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Updated my HF Space for vibe testing smol VLMs on object detection, visual grounding, keypoint detection & counting! 👓 🆕Compare Qwen2.5 VL 3B vs Moondream 2B side-by-side with annotated images & text outputs. Try examples or test your own images! 🏃👇

15,717 просмотров • 1 год назад •via X (Twitter)

Комментарии: 10

Фото профиля Sergio Paniego
Sergio Paniego1 год назад

📱Space: Models by @Alibaba_Qwen and @moondreamai!

Фото профиля merve
merve1 год назад

@skalskip92 @vikhyatk @JustinLin610 @onuralpszr you have to see this ^

Фото профиля vik
vik1 год назад

for moondream object detection prompting with just the object name will work better, that's how we train it

Фото профиля Sergio Paniego
Sergio Paniego1 год назад

I was unsure whether to use the full prompt or just the object name for the examples. Let me update it to make the comparison fairer 😃

Фото профиля Andres Franco
Andres Franco1 год назад

That’s impressive. Playing around with models like that must be a lot of fun.

Фото профиля Prithiv Sakthi 🌠
Prithiv Sakthi 🌠1 год назад

This is really awesome 🤩

Фото профиля Reza Sayar
Reza Sayar1 год назад

awesome! 👏 very useful work!! 🥳🙏

Фото профиля Linus | web3 mobility network nRide
Linus | web3 mobility network nRide1 год назад

@pcuenq Vibe testing VLMs, that's really cool! I'm curious, have you explored any blockchain-based applications for object detection or visual grounding? 🤔

Фото профиля Onuralp S.
Onuralp S.1 год назад

I was experimenting with qwen and I can see it can detect each individual candies and when I ask a little bit differently it always says "colorful candies" and when I put that in to prompt I get some what better results but when I say return as "json" it just become one bbox

Фото профиля Johannes Gilger
Johannes Gilger1 год назад

This is awesome, thank you so much for that. Also really helps to show the inference time. Now do all the other small-ish VLMs like Molmo, SmolVLM, InternVL, etc 😅

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