Bump transformers from 5.1.0 to 5.4.0 in /detector

[dependabot]

Bumps transformers from 5.1.0 to 5.4.0.

Release notes

Sourced from transformers's releases.

Release v5.4.0: PaddlePaddle models 🙌, Mistral 4, PI0, VidEoMT, UVDoc, SLANeXt, Jina Embeddings v3

New Model additions

VidEoMT

Video Encoder-only Mask Transformer (VidEoMT) is a lightweight encoder-only model for online video segmentation built on a plain Vision Transformer (ViT). It eliminates the need for dedicated tracking modules by introducing a lightweight query propagation mechanism that carries information across frames and employs a query fusion strategy that combines propagated queries with temporally-agnostic learned queries. VidEoMT achieves competitive accuracy while being 5x-10x faster than existing approaches, running at up to 160 FPS with a ViT-L backbone.

Links: Documentation | Paper

• Add VidEoMT (#44285) by @​NielsRogge in #44285

UVDoc

UVDoc is a machine learning model designed for document image rectification and correction. The main purpose of this model is to carry out geometric transformation on images to correct document distortion, inclination, perspective deformation and other problems in document images. It provides both single input and batched inference capabilities for processing distorted document images.

Links: Documentation

• [Model] Add UVDoc Model Support (#43385) by @​XingweiDeng in #43385

Jina Embeddings v3

The Jina-Embeddings-v3 is a multilingual, multi-task text embedding model designed for a variety of NLP applications. Based on the XLM-RoBERTa architecture, this model supports Rotary Position Embeddings (RoPE) replacing absolute position embeddings to support long input sequences up to 8192 tokens. Additionally, it features 5 built-in Task-Specific LoRA Adapters that allow the model to generate task-specific embeddings (e.g., for retrieval vs. classification) without increasing inference latency significantly.

Links: Documentation | Paper

• Add Jina-Embeddings-V3 Model (#44251) by @​Sai-Suraj-27 in #44251

Mistral4

Mistral 4 is a powerful hybrid model with the capability of acting as both a general instruction model and a reasoning model. It unifies the capabilities of three different model families - Instruct, Reasoning (previously called Magistral), and Devstral - into a single, unified model. The model features a MoE architecture with 128 experts and 4 active, 119B parameters with 6.5B activated per token, 256k context length, and supports multimodal input with both text and image processing capabilities.

Links: Documentation

• Add Mistral 4 (#44760) by @​juliendenize in #44760

PI0

PI0 is a vision-language-action model for robotics manipulation that jointly processes visual observations and language instructions to generate robot actions. It uses a novel flow matching architecture built on top of a pre-trained vision-language model to inherit Internet-scale semantic knowledge. The model can perform complex dexterous tasks like laundry folding, table cleaning, and assembling boxes across multiple robot platforms including single-arm robots, dual-arm robots, and mobile manipulators.

Links: Documentation | Paper

• Add model lerobot PI0 to transformers (#44160) by @​molbap in #44160

SLANeXt

SLANeXt is a series of dedicated lightweight models for table structure recognition, focusing on accurately recognizing table structures in documents and natural scenes. The SLANeXt series is a new generation of table structure recognition models independently developed by the Baidu PaddlePaddle Vision Team, with dedicated weights trained separately for wired and wireless tables. The recognition ability for all types of tables has been significantly improved, especially for wired tables.

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Commits

• 276f140 v5.4.0
• 16d437e fix: protect torch imports in processing files and fix import guards
• 97b7727 Fix release full (#45029)
• 78bdaf0 Add cohere asr (#45023)
• e895107 docs: add PermuteForRope to conversion operations reverse table (#45035)
• 67100cc [CB] Persistent manager (#44435)
• 69f9d55 Add BC for _further_process_kwargs (#45033)
• e3f7cc3 Use multi runners to check new failing tests in a CI run (#45032)
• 882ffdb [fix] Use the correct _tied_weights_keys for CamembertForCausalLM (#45031)
• d81ad48 change dev ver. we forgot to do this when we released 5.3.0
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