and discussing some questions and answers about it. Read the conversation and then answer as the next person.\n\nPerson1: are they on a large body of water?\nPerson2: yes\nPerson1: are there lots of waves?\nPerson2: no\nPerson1: are there other people in the water?\nPerson2: yes 2 in a paddle boat\nPerson1: are they wearing life jackets?\nPerson2: the boy is\nPerson1: is the woman wearing a bathing suit?\nPerson2: no shorts\nPerson1: is it sunny outside?\nPerson2: yes\nQuestion: are there mountains near by?\n<image>"
},
{
"from": "gpt",
"value": "Person2: no"
}
] | visdial+answer_question_6 | visdial+answer_question_6_964_COCO_train2014_000000473052.jpg |
vision-flan_CLEVR_CoGenT+Question_Answer_Matching+CLEVR_trainA_002897.png | [
{
"from": "human",
"value": "<image>\nIn this task, you will be presented with an image containing 3D-rendered objects along with a set of questions and corresponding answers. Your goal is to correctly match each question with its corresponding answer based on the visual content of the image. The output format should follow this pattern: Q1A3, Q2A5, Q3A2, Q4A1, Q5A1, indicating the question number followed by the corresponding answer number.\n\nQ1: How many tiny red shiny balls are on the right side of the metal ball that is behind the tiny brown ball?\nQ2: Are there fewer cyan matte blocks than brown rubber objects?\nQ3: What number of rubber things are large things or red spheres?\nQ4: Is the number of things left of the tiny red sphere less than the number of blue things?\nQ5: Are there any other things that have the same size as the blue rubber cube?\nQ6: There is a red thing on the right side of the small red metal object to the right of the large blue object; what shape is it?\nQ7: How many small cylinders are on the left side of the big matte object to the left of the tiny cyan rubber cylinder that is behind the blue matte thing?\nQ8: Is there any other thing that is the same shape as the green thing?\nQ9: What number of things are either matte things or tiny cyan rubber objects behind the small green matte cylinder?\nA1: yes\nA2: 2\nA3: sphere\nA4: 6\nA5: 0\nA6: no\n"
},
{
"from": "gpt",
"value": "Q1A5\nQ2A1\nQ3A2\nQ4A6\nQ5A1\nQ6A3\nQ7A5\nQ8A1\nQ9A4\n"
}
] | CLEVR_CoGenT+Question_Answer_Matching | CLEVR_CoGenT+Question_Answer_Matching_535_CLEVR_trainA_002897.png |
multiinstruct_VQAv2_7393004 | [
{
"from": "human",
"value": "<image>\nDo you see green grass?"
},
{
"from": "gpt",
"value": "no"
}
] | VQAv2 | VQAv2_COCO_train2014_000000007393.jpg |
vision-flan_DTD+all_texture_detection++flecked/flecked_0003.jpg | [
{
"from": "human",
"value": "<image>\nTexture is defined as the feel, appearance or consistency of a surface or substance from a human's perspective. Detect all the textures in the image. Present it as a comma separated list"
},
{
"from": "gpt",
"value": "flecked,marbled"
}
] | DTD+all_texture_detection | DTD+all_texture_detection_186_flecked_0003.jpg |
vision-flan_coco+image_classification_vehicle+59392 | [
{
"from": "human",
"value": "Given an image of a vehicle, identify the kind of vehicle it is. The vehicle can be of different types; it could be something used, personal, or public transport. It could carry one or more people at the same time.\nOptions: (a) This image contains a boat (b) This image contains a bus (c) This image contains a bicycle (d) This image contains a motorcycle (e) This image contains a truck (f) This image contains a car (g) This image contains a train (h) This image contains an airplane\n<image>"
},
{
"from": "gpt",
"value": "(f) This image contains a car"
}
] | coco+image_classification_vehicle | coco+image_classification_vehicle_425_000000156652.jpg |
