The original SAM struggled on video because frames are lower quality, it depended on external trackers that didn’t generalise across object categories, and there was no feedback loop to learn from segmentation errors. SAM 2 was designed to operate directly on video frames, include an error‑learning mechanism, and remove the need for separate trackers.
The linked blog covers theoretical aspects of fine‑tuning Meta’s SAM model in just 5 min.
I am informing the audience that SAM can be fine‑tuned, highlighting a quick 5‑minute blog for theory and announcing that a detailed step‑by‑step tutorial will be published soon, and I recommend following Labellerr for similar updates.
I posted on 2025‑07‑23 that AI software engineers struggling with image segmentation using the base SAM should not miss the guide I recommend. It directly tackles the pain point and I consider it essential for anyone working with the base SAM.
Decision: promote the guide as essential for overcoming difficulties with base‑SAM segmentation.
I noted that “medical imaging” annotations constitute the largest share of instance masks in the SAM3 dataset.
With Scale AI founder on the AI hot seat at AI at Meta, it would be interesting to see efforts underway especially the emphasis at data infrastructure layer!