Row 15284

Row ID: 15284 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 15284 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

I've tried long and complex prompts for my app [Calora](https://apps.apple.com/fi/app/calora/id6475647627).

Right now we're going with this:

For attached image, estimate the range (low, high) for 1) weight, 2) calories, 3) protein, 4) carbs, 5) fat. Offer a range of combined totals for multiple items in image. If available, use hints to understand smaller than usual portion sizes. After reviewing image, condense your estimates into one 'RESULT' line and one 'TITLE' line. Use the format: 'RESULT, total\_weight\_min, total\_weight\_max, total\_kcal\_min, total\_kcal\_max, g\_protein\_min, g\_protein\_max, g\_carbs\_min, g\_carbs\_max, g\_fat\_min, g\_fat\_max', followed by 'TITLE, \[combined title for all images\]'. In last two lines, only include 'RESULT', 'TITLE', commas, and floats. No formatting.

Example: 'Brief text with reasoning.

RESULT, 512.1, 723.2, 842.3, 1251.5, 41.3, 62.3, 128.7, 158.1, 31.2, 50.1 TITLE, Assorted Fruits’

Most if it is just ensuring results are easy to parse.

We are using [nutrition5k](https://github.com/google-research-datasets/Nutrition5k) database with ground truth values to validate accuracy. It tends to do well, but one can always do better. I've added some images to ground truth data where I've weighted the foods and know what they are, allowing me to get reasonable estimates from database based approach.

One problem is that it assumes quite high thickness for ham, cheese, bread, likely some default US portions. Hinting about smaller portions helps a tad, but there's still some reasonably common errors in this direction.

[Here](https://dosibjrn.github.io/report-2024-05-15-2242-gpt4o-seed123-t0-g0-adj-50-hardest.html) are results for 50 of the hardest cases out of 660 I'm currently using for wider quality assessment.

Any tips or thoughts regarding the prompt on improving accuracy for the more challenging images? GPT itself is coming up with ideas resulting in just way worse results.

Field	Value
text	I've tried long and complex prompts for my app [Calora](https://apps.apple.com/fi/app/calora/id6475647627). Right now we're going with this: " For attached image, estimate the range (low, high) for 1) weight, 2) calories, 3) protein, 4) carbs, 5) fat. Offer a range of combined totals for multiple items in image. If available, use hints to understand smaller than usual portion sizes. After reviewing image, condense your estimates into one 'RESULT' line and one 'TITLE' line. Use the format: 'RE…
label	r/chatgpt
dataType	post
communityName	r/ChatGPT
datetime	2024-05-20
username_encoded	Z0FBQUFBQm5Lakw4MDB4NXVYU0M1VlIxMDJRSkdVVmdLTGNzUE9abUxuUVFwNjV3WVJ5WUVYOHNKVGlBSlQ3Q1AwcXhGbjEwbVR5TUVZSUNfeXN3S19JQnE2eEVKUkhpUWc9PQ==
url_encoded	Z0FBQUFBQm5Lak9Mc0xENnQ2cGF2bVpGbmhRS0xRZjhqTnNQRmRkbFVkZURGNTJTclBxZE1ZU3hnMTVMWElrNEhTR3JvSi1wSkZvQUJUNGszQTExVUp4TjVqMUZaclA1aWJqNXB0TEYzUFVlNVVQVjkxZGdWVXNvclNiUVhZRWVsakpEb1RHbXhnalQwMEFZWFUtNi1hYU5JbTVxaGc5OFNWa2VZVE9faTZHYS11NDRPXy1HVEZNSEw5ZlhkSlRfNE4zOHg0STZYN0ZNWGNNVXl0OThBaUtTLWZtZGprS2RpZz09

Raw Record

{
  "text": "I've tried long and complex prompts for my app [Calora](https://apps.apple.com/fi/app/calora/id6475647627).\n\nRight now we're going with this:\n\n\"\n\nFor attached image, estimate the range (low, high) for 1) weight, 2) calories, 3) protein, 4) carbs, 5) fat. Offer a range of combined totals for multiple items in image. If available, use hints to understand smaller than usual portion sizes. After reviewing image, condense your estimates into one 'RESULT' line and one 'TITLE' line. Use the format: 'RESULT, total\\_weight\\_min, total\\_weight\\_max, total\\_kcal\\_min, total\\_kcal\\_max, g\\_protein\\_min, g\\_protein\\_max, g\\_carbs\\_min, g\\_carbs\\_max, g\\_fat\\_min, g\\_fat\\_max', followed by 'TITLE, \\[combined title for all images\\]'. In last two lines, only include 'RESULT', 'TITLE', commas, and floats. No formatting.\n\nExample:  \n'Brief text with reasoning.\n\nRESULT, 512.1, 723.2, 842.3, 1251.5, 41.3, 62.3, 128.7, 158.1, 31.2, 50.1  \nTITLE, Assorted Fruits’\n\n\"\n\nMost if it is just ensuring results are easy to parse.\n\nWe are using [nutrition5k](https://github.com/google-research-datasets/Nutrition5k) database with ground truth values to validate accuracy. It tends to do well, but one can always do better. I've added some images to ground truth data where I've weighted the foods and know what they are, allowing me to get reasonable estimates from database based approach.\n\nOne problem is that it assumes quite high thickness for ham, cheese, bread, likely some default US portions. Hinting about smaller portions helps a tad, but there's still some reasonably common errors in this direction.\n\n[Here](https://dosibjrn.github.io/report-2024-05-15-2242-gpt4o-seed123-t0-g0-adj-50-hardest.html) are results for 50 of the hardest cases out of 660 I'm currently using for wider quality assessment.\n\nAny tips or thoughts regarding the prompt on improving accuracy for the more challenging images? GPT itself is coming up with ideas resulting in just way worse results.",
  "label": "r/chatgpt",
  "dataType": "post",
  "communityName": "r/ChatGPT",
  "datetime": "2024-05-20",
  "username_encoded": "Z0FBQUFBQm5Lakw4MDB4NXVYU0M1VlIxMDJRSkdVVmdLTGNzUE9abUxuUVFwNjV3WVJ5WUVYOHNKVGlBSlQ3Q1AwcXhGbjEwbVR5TUVZSUNfeXN3S19JQnE2eEVKUkhpUWc9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9Mc0xENnQ2cGF2bVpGbmhRS0xRZjhqTnNQRmRkbFVkZURGNTJTclBxZE1ZU3hnMTVMWElrNEhTR3JvSi1wSkZvQUJUNGszQTExVUp4TjVqMUZaclA1aWJqNXB0TEYzUFVlNVVQVjkxZGdWVXNvclNiUVhZRWVsakpEb1RHbXhnalQwMEFZWFUtNi1hYU5JbTVxaGc5OFNWa2VZVE9faTZHYS11NDRPXy1HVEZNSEw5ZlhkSlRfNE4zOHg0STZYN0ZNWGNNVXl0OThBaUtTLWZtZGprS2RpZz09"
}

Explore Dataset Explore Row

Entry Information

Entry ID: 15284
Repository: Axioma AXP
Dataset: arrmlet/reddit_dataset_36
Total Entries: 100,000