multiinstruct_mocheg_111356 | [
{
"from": "human",
"value": "<image>\nContext: Our ruling Hillary Clinton said in Sacramento, 'It is a fact that the economy does better when we have a Democrat in the White House.' It's similar to her March statement in Phoenix that 'The economy always does better when there's a Democrat in the White House.' Yes, Democratic presidents do have more Gross Domestic Product growth than Republicans, according to quarterly GDP data dating back to 1947. But Clinton's comments require several caveats. The current growth in the economy under Obama is lower than the Republican average. Factors such as oil prices also reflect the higher GDP growth under Democratic presidents. On top of that, comparing one period of time to another or one president to another can be problematic. We agree with PolitiFact Arizona's findings and rate Clinton's claim in Sacramento Half True. HALF TRUE - The statement is partially accurate but leaves out important details or takes things out of context. Click here for more on the six PolitiFact ratings and how we select facts to check. https://www.sharethefacts.co/share/ee86664c-a54d-466d-97d0-e5b488faf87e\nCan the context support \"'It is a fact that the economy does better when we have a Democrat in the White House.\"? \n\nOptions: (a) no (b) not sure (c) yes"
},
{
"from": "gpt",
"value": "(b) not sure"
}
] | multimodal_factual_checking | multimodal_factual_checking_111356-proof-33-HillaryClintonLynwood060716P.jpg |
multiinstruct_VQAv2_255182001 | [
{
"from": "human",
"value": "Is this person alone?\n<image>"
},
{
"from": "gpt",
"value": "yes"
}
] | VQAv2 | VQAv2_COCO_train2014_000000255182.jpg |
multiinstruct_mscoco_caption2014_340986 | [
{
"from": "human",
"value": "Look at image and tell me what is the content.\n<image>"
},
{
"from": "gpt",
"value": "A cropped photo of a big black train with a \"482\" being displayed on the front of it."
}
] | image_caption | image_caption_COCO_train2014_000000515502.jpg |
vision-flan_CLEVR_CoGenT+Question_Answering+CLEVR_trainA_026961.png | [
{
"from": "human",
"value": "<image>\nThe input for this task is an image of 3D-rendered objects and a question that fall into different categories. The questions fall into five classes of tasks: Exist, Count, Compare Integer, Query Attribute, and Compare Attribute. The task here is to answer the question.\n\nDoes the small cyan object have the same shape as the big blue metal thing?"
},
{
"from": "gpt",
"value": "no"
}
] | CLEVR_CoGenT+Question_Answering | CLEVR_CoGenT+Question_Answering_271_CLEVR_trainA_026961.png |
vision-flan_visdial+answer_question_4+87988 | [
{
"from": "human",
"value": "<image>\nThis is a short conversation between two people, represented as A and B. They are talking about the given picture and are discussing some questions about it. Pay attention to the conversation and answer the question.\n\nA: are there more than 2 people present?\nB: no, just 2\nA: are they going fast?\nB: moderately so\nA: male and female?\nB: cannot tell\nA: is it snowing?\nB: i don't think so\nQuestion: are they wearing goggles?"
},
{
"from": "gpt",
"value": "i only see their backs"
}
] | visdial+answer_question_4 | visdial+answer_question_4_272_COCO_val2014_000000399049.jpg |
vision-flan_WIKIART+art_classification+vincent-van-gogh_mademoiselle-gachet-at-the-piano-1890.jpg | [
{
"from": "human",
"value": "<image>\nFor the given painting, give me the artist name. Also provide the genre and style, if possible."
},
{
"from": "gpt",
"value": "This artwork was created by Vincent van Gogh. It falls under the genre of sketch and study and showcases Post Impressionism style of painting."
