This is a GGUF quantized version converted from the driaforall/mem-agent model, using the Q4_K_M quantization level. This model focuses on the memory agent function and can process and recall a large amount of context information. It is suitable for dialogue and task processing scenarios that require long-term memory capabilities.

Natural Language ProcessingGguf

This model is a fine-tuned version based on bert-base-uncased, focusing on improving recall performance for specific tasks

Natural Language ProcessingTransformers

Marqo-FashionSigLIP is a multimodal embedding model optimized for fashion product search, achieving a 57% improvement in MRR and recall rate compared to FashionCLIP.

MultimodalTransformersEnglish

A vision-language model trained on the LAION-2B English dataset based on the OpenCLIP framework, supporting zero-shot image classification and cross-modal retrieval

MultimodalSafetensors

Marqo-FashionSigLIP is a multimodal embedding model optimized for fashion product search, with a 57% improvement in MRR and recall rate compared to FashionCLIP.

MultimodalTransformersEnglish

This is a deep - learning model focused on image classification tasks, using accuracy, recall, and F1 score as the main evaluation metrics, and following the Apache 2.0 open - source license.

Computer VisionTransformers

This is a binary classification model trained using AutoTrain, primarily for image classification tasks, with high accuracy and recall rates.

Computer VisionTransformers

This is a binary classification vision model trained using AutoTrain, excelling at distinguishing between two types of images with high accuracy and recall.

Computer VisionTransformers

A vision Transformer model fine-tuned from google/vit-base-patch16-224-in21k, specialized in fire detection tasks, demonstrating high precision and recall rates on the evaluation dataset.

Computer VisionTransformers

This model is a fine-tuned version of facebook/levit-192 on an unlabeled dataset, demonstrating excellent performance in precision, recall, F1 score, and accuracy.

Computer VisionTransformers