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Clip linear probing The feature learned by CLIP is quite strong that freezing half of the layers gets 85. , 2023), Ojha et al. 这三篇论文的精读文章已经很多了，但这篇文章更偏代码一点。如果你需要读懂这篇文章，最好要了解一点CLIP。CLIP简单来说，是基于两个编码器（图像文本），使用对比学习方法训练的一种训练方法（后来基于此方法的模… Jun 5, 2023 · Can be better than few-shot linear probing CLIP models are more robust to natural distribution shifts Limitations Only competitive with a linear classifier on top of ResNet50 features… Far behind state-of-the-art in many tasks Authors estimate a “1000x increase in compute” is necessary to reach state-of-the-art in zero-shot using CLIP Jun 19, 2025 · Encodes both image and text using CLIP encoders. I see some tutorials to add a classification layer for BERT but I don’t see any for CLIP so I was wondering if there’s any specific way to perform linear probing using CLIP with the HF layer? Thank you! Sep 19, 2023 · The LP-CLIP technique offers a promising approach to enhance the robustness of CLIP without the need for annotations. How-ever, its fine-tuning performance on most other tasks are worse than MAE, as shown in Tab. Jun 22, 2024 · In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline. 4 大规模的文本重写 1. 01% ImageNet zero-shot classification improvement. , 2021). It has demonstrated exceptional zero-shot capabilities for image Abstract This paper addresses the challenging problem of open-vocabulary object detection (OVOD) where an object detec-tor must identify both seen and unseen classes in test images without labeled examples of the unseen classes in training. Fine-tuning and linear probing are typical approaches for pre-trained models-based low-shot image classifica-tion [5, 7, 8, 28]. 1. To apply CLIP to a new task, all we need to do is “tell” CLIP’s text-encoder the names of the task’s visual concepts, and it will output a linear classifier of CLIP’s visual representations. 6 % top-1 accuracy, close to the full fine-tuning result. Apr 2, 2024 · In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline. This newly added layer is trained utilizing pseudo-labels produced by CLIP, coupled with a self-training strategy. Comparison between zero-shot and linear-probe CLIP models sharing the same backbone. This blog will guide you through the fundamental concepts, usage methods, common practices, and best practices of linear probing with CLIP on the CIFAR - 10 dataset using PyTorch Feb 24, 2025 · In the next part of this series, we’ll explore how CLIP can be fine-tuned and adapted using linear probing, along with its applications in specialized fields like medical imaging. A. Evaluates using cosine similarity. The prediction performances are then attributed to the knowledge contained in the target model's latent representation rather than to the simple linear probe. It can be instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, similarly to the zero-shot capabilities of GPT-2 and 3. The method works by training a linear classifier on top of frozen features extracted from a CLIP model and evaluating its performance on image classification tasks. Results linear probe scores are provided in Table 3 and plotted in Figure 10. First, the classification performance obtained by tuning the prompt through a pre-trained CLIP model is signifi-cantly more robust to noisy labels than the traditional fine-tuning or linear probing paradigms (see Figure 1). I see some tutorials to add a classification layer for BERT but I don’t see any for CLIP so I was wondering if there’s any specific way to perform linear probing using CLIP with the HF layer? Thank you! for massive labeled data as in traditional cases, helps initiate the data annotation process, and supports the construction of complex recognition systems, among other advantages. 1 CLIP 中的文本输入缺少有效的数据增强 1. The results demonstrate that linear probing, which uses only the pre-trained encoder and a linear classifier, pro-duces results that are only s Combining the above novel designs, we train our ProtoCLIP on Conceptual Captions and achieved an +5. Dec 3, 2023 · End-to-end Fine-tuning (FT) and Linear Probing (LP) are two traditional implementation. Jul 25, 2025 · Thanks to @teasgen for support of validation set and tuning linear probing similar to OpenAI's CLIP. Our proposed approach consistently outperformed these methods in terms of both distributional and out-of-distribution fine-tuning benchmarks. GitHub is where people build software. Linear probing is an evaluation method in the CLIP benchmark system that assesses the quality of visual representations learned by CLIP models. Through our experiments, we found that the initial value of α plays a substantial role in the final performance of the Tip-Adapter-F model. Figure 1. Recently, vision-language models, e. 7 Zero-Shot 分类实验结果 1. In (Ojha et al. 6 实验设置 1. Comparison with supervised models: CLIP is always more computationally efficient → best gain with scaling. 