}
] | WIKIART+art_classification | WIKIART+art_classification_858_vincent-van-gogh_mademoiselle-gachet-at-the-piano-1890.jpg |
vision-flan_DOMAIN_NET+clipart_image_classification+clipart_258_000100 | [
{
"from": "human",
"value": "Clip art is defined as simple pictures or symbols used in documents and presentations. The input is a clip art image. Identify the main object in the image.\n<image>"
},
{
"from": "gpt",
"value": "shark"
}
] | DOMAIN_NET+clipart_image_classification | DOMAIN_NET+clipart_image_classification_280_clipart_258_000100.jpg |
vision-flan_MEMOTION+sentiment_detection+image_3365.jpg | [
{
"from": "human",
"value": "<image>\nCan you please read the image and extract all the text from it?"
},
{
"from": "gpt",
"value": "How many girls do you see? If you said 5 you're racist."
}
] | MEMOTION+sentiment_detection | MEMOTION+sentiment_detection_100_image_3365.jpg |
multiinstruct_VQA_activity_recognition_30030116 | [
{
"from": "human",
"value": "In this task, you will be asked about the activity of some object in the image. Question: What is the puppy doing?\n<image>"
},
{
"from": "gpt",
"value": "sleeping"
}
] | VQA_activity_recognition | VQA_activity_recognition_COCO_train2014_000000040580.jpg |
multiinstruct_VQA_object_recognition_10195548 | [
{
"from": "human",
"value": "In this task, you will answer a question about the class of an object in the image. What animal is in the picture?\n<image>"
},
{
"from": "gpt",
"value": "dog"
}
] | VQA_object_recognition | VQA_object_recognition_COCO_train2014_000000037716.jpg |
vision-flan_PACS+dog_image_category_classification+pic_019.jpg | [
{
"from": "human",
"value": "<image>\nYou will be given an image of a dog. The image could be of different categories like painting, cartoon, photograph, or sketch. Identify the image category.\nOptions: (a) Art painting (b) Photograph (c) Sketch (d) Cartoon"
},
{
"from": "gpt",
"value": "(d) Cartoon"
}
] | PACS+dog_image_category_classification | PACS+dog_image_category_classification_588_pic_019.jpg |
vision-flan_AID+aerial_scene_classification+resort_38 | [
{
"from": "human",
"value": "You are given an aerial image. Tell me the scene in the image.\n<image>"
},
{
"from": "gpt",
"value": "The aerial scene is Resort."
}
] | AID+aerial_scene_classification | AID+aerial_scene_classification_657_resort_38.jpg |
vision-flan_spot-the-diff+image_diff_identification+4614 | [
{
"from": "human",
"value": "<image>\nYou are provided with an image which contains two pictures side by side. Your task is to identify the differences between the two pictures. Separate the differences with a comma."
},
{
"from": "gpt",
"value": "the redish brown car moves slightly"
}
] | spot-the-diff+image_diff_identification | spot-the-diff+image_diff_identification_862_4614.png |
vision-flan_KVQA+image_captioning+33317.JPG | [
{
"from": "human",
"value": "You are provided with a picture. Write a caption for the image mentioning the people present in the image and also the occasion depicted.\n<image>"
},
{
"from": "gpt",
"value": "Borini training at Chelsea"
}
] | KVQA+image_captioning | KVQA+image_captioning_799_33317.JPG |
vision-flan_coco+image_classification_kitchen+81239 | [
{
"from": "human",
"value": "<image>\nGiven an image of something from the kitchen, identify what it could be. The image could be of cooking tools or items that are used for eating. It could also be used for serving food or storing it.\nOptions: (a) This image contains a cup (b) This image contains a bottle (c) This image contains a knife (d) This image contains a wine glass (e) This image contains a spoon (f) This image contains a bowl (g) This image contains a fork"
},
{
"from": "gpt",
"value": "(a) This image contains a cup"
}
] | coco+image_classification_kitchen | coco+image_classification_kitchen_317_000000307113.jpg |
vision-flan_FGVC_Aircraft+Aircraft_Classification_Variant+0487344 | [
{
"from": "human",
"value": "Your objective is to analyze an aircraft image and provide the variant of the aircraft. (e.g., A300B4).\n Variant: A variant indicates a variation of a particular aircraft model, often incorporating specific modifications, improvements, or customizations compared to the base model.\n<image>"
},
{
"from": "gpt",
"value": "MD-90"
}
] | FGVC_Aircraft+Aircraft_Classification_Variant | FGVC_Aircraft+Aircraft_Classification_Variant_482_0487344.jpg |
vision-flan_CUB-200-2011+Bird_Classification+Tennessee_Warbler_0071_175084.jpg | [
{
"from": "human",
"value": "<image>\nYour objective is to identify the species of the bird depicted in the provided image."
