Image Data Labeler and Trainer - 'Bird or Forest' CNN Model
ImageClassification
Collected and curated image datasets for a binary image classification task distinguishing between birds and forests. Labeled image data to train a ResNet-18 CNN model using Fast AI, ensuring accuracy and clean labeling practices. Achieved 0% validation error indicating high-quality and consistent labels.
• Managed data split for training and validation sets.
• Ensured correct annotation according to project guidelines.
• Collaborated on dataset curation and preprocessing.
• Supported model evaluation with consistently labeled image samples.
Collected and curated image datasets for a binary image classification task distinguishing between birds and forests. Labeled image data to train a ResNet-18 CNN model using Fast AI, ensuring accuracy and clean labeling practices. Achieved 0% validation error indicating high-quality and consistent labels. • Managed data split for training and validation sets. • Ensured correct annotation according to project guidelines. • Collaborated on dataset curation and preprocessing. • Supported model evaluation with consistently labeled image samples.
2025 - 2025