4. This technique involves the distillation of CLIP features through the incorporation of a linear probing layer positioned atop its encoding structure. 8 Few-Shot 分类和 Linear Probing 实验结果 This repository explores the semantic richness of patch-level representations from Vision Transformer (ViT) models—including CLIP, DINO, MAE, and DINOv2 —through linear probing on a semantic segmentation dataset, specifically Pascal VOC 2012. 原理训练后，要评价模型的好坏，通过将最后的一层替换成线性层。 Apr 20, 2023 · Hi, You mention in the paper that for linear probe evaluation For CLIP-ViT models, we used the features before the linear projection to the embedding space, which corresponds to I_f in Figure 3. 28B seen samples. 81% ImageNet linear probing improvement and an +2. , 2020) in detecting deepfakes. Applies early stopping to avoid overfitting. , CLIP [45], have significantly advanced zero In the field of vision-language contrastive learning, models such as CLIP capitalize on matched image-caption pairs as positive ex-amples and leverage within-batch non-matching pairs as negatives. Thanks to @visheratin for multilingual retrieval datasets support from https://arxiv. g. Few-shot classification with CLIP. Our study has revealed several interesting findings. By leveraging a simple linear probing layer, we aim to improve the model’s ability to withstand various uncertainties and challenges commonly encountered in real-world scenarios. A constraint formulation to retain prior knowledge of the robust zero-shot prototypes per class, CLass adaptive Linear Probing (CLAP). However, this method has a Hi, I am super new to HF and I am trying to perform a simple linear probing using CLIP as a baseline. 2 CLIP 的训练目标 1. We did it on the following training setups: linear probing and contrastive fine-tuning of CLIP with ResNet and ViT backbones. arXiv:2202. TL;DR: CLIP projects the visual embeddings to the shared latent space using a linear projection layer. In this work, we propose and exam-ine from convex-optimization perspectives a generalization of the standard LP baseline, in which the linear classifier Sep 10, 2025 · 文章浏览阅读5. Which method does better? Jun 25, 2024 · One of the most prominent and widely used pre-trained vision–language models is OpenAI’s Contrastive Language–Image Pre-training (CLIP) model (Radford et al. With the rise of powerful pre-trained vision-language models like CLIP, the community has started to investigate potential solutions to efficiently adapt these models to downstream datasets and tasks. Nov 25, 2022 · Second, linear probing trains a linear regression on top of the last hidden activations of CLIP's image encoder. Nov 28, 2022 · I’m not an expert, so please take this with a grain of salt, but based on my experience working with OpenAI’s CLIP, fine-tuning pre-trained OpenAI models works via linear probing. included in the Cloppe Apr 12, 2024 · Linear Probing To validate the performance of various encoders in CLIP models, we evaluate them using linear probing and present the results in Figure 14. With the CLIP being proposed, the fine-tuning of language-image pre-training models becomes more flexible and diverse. The list of papers is in To outperform a carefully designed Linear Probing (ZS-LP) baseline, these methods require to optimize their hyperparameters on each target task, which is unrealistic. a) Using a pre-trained CLIP, zero-shot classification is performed by measuring text and visual embeddings similarity. CLIP [Blog] [Paper] [Model Card] [Colab] CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. Among few-shot adaptation strategies of CLIP, b) Linear Probing [19, 38] trains a linear classifier of the visual features, c) Adapter-style tuning adds external learnable MLPs [11, 51], d) Prompt Tuning learns word embeddings [5 Linear-Probe Classification: A Deep Dive into FILIP and SODA | SERP AIhome / posts / linear probe classification Jul 31, 2025 · Notifications You must be signed in to change notification settings Fork 98 We compare SuperClass against CLIP for ImageNet zero-shot (LiT) and linear probing classification, as shown in Table 10. The LP-CLIP technique offers a promising approach to enhance the robust-ness of CLIP without the need for annotations. Linear probing of patch-level representations from ViT-based models (CLIP, DINO, MAE) on a semantic segmentation dataset. LG] 21 Feb 2022 Jan 12, 2024 · 本文目录 1 使用语言重写改进 CLIP 训练 (来自谷歌，MIT) 1 LaCLIP 论文解读 1. It Nov 21, 2023 · 更进一步地，论文还对比了Zero-shot CLIP和ResNet50 linear probing（ImageNet数据上预训练，在加上线性分类层进行finetune）在27个数据集上表现，如下图所示，其中在16个数据集上CLIP可以超过ResNet50（对物体分类比较敏感）。 