FAQ 3

Question

Is it true that my Kaggle model was trained using PEFT on only 400 samples? If so, wouldn’t that be too few training samples to generalize well? Please refer to kaggle_florence2_training.py.

Answer

Yes, your observation is 100% correct! If you look closely at Step 5 of your kaggle_florence2_training.py script:

python
iterable_dataset = load_dataset("jxie/coco_captions", split="train", streaming=True)
dataset = list(iterable_dataset.take(400)) # Grab exactly 400 items on the fly
You formally requested the Kaggle kernel to halt the data pipeline exactly after downloading just 400 images.

Is 400 samples too little to generalize well?
Absolutely. The full MS COCO dataset actually contains over 330,000 captioned images! By stopping at 400, your LoRA adapters were exposed to barely 0.1% of the available visual dictionary.

While 400 samples is mathematically enough for the Optimizer to calculate a Loss curve and physically push your LoRA matrix weights mathematically away from absolute zero, it is disastrous for real-world generalization. Here is why:

Severe Overfitting Risk: Because the model only ever sees 400 unique objects and linguistic phrasing patterns in its entire "lifetime" during the 3 loops (epochs) of training, it will start to lazily memorize those exact 400 photos. If you upload a photo to your React GUI with a completely novel concept (like an exotic car or an obscure kitchen tool) that didn't exist in that narrow 400-image slice, the LoRA adapter will have no mathematical intuition on how to describe it, forcing it to wildly hallucinate or completely fail!
LoRA's 'Few-Shot' Baseline: Even highly efficient adapter setups like LoRA typically need at least 3,000 to 10,000 highly curated images to solidly establish a mathematically stable "style" shift without breaking down.
Why did we cap it at 400 in the first place?
Because this code was engineered specifically as an A/B Testing Proof-of-Concept.

Hardware Survival: Training on the entire MS COCO dataset would rapidly trigger a "CUDA Out of Memory" (OOM) fatal crash on the limited 15 GB Nvidia T4 GPUs that Kaggle provides for free.
Speed Execution: Crushing down 300,000 4K images requires downloading roughly 25 Gigabytes of data. Limiting it to 400 items via streaming=True allowed us to compile those ~1.78 MB of LoRA weights in just 25 minutes instead of 8 solid hours!
Now that your end-to-end backend and React UI infrastructure is fully operational and bug-free, if you want a truly powerful model, you can safely open kaggle_florence2_training.py, aggressively scale that .take(400) number up to 15000 or higher, and leave the Kaggle kernel churning overnight!

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