> ## Content Index > Fetch the complete content index at: https://www.hubwiz.com/blog/llms.txt > Use this file to discover other available public pages before exploring further. # Ollama视觉模型实测 - URL: https://www.hubwiz.com/blog/ollama-vision-models-test/ - Published: 2026-02-25T03:00:54.000Z - Updated: 2026-02-25T03:00:54.000Z - Description: 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。 - Author: admin - Tags: MODEL-ZOO, OLLAMA, #Import 2026-10-14 14:41 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： ![无](https://www.hubwiz.com/blog/content/images/2026/02/1-iQsbkyxMZDuHn0p8HehCjw-1.jpeg) 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: ```json { "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://www.hubwiz.com/blog/content/images/2026/02/1-NPlFgwSrmcSZbr3ljzmvZA.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: [Vision Models in the Wild: A Test Case](https://medium.com/@sinan.ozel%5F23433/vision-models-in-the-wild-a-test-case-13b865c3b155?ref=hubwiz.com) 汇智网翻译整理，转载请标明出处