Nov 28, 2024 · Yet, these SSL models require an additional supervised linear probing step, which relies on fully labeled data which is often expensive and difficult to obtain at scale. This has motivated intensive research building convoluted prompt learning or feature adaptation strategies. Despite CLIP not being trained for these specific tasks, it outperforms a ResNet-50 with a linear probe. However, this method has a critical Mar 14, 2024 · Linear-probing CLIP drops the text encoder and instead attaches a randomly initialized linear layer to the image encoder, and then tunes only this linear layer with downstream training data for domain-adapted classification. 01859. In the code, this can be done very nicely thanks to this line: CLIP/clip/mo erfor-mance in the imbalanced setting. A typical approach for OVOD is to use joint text-image em-beddings of CLIP to assign box proposals to their closest text label. When comparing the two pre-training methods, the CLIP model learns richer semantic information reflected by its su-perior linear probing performance on ImageNet-1K. Support various datasets from torchvision, tensorflow datasets, and VTAB. Main plots can be found in the results section. Third, we fine-tune all parameters of the model on the AVA dataset. 5 使用增强的文本数据训练 CLIP 模型 1. Feb 28, 2024 · To achieve this, we introduce a novel approach named LP-CLIP. Jan 5, 2021 · In contrast, CLIP can be adapted to perform a wide variety of visual classification tasks without needing additional training examples. All experiments were conducted with a batch size of 16k and 1. Nov 14, 2025 · By combining CLIP with linear probing, we can leverage the pre - trained knowledge of CLIP to perform image classification on the CIFAR - 10 dataset effectively. Zero-shot CLIP performs competitively against fully supervised Linear Probe on ResNet50 on a wide array of tasks (wins in 16/27 datasets). We show that the zero-shot classification and retrieval This paper introduces EVA-CLIP-18B, the largest open-source CLIP model with 18-billion parameters to narrow this gap. Mar 1, 2024 · The 0 layer tuning is linear probing and 12 is the full fine-tuning. Trains a linear probing head (optional) or fine-tunes CLIP. org/abs/2309. By leveraging a simple linear probing layer, we aim to improve the model's ability to withstand various uncertainties and challenges commonly encountered in real-world scenarios. A specific modeling of the classifier weights, blending visual prototypes and text embeddings via learnable multipliers, along with convex-optimization ingredients, often overlooked in deep learning practices, led to the surprising results. However, it’s worth noting that zero-shot did not outperform linear probing when given more training samples. . Support for zero-shot classification and zero-shot retrieval, linear probing, and captioning. adapted CLIP for deepfake detection using linear probing, and the results they achieved showed strong generalization capabilities as compared to previous state-of-the-art (Wang et al. Moreover, supervision models may collapse intra-class details → worse performance. Linear probing: evaluating representation learning with linear classifiers instead of end-to-end fine tuning (expensive, many params, masks failures). ProLIP simply fine-tunes this layer with a zero-shot regularization loss. , 2022; Kim et al. e. It can be observed that when the number of sampled data is below 100 million, MLP-Mixer outperforms the other four architectures on both ImageNet and other OOD variants. Linear probing freezes the foundation model and trains a head on top. 