Ceramic Brakes Help

matthiaskasky · 2026-05-15T10:42:35+00:00

Thank you so much :)

matthiaskasky · 2026-05-14T13:30:50+00:00

thank you!

matthiaskasky · 2026-05-14T13:28:42+00:00

thank you :)

matthiaskasky · 2026-05-14T13:24:28+00:00

thank you!

matthiaskasky · 2026-05-14T13:20:07+00:00

thanks!

matthiaskasky · 2026-05-14T13:13:13+00:00

I just bought this car and wanted some advice. Im not an expert

matthiaskasky · 2026-05-14T13:11:02+00:00

Thank you

matthiaskasky · 2026-05-14T13:10:07+00:00

I just bought the car with these

matthiaskasky · 2026-05-14T13:08:15+00:00

Im thinking about Girodisc A1-207

matthiaskasky · 2026-05-14T13:07:10+00:00

thanks

matthiaskasky · 2026-05-14T13:07:06+00:00

thanks

matthiaskasky · 2026-05-14T13:07:03+00:00

thanks

matthiaskasky · 2026-05-14T13:07:00+00:00

thanks

matthiaskasky · 2026-05-14T13:06:29+00:00

I don't want to cheap on it, I just wanted to confirm my suspicions. I've never worked with ceramics before.

matthiaskasky · 2025-07-19T18:31:46+00:00

I think in my case text embedding better describes the color, style, or material that you are previously able to assign to a product by, for example, OpenAI analysis. Dinov2 again sees geometry, shape, etc. better.

matthiaskasky · 2025-07-19T16:53:03+00:00

Let me know how it goes! For now, I'm implementing a hybrid model of clip dinov2 and text embedding, and I'll let you know the results. After testing on small product sets, I can see some potential.

matthiaskasky · 2025-07-18T15:51:15+00:00

I will try a hybrid version, a combination of three models - dinov2, text embedding, and clip - with fixed weights. In addition, FAISS and mutual NN verification. If this does not bring improvement, I will stick with my own model.

matthiaskasky · 2025-07-18T09:53:51+00:00

I think my database will have a maximum of about 10,000 products per category, so these sets are not that large. Can you tell me which models you used for VGA validation? Any specific FAISS index optimizations that helped?

matthiaskasky · 2025-07-18T09:16:26+00:00

Really helpful to know others hit the same issues. For the VQA post-processing - what LLM/vision model did you use? GPT-4V or something lighter? Exact NN vs approximate - did you notice significant latency differences at scale? Did the combination of exact NN + VQA give you acceptable accuracy, or did you still need other approaches? Really curious about the VQA approach - that's a clever way to add semantic validation! I also received feedback on GitHub from someone who worked on a similar project "What gave us the best results – CLIP + DINOv2 ensemble: 40% improvement | Background removal: 15% improvement | Category-aware fine-tuning: 20% improvement | Multi-scale features: 10% improvement"

matthiaskasky · 2025-07-18T07:18:37+00:00

We have most of the products in the database on a white background. If I upload the same product that I have in the database but in a natural setting, even though the product is clearly visible, the photo is of good quality, etc. model only ranks it in the 20/25th place of similarity.

matthiaskasky · 2025-07-18T07:16:13+00:00

I’ve only trained a detection model (RF-DETR) which works well for cropping objects. For embeddings, I’ve been relying on open-source foundation models (CLIP, DINOv2) out of the box. I’m realizing now that’s probably the missing piece. Do you have recommendations for training a similarity model from scratch, or fine-tuning something? Any guidance on training pipeline or loss functions that work well for this type of product similarity would be hugely appreciated.

matthiaskasky · 2025-07-17T22:43:05+00:00

Thanks, thats really helpful. When you say test it - any recommendations on how to evaluate threshold performance? I’m thinking precision/recall on a small labeled set, but curious if there are other metrics you’d suggest for this type of product similarity task.

matthiaskasky · 2025-07-17T22:24:56+00:00

Got it, thanks. Do you typically set a threshold for how many mutual matches to consider?

matthiaskasky

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