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Talk2DINO-ViTB

lorebianchi98

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Talk2DINOv3-ViTB

lorebianchi98

Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation (ICCV 2025) Luca Barsellotti*   Lorenzo Bianchi*   Nicola Messina   Fabio Carrara   Marcella Cornia   Lorenzo Baraldi   Fabrizio Falchi   Rita Cucchiara Open-Vocabulary Segmentation (OVS) aims at segmenting images from free-form textual concepts without predefined training classes. While existing vision-language models such as CLIP can generate segmentation masks by leveraging coarse spatial information from Vision Transformers, they face challenges in spatial localization due to their global alignment of image and text features. Conversely, self-supervised visual models like DINO excel in fine-grained visual encoding but lack integration with language. To bridge this gap, we present Talk2DINO, a novel hybrid approach that combines the spatial accuracy of DINOv2 with the language understanding of CLIP. Our approach aligns the textual embeddings of CLIP to the patch-level features of DINOv2 through a learned mapping function without the need to fine-tune the underlying backbones. At training time, we exploit the attention maps of DINOv2 to selectively align local visual patches with textual embeddings. We show that the powerful semantic and localization abilities of Talk2DINO can enhance the segmentation process, resulting in more natural and less noisy segmentations, and that our approach can also effectively distinguish foreground objects from the background. Experimental results demonstrate that Talk2DINO achieves state-of-the-art performance across several unsupervised OVS benchmarks.

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This model is a fine-tuned version of nvidia/mit-b5 on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2292 - Mean Iou: 0.6258 - Mean Accuracy: 0.7547 - Overall Accuracy: 0.8256 - Accuracy Background: nan - Accuracy Hat: 0.8561 - Accuracy Hair: 0.8974 - Accuracy Sunglasses: 0.7540 - Accuracy Upper-clothes: 0.8553 - Accuracy Skirt: 0.7026 - Accuracy Pants: 0.8913 - Accuracy Dress: 0.7525 - Accuracy Belt: 0.4251 - Accuracy Left-shoe: 0.6014 - Accuracy Right-shoe: 0.6374 - Accuracy Face: 0.9094 - Accuracy Left-leg: 0.8452 - Accuracy Right-leg: 0.8343 - Accuracy Left-arm: 0.8506 - Accuracy Right-arm: 0.8287 - Accuracy Bag: 0.8232 - Accuracy Scarf: 0.3662 - Iou Background: 0.0 - Iou Hat: 0.7625 - Iou Hair: 0.8171 - Iou Sunglasses: 0.6400 - Iou Upper-clothes: 0.7700 - Iou Skirt: 0.6211 - Iou Pants: 0.7788 - Iou Dress: 0.5512 - Iou Belt: 0.3564 - Iou Left-shoe: 0.5032 - Iou Right-shoe: 0.5381 - Iou Face: 0.8294 - Iou Left-leg: 0.7412 - Iou Right-leg: 0.7591 - Iou Left-arm: 0.7579 - Iou Right-arm: 0.7705 - Iou Bag: 0.7729 - Iou Scarf: 0.2956 The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15

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SegFormer model fine-tuned on ATR dataset for clothes segmentation but can also be used for human segmentation. The dataset on hugging face is called "mattmdjaga/human_parsing_dataset". Labels: 0: "Background", 1: "Hat", 2: "Hair", 3: "Sunglasses", 4: "Upper-clothes", 5: "Skirt", 6: "Pants", 7: "Dress", 8: "Belt", 9: "Left-shoe", 10: "Right-shoe", 11: "Face", 12: "Left-leg", 13: "Right-leg", 14: "Left-arm", 15: "Right-arm", 16: "Bag", 17: "Scarf" Overall Evaluation Metrics: - Evaluation Loss: 0.15 - Mean Accuracy: 0.80 - Mean IoU: 0.69

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roboengine-sam

michaelyuanqwq

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BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ADE20k (an important benchmark for semantic segmentation of images) at resolution 640x640. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei and first released in this repository. Disclaimer: The team releasing BEiT did not write a model card for this model so this model card has been written by the Hugging Face team. The BEiT model is a Vision Transformer (ViT), which is a transformer encoder model (BERT-like). In contrast to the original ViT model, BEiT is pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. The pre-training objective for the model is to predict visual tokens from the encoder of OpenAI's DALL-E's VQ-VAE, based on masked patches. Next, the model was fine-tuned in a supervised fashion on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

MODEL HANDBOOK

读懂模型，再做选择

选型与常见问题
API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

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