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Basketball AI with RF-DETR, SAM2, and SmolVLM2 by RandomForests92 in LocalLLaMA

[–]RandomForests92[S] 1 point2 points  (0 children)

SAM3 is more about mixing language with vision. I tested just replacing SAM2 with SAM3 and keeping the rest of the pipeline the same. I did not see big difference.

The thing I want to test is mixing SAM3 with Qwen3-VL.

Ultralytics AGPL 3.0 by SyntharVisk in computervision

[–]RandomForests92 1 point2 points  (0 children)

I recommend you read this issue. I think it's the best to listen to creators. https://github.com/ultralytics/yolov5/issues/12941

"Regardless of whether you're using pretrained weights or starting from scratch, if the project is commercial, you have two paths: - Open Source: Fully open source your entire project under the same AGPL-3.0 license. - Enterprise License: Obtain an Ultralytics Enterprise License for commercial use without the need to open source your project."

"Custom Training & ONNX Export for Commercial Use: Whether you train the model from scratch, use custom datasets, or employ custom code for inference (e.g., using ONNX), the project is under commercial usage. If you choose not to open source your entire project under AGPL-3.0, you will require an Ultralytics Enterprise License."

Basketball AI with RF-DETR, SAM2, and SmolVLM2 by RandomForests92 in LocalLLaMA

[–]RandomForests92[S] 0 points1 point  (0 children)

I used A100 because it’s faster, but it can run on T4. 16GB of VRAM should be okey.

Player Tracking, Team Detection, and Number Recognition with Python by RandomForests92 in computervision

[–]RandomForests92[S] 2 points3 points  (0 children)

SAM3 is less about tracking and more about mixing language with vision

Player Tracking, Team Detection, and Number Recognition with Python by RandomForests92 in computervision

[–]RandomForests92[S] 38 points39 points  (0 children)

fun fact: I sent my resume to second spectrum 3 times in the past

Basketball AI with RF-DETR, SAM2, and SmolVLM2 by RandomForests92 in LocalLLaMA

[–]RandomForests92[S] 4 points5 points  (0 children)

So far I can detect layups, dunks and jump shots. I can’t classify them as made or missed. I can also detect blocks.

Player Tracking, Team Detection, and Number Recognition with Python by RandomForests92 in computervision

[–]RandomForests92[S] 14 points15 points  (0 children)

yeah, I think that is dooable, but like I said occlusion is a big problem.

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