I built a imagine classifier from scratch (vibe coded)
So I’m new to ai and coding etc have super basic knowledge of them I vibe coded a image classifier on to run on my PC
Right now these are the results so far :
Experimental Results (CIFAR‑100 / any image data set )
| Metric | Value |
|--------|-------|
| Model | ConvNeXt‑Large (13.2 M params) |
| Hardware | NVIDIA RTX 3060 12 GB, Ryzen 5600G |
| Training time (100 epochs) | ~15 minutes |
| Validation accuracy | **75–78%** |
| Peak VRAM usage | 2.8 GB |
| Throughput | 5,000–6,000 images/s |
| Exported ONNX size | ~52 MB |
I have some other tweaks I’m Gona try and will update the thread with the results but I’m at work rn so have to wait till later that should get me to around the 82% accuracy mark, my script auto optimise to your hardware specs so if you was to run it on your own hardware it would auto tune itself to that.
Is the above any good again as great as these numbers look they don’t mean much to me as it’s still Al quite confusing if anyone has knowledge on this can you let me know if this is any good or is there’s tweaks to improve it
Thank you in advance