},
{
"from": "gpt",
"value": "Tennessee Warbler"
}
] | CUB-200-2011+Bird_Classification | CUB-200-2011+Bird_Classification_553_Tennessee_Warbler_0071_175084.jpg |
vision-flan_Caltech101+Image_classification+cougar_face_image_0035 | [
{
"from": "human",
"value": "In this task, you have to classify the object in the image among a total of 102 classes such as Airplane, Ant, Butterfly, Chair,... The classes of the image are a diverse set ranging from objects to living beings. Pay attention to details as the object in the image can be in any format(sketch, painting, captured photo, etc) So, your answer should be the class of the object in the image\n<image>"
},
{
"from": "gpt",
"value": "cougar face"
}
] | Caltech101+Image_classification | Caltech101+Image_classification_145_image_0035.jpg |
vision-flan_DOMAIN_NET+clipart_image_classification+clipart_313_000182 | [
{
"from": "human",
"value": "<image>\nClip art is defined as simple pictures or symbols used in documents and presentations. The input is a clip art image. Identify the main object in the image."
},
{
"from": "gpt",
"value": "toaster"
}
] | DOMAIN_NET+clipart_image_classification | DOMAIN_NET+clipart_image_classification_413_clipart_313_000182.jpg |
vision-flan_SCUT-CTW1500+text_detection+1140 | [
{
"from": "human",
"value": "Detect the text in the image.\n<image>"
},
{
"from": "gpt",
"value": "The texts are: Coffee; Breakfast Bueeitos & Sandwiches."
}
] | SCUT-CTW1500+text_detection | SCUT-CTW1500+text_detection_185_1140.jpg |
vision-flan_cinic-10+object_presence_shipping+150852 | [
{
"from": "human",
"value": "<image>\nThe given image can contain some vehicles used for transporting goods and materials across large distances, even around the world. The picture could also contain something that does not fit this description. Your job is to identify if the subject of the image can be used for shipping goods or not."
},
{
"from": "gpt",
"value": "The object cannot be used for shipping"
}
] | cinic-10+object_presence_shipping | cinic-10+object_presence_shipping_766_n01639765_36219.png |
vision-flan_Clevr+VQA_context+CLEVR_train_041121.png | [
{
"from": "human",
"value": "you are given some question and answer pairs as context, and you will answer the question at the end based on the image.\n\nWhat is the shape of the thing that is the same color as the big cylinder? cube\nDoes the small green cylinder have the same material as the big brown object that is behind the tiny green thing? yes\nHow many other things are the same shape as the large brown rubber thing? 1\nWhat material is the tiny cylinder that is behind the rubber cube on the right side of the cube to the left of the brown rubber block made of? metal\nAre there an equal number of metallic things that are on the right side of the gray thing and metallic things? no\nDo the large block to the left of the small green thing and the big block to the right of the tiny rubber object have the same material? yes\nIs the number of metal objects behind the large cyan metallic cylinder less than the number of blue rubber blocks? no\nWhat number of tiny cubes have the same color as the big metal object? 0\n\nDo the matte thing that is on the left side of the gray shiny cylinder and the large matte thing to the right of the cyan cube have the same shape?\n<image>"
},
{
"from": "gpt",
"value": "yes"
}
] | Clevr+VQA_context | Clevr+VQA_context_84_CLEVR_train_041121.png |
multiinstruct_VQA_color_30215117 | [
{
"from": "human",
"value": "Question: What color are the trees?\n\nAnswer the above question by first finding the object in the image and identify its color.\n<image>"
},
{
"from": "gpt",
"value": "brown"
}
] | VQA_color | VQA_color_COCO_train2014_000000079247.jpg |
vision-flan_ImageNet-R+image_classification+graphic_0 | [
{
"from": "human",
"value": "Your task is to classify the image using various categories. You need to carefully observe the details of the object in the image, including its shape, color, and texture, as these characteristics may vary across different renditions. Output the appropriate object name as the result of your classification process.\n<image>"
},
{
"from": "gpt",
"value": "chimpanzee"
}
] | ImageNet-R+image_classification | ImageNet-R+image_classification_850_graphic_0.jpg |
multiinstruct_ok_vqa_['train', 'val']_342757 | [
{
"from": "human",
"value": "<image>\nWhat time was this taken?"
