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Ollama视觉模型实测

news 2026/5/12 17:04:50

我简单地拍了几张营养标签的照片，并使用提示转换为 JSON。然后我针对一堆自托管模型和 Mistral Open API 进行了测试。它们都表现得相当好，但 Mistral API 是最好的。

你知道，击败基准测试是一回事。但基准测试通常是自我报告的随机变量，就像驾照考试和驾驶并不完全一样，它们不能保证给定的现实生活问题会以可行的方式解决。这就是为什么我想给它一个真实的测试用例。

测试很简单：我拍了一堆营养标签的照片，就像下面这张：

视觉模型在将其转换为 JPEG 方面有多好？

我尝试提取以下格式的 JSON：

{ "calories": 180, "serving_size": 28.0, "unit": "g" }

我使用的提示：

The image is the label of a packaged food product. Extract the following information from the nutrition facts label and return ONLY a JSON object with no additional text: - calories: total calories per serving (integer) - serving_size: numeric serving size value (float) - unit: unit of measurement for serving size (string, e.g., "g", "ml", "oz", "cup") Use this exact format: ```json { "$schema": "http://json-schema.org/draft-07/schema#", "type": "object", "properties": { "calories": { "type": "integer", "description": "Total calories per serving" }, "serving_size": { "type": "number", "description": "Numeric serving size" }, "unit": { "type": "string", "description": "Unit of measurement (e.g., g, ml, oz, cups)" } }, "required": ["calories", "serving_size", "unit"] }

Example:

{"calories":180,"serving_size":28.0,"unit":"g"}

If any value cannot be determined from the label, use null for that field.

我运行同样的东西 50 次，看看是否能得到期望的结果。 我使用的评估标准：

prompt_path: “…/prompts/calories_and_serving_size.md”
repeat: 50
threshold: 0
cases:

id: calories-label-180
steps:
- input:
  image_path: “images/IMG_B768CE83-9FEC-461A-BE63-CDDF64EBEB58.jpeg”
  max_tokens: 64
  expectations:
  - type: equals
    value:
    calories: 180
    serving_size: 40.0
    unit: g
id: calories-label-harvest-trail-mix-1
steps:
- input:
  image_path: “images/IMG_3236.HEIC”
  max_tokens: 64
  expectations:
  - type: oneOf
    values:
    - calories: 220
      serving_size: 50.0
      unit: g
    - calories: 220
      serving_size: 0.5
      unit: cup
    - calories: 220
      serving_size: 50.0
      unit: grams
id: calories-label-chunky-supreme-granola-exact
steps:
- input:
  image_path: “images/IMG_3228.HEIC”
  max_tokens: 64
  expectations:
  - type: equals
    value:
    calories: 630
    serving_size: 140.0
    unit: g
id: calories-label-maple-eh-granola-exact
steps:
- input:
  image_path: “images/IMG_3231.HEIC”
  max_tokens: 64
  expectations:
  - type: equals
    value:
    calories: 570
    serving_size: 130.0
    unit: g
id: calories-label-harvest-trail-mix-2
steps:
- input:
  image_path: “images/IMG_3232.HEIC”
  max_tokens: 64
  expectations:
  - type: oneOf
    values:
    - calories: 220
      serving_size: 50.0
      unit: g
    - calories: 220
      serving_size: 0.5
      unit: cup
    - calories: 220
      serving_size: 50.0
      unit: grams
id: calories-label-harvest-trail-mix-3
steps:
- input:
  image_path: “images/IMG_3237.HEIC”
  max_tokens: 64
  expectations:
  - type: oneOf
    values:
    - calories: 220
      serving_size: 50.0
      unit: g
    - calories: 220
      serving_size: 0.5
      unit: cup
    - calories: 220
      serving_size: 50.0
      unit: grams
id: calories-label-hersheys-christmas-cookies-exact
steps:
- input:
  image_path: “images/IMG_3276.HEIC”
  max_tokens: 64
  expectations:
  - type: equals
    value:
    calories: 200
    serving_size: 41.0
    unit: g
id: calories-label-mnm-mint-chocolate-candy-1
steps:
- input:
  image_path: “images/IMG_3279.HEIC”
  max_tokens: 64
  expectations:
  - type: oneOf
    values:
    - calories: 190
      serving_size: 40.0
      unit: g
    - calories: 220
      serving_size: 0.25
      unit: cup
id: calories-label-hersheys-candy-cane-kisses
steps:
- input:
  image_path: “images/IMG_3280.HEIC”
  max_tokens: 64
  expectations:
  - type: equals
    value:
    calories: 220
    serving_size: 42.0
    unit: g
id: calories-label-milk-chocolate-christmas-balls
steps:
- input:
  image_path: “images/IMG_3282.HEIC”
  max_tokens: 64
  expectations:
  - type: oneOf
    values:
    - calories: 200
      serving_size: 40.0
      unit: g
    - calories: 200
      serving_size: 6.0
      unit: pcs
    - calories: 200
      serving_size: 6.0
      unit: pieces
id: calories-label-mnm-mint-chocolate-candy-2
steps:
- input:
  image_path: “images/IMG_3284.HEIC”
  max_tokens: 64
  expectations:
  - type: oneOf
    values:
    - calories: 190
      serving_size: 40.0
      unit: g
    - calories: 220
      serving_size: 0.25
      unit: cup
id: calories-label-jumbo-sour-sukkers
steps:
- input:
  image_path: “images/IMG_3286.HEIC”
  max_tokens: 64
  expectations:
  - type: equals
    value:
    calories: 150
    serving_size: 40.0
    unit: g
  - type: oneOf
    values:
    - calories: 150
      serving_size: 40.0
      unit: g
    - calories: 150
      serving_size: 2.0
      unit: pcs
    - calories: 150
      serving_size: 2.0
      unit: pieces
id: calories-label-quality-street
steps:
- input:
  image_path: “images/IMG_3288.HEIC”
  max_tokens: 64
  expectations:
  - type: oneOf
    values:
    - calories: 200
      serving_size: 42.0
      unit: g
    - calories: 200
      serving_size: 5.0
      unit: pcs
    - calories: 200
      serving_size: 5.0
      unit: pieces

不再废话，以下是结果： ![无](https://i-blog.csdnimg.cn/img_convert/4d6a03e2259cc129d7eae5c3fae24b4c.webp?x-oss-process=image/format,png) 测试用例的平均模型准确度让我们分解一下：我认为 Llava 失败了。我认为 Llava 是一个 OCR 模型，旨在像 DeepSeek-OCR 一样使用。这些模型不属于这里，它们不遵循像"以 JSON 格式输出"这样的具体指令。 第二个学习是：Qwen3，8B 参数，在 16GB-VRAM 机器上工作得很好。但是，在现实设置中，它们可能并没有比在更小机器上也能工作的 Qwen3:2B 带来太多 —— 它们可能即使在 6GB VRAM 上也能正常工作。使用 8B 来实现与统计噪音无法区分的性能提升可能是不可行的。 另一个观点是，Qwen3:2B 似乎比 Qwen3:4B 更好。我不知道这是否真的是改进，或者只是统计波动。我目前的想法是：如果应用程序实际上需要一个小窗口，那么增加上下文窗口确实没有意义。不仅会增加成本，实际上可能还会降低性能。 最后一个学习：托管 Ollama 时，打开流式传输！否则，当请求超过 60 秒时，请求会被丢弃。我听说这是可定制的，但默认为流式传输可能更容易，尽管需要额外的代码。 --- 原文链接：[Ollama视觉模型实测 - 汇智网](https://www.hubwiz.com/blog/ollama-vision-models-test/)

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