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omUniyal/
cv-multitask-pipeline

Name: cv-multitask-pipeline
Availability: InStock

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CV Multitask Pipeline

Multi-task computer vision model performing simultaneous object classification and bounding box detection on PASCAL VOC 2012 (20 classes).

Model Details

Architecture: Frozen EfficientNet-B0 backbone + custom classification head + custom detection head
Backbone: ImageNet-pretrained EfficientNet-B0 (frozen, 4M params)
Classification head: 667K trainable params, outputs 20-class logits
Detection head: 329K trainable params, outputs normalized bbox coordinates

Training

Dataset: PASCAL VOC 2012 (5,717 train / 5,823 val images)
Epochs: 30 (best checkpoint at epoch 5)
Loss: Weighted multi-task loss (CrossEntropy + SmoothL1, λ_cls=1.0, λ_det=5.0)
Hardware: Google Colab T4 GPU

Results

Metric	Value
Top-1 Accuracy	79.9%
Mean IoU	0.468
IoU@0.5 Accuracy	49.2%

Usage

python

import torch
from huggingface_hub import hf_hub_download

checkpoint_path = hf_hub_download(
    repo_id="OmUniyal/cv-multitask-pipeline",
    filename="best_model.pt"
)
checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=True)

Full model code and inference pipeline: GitHub repo

Limitations

Closed-set classification — no "none of the above" option for out-of-distribution images
Overfitting observed after epoch 5
Single bbox regression — doesn't handle multiple objects per image

Live Demo

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omUniyal/cv-multitask-pipeline