},
{
"from": "gpt",
"value": "dusk"
}
] | ok_vqa | ok_vqa_COCO_train2014_000000342757.jpg |
vision-flan_ImageNet-C+image_classification_weather+344131 | [
{
"from": "human",
"value": "Given an image, identify what kind of weather conditions might have corrupted the image. It can be different types of bad weather or outdoor conditions\nOptions: (a) The corruption type is brightness (b) The corruption type is snow (c) The corruption type is fog (d) The corruption type is frost\n<image>"
},
{
"from": "gpt",
"value": "(b) The corruption type is snow"
}
] | ImageNet-C+image_classification_weather | ImageNet-C+image_classification_weather_387_ILSVRC2012_val_00032622.JPEG |
vision-flan_Winoground+image_captioning+146+image_0 | [
{
"from": "human",
"value": "In this task, you will be provided with an image and two captions. Your task is to identify which of the two captions correctly describes the image.\nOptions: (a) there's more coffee than milk (b) there's more milk than coffee\n<image>"
},
{
"from": "gpt",
"value": "(b) there's more milk than coffee"
}
] | Winoground+image_captioning | Winoground+image_captioning_592_ex_146_img_0.png |
vision-flan_Clevr+Question_Answer_Matching+CLEVR_train_043198.png | [
{
"from": "human",
"value": "You will be given an Image of 3D-rendered objects, a number of Questions and same number of Answers. The task here is to match the questions to the right answers according to the image you see. The format of the output shoud be something like: Q1A3,Q2A5,Q3A2,Q4A1,Q5A1\n\nQ1: There is a blue ball; is its size the same as the yellow matte object behind the yellow rubber sphere?\nQ2: What material is the other object that is the same shape as the green rubber thing?\nQ3: There is a object that is behind the yellow rubber cube and left of the large yellow ball; what size is it?\nQ4: How many other objects are the same material as the large sphere?\nQ5: There is a big yellow matte thing that is to the right of the small brown sphere; what is its shape?\nQ6: Are there any yellow matte spheres?\nQ7: How many objects are either large blocks that are in front of the large green matte cube or large objects behind the rubber sphere?\nQ8: How many objects are either tiny purple shiny blocks or yellow matte cubes?\nQ9: What is the shape of the tiny blue metal thing?\nA1: yes\nA2: 1\nA3: large\nA4: sphere\nA5: 2\nA6: rubber\nA7: no\n\n<image>"
},
{
"from": "gpt",
"value": "Q1A7\nQ2A6\nQ3A3\nQ4A5\nQ5A4\nQ6A1\nQ7A5\nQ8A2\nQ9A4\n"
}
] | Clevr+Question_Answer_Matching | Clevr+Question_Answer_Matching_49_CLEVR_train_043198.png |
multiinstruct_wikihow_wikihow_text_image_step_order_9791756_cut-rigid-foam-insulation | [
{
"from": "human",
"value": "You are doing Cutting With a Utility Knife. Is the step specified in the picture the next or previous step to \"Score the insulated foam with your utility knife.\"?\n\nOptions: previous next\n<image>"
},
{
"from": "gpt",
"value": "next"
}
] | wikihow_text_image_step_order | wikihow_text_image_step_order_method_1_step_2.jpg |
vision-flan_Yoga-82+yoga_pose_recognition+6308650942_e266c75bf2_b | [
{
"from": "human",
"value": "What is the name of the yoga pose?\n<image>"
},
{
"from": "gpt",
"value": "The yoga pose is Seated Forward Bend pose or Paschimottanasana."
