kaushik3009/industrial-vision-agent

Industrial Vision Agent — Model Weights

Trained checkpoints for an agentic multimodal quality-inspection system on metal castings (7,347 images, binary defect classification + weakly-supervised segmentation).

File	Architecture	Role	Verified metric
vit_tiny_patch16_224_best.pth	ViT-Tiny (timm), fine-tuned	Defect classifier	Test F1 0.9967 / Recall 0.9956
resnet18_best.pth	ResNet18, linear-probed head	Grad-CAM localization backbone	Test F1 0.924
mobilenetv3_small_050_best.pth	MobileNetV3-small distilled student	Edge classifier (94.9% fewer params)	Test F1 0.991
unet_pseudo_best.pth	U-Net trained on Grad-CAM pseudo-masks	Segmentation (224x224 mask)	Val Dice 0.706
metrics.json	—	Full training/test results	—

INT8 dynamic quantization of the ViT shrinks it 71.7% (22.2 -> 6.3 MB).

Quick load

import torch, timm
from huggingface_hub import hf_hub_download

weights = hf_hub_download("kaushik3009/industrial-vision-agent", "vit_tiny_patch16_224_best.pth")
model = timm.create_model("vit_tiny_patch16_224", pretrained=False, num_classes=2)
model.load_state_dict(torch.load(weights, map_location="cpu"))

Labels: 0 = OK, 1 = Defective. Input: RGB, resized 224x224, ImageNet-normalized.

Full pipeline, agent code, and docs: https://github.com/kaushik-3009/industrial-vision-agent