作用自监督模型评测方法是测试预训练模型性能的一种方法，又称为linear probing evaluation 2. Support for various multilingual datasets for classification and retrieval Oct 25, 2021 · Using a linear probe, CLIP beats other models in a few-shot context (up to 16 instances), and interestingly its 0-shot approach beats few shots up to 4. The best-performing CLIP model, using ViT-L/14 archiecture and 336-by-336 pixel images, achieved the state of the art in 21 of the 27 datasets, i. In order to systematically organize and understand the development of this field, we summarize awesome Vision Language (VL) Prompt/Finetune/Adapter methods and models. The round markers represent the zero-shot models, and the star markers represent their respective linear-probes. 3 多样化的文本对生成策略 1. Y Jun 17, 2024 · In our experiments ϵ = 3. EVA-CLIP [63] open-sources a series of effective and eficient CLIP models, which have been leveraged as the vision foundation by numerous impactful works across 2D / 3D vision and multimodal modeling [42, 78, 77, 50, 69, 64]. Fine-tuning updates all the parameters of the model. For Imagenet with 1M+ images in the training split it was quite slow and requires huge memory especially considering the hyperparameter sweep for the L2 regularization term (C). , 2021), adapting CLIP through linear probing does not exploit its language component May 3, 2021 · We design AI systems that create real ROI for brands and enterprises through solution development, embedded expertise, and more. Feb 5, 2022 · If I understand correctly when performing linear probing you take the representations before the linear projection heads. Comparison with Baselines We further conducted comparisons with other state-of-the-art approaches like LPFT (Linear Probing Fine-Tuning) and standard linear probing. A revisited zero-shot initialized Linear Probe (ZS-LP), tailored for CLIP-alike vision-language models. In this work, we propose and examine from convex-optimization perspectives a generalization of the standard LP baseline, in which the linear classifier We introduced LP++, a strong linear probe for few-shot CLIP adaptation. Then, we maintain the implementation of the Templated type-safe hashmap implementation in C using open addressing and linear probing for collision resolution. Hence, for a fair comparison, we re-evaluated Tip-Adapter-F* (Table 1) by (i) finding the ini-tial value of αinit ∈ [1, 10] on the small validation set de-ployed for all methods, and (ii) setting βinit = 1. However, as highlighted in (Zhou et al. CLIP is a dual-stream vision–language encoder trained for learning a shared representation space, in which image and text modalities can be jointly embedded. CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image - openai/CLIP Oct 26, 2023 · This paper addresses the challenging problem of open-vocabulary object detection (OVOD) where an object detector must identify both seen and unseen classes in test images without labeled examples of the unseen classes in training. 이러한 Linear Probing 테스트에서 CLIP 모델이 기존 모델들보다 좋은 성능을 냈다는 의미는 그만큼 이미지-자연어 쌍을 Contrastive Learning 으로 학습하는 방법이 우수함을 입증한다고 할 수 있습니다. 10054v1 [cs. Nov 12, 2023 · Hi, we used full-batch linear regression using L-BFGS. Support for OpenCLIP pre-trained models, Japanese CLIP, and NLLB CLIP for general multilingual abilities. Linear probing is a technique where you take the second-to-last layer of a NN (so the layer before the output layer) and further tune the weights from the base model using your datasets. Sep 7, 2021 · Hi, I am super new to HF and I am trying to perform a simple linear probing using CLIP as a baseline. 7k次，点赞10次，收藏40次。本文详细介绍CLIP模型原理，包括对比学习目标、模型结构、训练数据集等，并通过zero-shot推理与linear probe分类任务验证模型性能。 Without losing generalizability, we mainly discuss MAE [17] in this paper. We found CLIP matches the performance of the Apr 5, 2023 · Two standard approaches to using these foundation models are linear probing and fine-tuning. A typical approach for OVOD is to use joint text-image embeddings of CLIP to assign box proposals to their closest text label. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. Linear Probing Linear probing is a simple idea where you train a linear model (probe) to predict a concept from the internals of the interpreted target model. ProLIP is a strong alternative to linear probing, prompt tuning and CLIP-adapters, and is robust to the learning rate. Our experiments show that CLIP is suitable as a base model for IAA methods, since it extracts features related to image aesthetics. Looking at the request Sep 20, 2025 · 【Linear Probing | 线性探测】深度学习线性层 1. This approach has led to remarkable outcomes in zero-shot image classifica-tion, cross-modal retrieval, and linear evaluation tasks.