ML Research Wiki / Benchmarks / Image Classification / ObjectNet

ObjectNet

Image Classification Benchmark

Performance Over Time

📊 Showing 94 results | 📏 Metric: Top-1 Accuracy

Top Performing Models

Rank Model Paper Top-1 Accuracy Date Code
1 CoCa 📚 CoCa: Contrastive Captioners are Image-Text Foundation Models 82.70 2022-05-04 📦 mlfoundations/open_clip 📦 facebookresearch/multimodal 📦 lucidrains/CoCa-pytorch
2 LiT 📚 LiT: Zero-Shot Transfer with Locked-image text Tuning 82.50 2021-11-15 📦 mlfoundations/open_clip 📦 google-research/vision_transformer 📦 google-research/big_vision 📦 laion-ai/clip_benchmark 📦 eify/clip_benchmark
3 BASIC 📚 Combined Scaling for Zero-shot Transfer Learning 82.30 2021-11-19 -
4 ViT-H/14 📚 An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale 82.10 2020-10-22 📦 huggingface/transformers 📦 labmlai/annotated_deep_learning_paper_implementations 📦 rwightman/pytorch-image-models
5 EVA-02-CLIP-E/14+ 📚 EVA-CLIP: Improved Training Techniques for CLIP at Scale 79.60 2023-03-27 📦 baaivision/eva 📦 PaddlePaddle/PaddleMIX 📦 Yui010206/CREMA 📦 jaehong31/raccoon
6 Baseline (ViT-G/14) 📚 Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time 79.03 2022-03-10 📦 mlfoundations/model-soups 📦 Burf/ModelSoups 📦 facebookresearch/ModelRatatouille
7 Model soups (ViT-G/14) 📚 Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time 78.52 2022-03-10 📦 mlfoundations/model-soups 📦 Burf/ModelSoups 📦 facebookresearch/ModelRatatouille
8 MAWS (ViT-6.5B) 📚 The effectiveness of MAE pre-pretraining for billion-scale pretraining 77.90 2023-03-23 📦 facebookresearch/maws
9 MAWS (ViT-2B) 📚 The effectiveness of MAE pre-pretraining for billion-scale pretraining 75.80 2023-03-23 📦 facebookresearch/maws
10 MAWS (ViT-H) 📚 The effectiveness of MAE pre-pretraining for billion-scale pretraining 72.60 2023-03-23 📦 facebookresearch/maws

All Papers (94)

Class-agnostic Object Detection

2020
ResNet-152 (FRCNN-ag-ad, VOC)