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Fine Grained Image Classification On Compcars
Metrics
Accuracy
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| Resnet50 + PMAL | 99.1% | Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle Recognition | |
| ResNet101-swp | 97.6% | Deep CNNs With Spatially Weighted Pooling for Fine-Grained Car Recognition | - |
| Fine-Tuning DARTS | 95.9% | Fine-Tuning DARTS for Image Classification | - |
| Resnet50 + COOC | 95.6% | Fine-Grained Vehicle Classification with Unsupervised Parts Co-occurrence Learning | - |
| A3M | 95.4% | Attribute-Aware Attention Model for Fine-grained Representation Learning | |
| GoogLeNet | 91.2% | A Large-Scale Car Dataset for Fine-Grained Categorization and Verification | |
| AlexNet | 81.9% | A Large-Scale Car Dataset for Fine-Grained Categorization and Verification |
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