}
] | Yoga-82+yoga_pose_recognition | Yoga-82+yoga_pose_recognition_215_6308650942_e266c75bf2_b.jpg |
multiinstruct_visualgenome_VG_2366133_1755415 | [
{
"from": "human",
"value": "<image>\nWhat is the horse doing?"
},
{
"from": "gpt",
"value": "Jumping over a platform"
}
] | visualgenome_vqa | visualgenome_vqa_2366133.jpg |
vision-flan_trainSet+image_classification+041 | [
{
"from": "human",
"value": "<image>\nUsing one word, classify the type of distortion / style is applied to the image or what type of setting is represented in the image."
},
{
"from": "gpt",
"value": "OutdoorManMade"
}
] | trainSet+image_classification | trainSet+image_classification_431_041.jpg |
multiinstruct_VQA_sentiment_understanding_20018026 | [
{
"from": "human",
"value": "Please analyze the sentiment depicted in the image and answer the question.\nIs the woman happy?\n<image>"
},
{
"from": "gpt",
"value": "no"
}
] | VQA_sentiment_understanding | VQA_sentiment_understanding_COCO_train2014_000000548723.jpg |
vision-flan_spot-the-diff+image_diff_identification+13043 | [
{
"from": "human",
"value": "You are provided with an image which contains two pictures side by side. Your task is to identify the differences between the two pictures. Separate the differences with a comma.\n<image>"
},
{
"from": "gpt",
"value": "where there s a person in a white shirt on the left there s someone in a black shirt on the right, the man in yellow is farther away on the right, the man in white with the woman in black is obscured by the light post on the left"
}
] | spot-the-diff+image_diff_identification | spot-the-diff+image_diff_identification_769_13043.png |
multiinstruct_GQA_11532754_2334825 | [
{
"from": "human",
"value": "<image>\nAre there both window and a door in the image?"
},
{
"from": "gpt",
"value": "no"
}
] | GQA | GQA_2334825.jpg |
vision-flan_PACS+elephant_image_category_classification+pic_226.jpg | [
{
"from": "human",
"value": "You will be given an image of an elephant. The image could be of different categories like painting, cartoon, photograph, or sketch. Identify the image category.\nOptions: (a) Photograph (b) Sketch (c) Art painting (d) Cartoon\n<image>"
},
{
"from": "gpt",
"value": "(d) Cartoon"
}
] | PACS+elephant_image_category_classification | PACS+elephant_image_category_classification_110_pic_226.jpg |
vision-flan_VQA-E+image_captioning+195529+60759 | [
{
"from": "human",
"value": "You are provided with an image. Explain what is going on in the image.\n<image>"
},
{
"from": "gpt",
"value": "Two people standing on snow boards beside each other."
}
] | VQA-E+image_captioning | VQA-E+image_captioning_778_COCO_train2014_000000195529.jpg |
vision-flan_infographicvqa+question_answer+12651 | [
{
"from": "human",
"value": "<image>\nHere is an infographic representing some data in a visual form. Please answer my question using the data in the image\n\nWhat is the COVID-19-associated hospitalization rate (per 100,000 population) of non-hispanic blacks during March 1-July 18, 2020?"
},
{
"from": "gpt",
"value": "247"
}
] | infographicvqa+question_answer | infographicvqa+question_answer_175_11544.jpeg |
vision-flan_FLICKR30K+caption_image+397080364.jpg | [
{
"from": "human",
"value": "Each image has something going on. Carefully analyze the image and generate 5 captions for each image.\n<image>"
},
{
"from": "gpt",
"value": "Caption 1: A skier shrugs from behind goggles at an experts only sign while snow covered mountains fill the sky in the background.\nCaption 2: A skier wearing white pants and a purple and black jacket poses for a picture next to a sign near the ski lift.\nCaption 3: A skier is posing for a photo near an \" experts only \" sign a top of a large mountain.\nCaption 4: A skier poses next to a \" Experts Only \" sign with a ski lift in the background.\nCaption 5: A skier is posing beside an \" experts only \" sign with ski lifts in the background.\n"
}
] | FLICKR30K+caption_image | FLICKR30K+caption_image_225_397080364.jpg |
multiinstruct_VQA_attribute_30054625 | [
{
"from": "human",
"value": "<image>\nIn this task, you will be asked about the attribute of some object. Question: What material are the cabinets?"
},
{
"from": "gpt",
"value": "wood"
}
] | VQA_attribute | VQA_attribute_COCO_train2014_000000479936.jpg |
vision-flan_VisDA-2017+image_classification+src_1_02691156_6a9b8fd1f4ddd87f152be1f5910e33df__265_123_165 | [
{
"from": "human",
"value": "<image>\nYour task is to classify an image based on its corresponding object category. The image contains a variety of objects distributed among 12 categories, including aeroplane, horse, knife, person, plant, and others. To accurately classify the image, carefully analyze its visual characteristics, such as shape, color, texture, and spatial context relations, as these attributes can vary significantly across different domains. Once you have identified the object category of the image, output the appropriate label for your classification."
},
{
"from": "gpt",
"value": "aeroplane"
}
] | VisDA-2017+image_classification | VisDA-2017+image_classification_629_src_1_02691156_6a9b8fd1f4ddd87f152be1f5910e33df__265_123_165.png |
multiinstruct_mscoco_caption2014_767185 | [
{
"from": "human",
"value": "In this task, you will look at the image and briefly describe the image.\n<image>"
},
{
"from": "gpt",
"value": "A person cutting a slice of pizza with a knife and fork. "
}
] | image_caption | image_caption_COCO_train2014_000000177381.jpg |
End of preview.
🚀 Vision-Flan Dataset
vision-flan_191-task-1k is a human-labeled visual instruction tuning dataset consisting of 191 diverse tasks and 1,000 examples for each task. It is constructed for visual instruction tuning and for building large-scale vision-language models.
Paper or blog for more information:
https://github.com/VT-NLP/MultiInstruct/
https://vision-flan.github.io/
Paper coming soon 😊
Citation
Paper coming soon 😊. If you use Vision-Flan, please use the following cites:
@misc{visionFlan2023,
title = {Vision-Flan:Scaling Visual Instruction Tuning},
url = {https://vision-flan.github.io/},
author = {Zhiyang Xu and Trevor Ashby and Chao Feng and Rulin Shao and Ying Shen and Di Jin and Qifan Wang and Lifu Huang},
month = {Sep},
year = {2023}
}
@inproceedings{DBLP:conf/acl/XuSH23,
author = {Zhiyang Xu and Ying Shen and Lifu Huang},
editor = {Anna Rogers and Jordan L. Boyd{-}Graber and Naoaki Okazaki},
title = {MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning},
booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), {ACL} 2023, Toronto, Canada, July 9-14, 2023},
pages = {11445--11465},
publisher = {Association for Computational Linguistics},
year = {2023},
url = {https://doi.org/10.18653/v1/2023.acl-long.641},
doi = {10.18653/v1/2023.acl-long.641},
timestamp = {Thu, 10 Aug 2023 12:35:59 +0200},
biburl = {https://dblp.org/rec/conf/acl/XuSH23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
License:
Please carefully check the licenses for all the datasets on this page before use.
Contact:
If you have any questions or concerns please contact us at [email protected] .
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