my_awesome_food_model
psk101基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DISCOVER & COMPARE
Explore AI models 1529
VisionHOPE
PSRben基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗clip-ViT-B-32-vision
QdrantONNX port of sentence-transformers/clip-ViT-B-32. This model is intended to be used for image classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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View details ↗resnet50-onnx
QdrantThis model is intended to be used for image classification and similarity searches. You can find the ONNX port implementation here Here's an example of performing inference using the model with FastEmbed.
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View details ↗frost-marten
qhoenix基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗ConvNext-Base
qualcomm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗ConvNext-Tiny
qualcomm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Inception-v3
qualcomm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗MobileNet-v3-Large
qualcomm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗MobileNet-v3-Small
qualcomm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗OpenAI-Clip
qualcommContrastive Language-Image Pre-Training (CLIP) uses a ViT like transformer to get visual features and a causal language model to get the text features. Both the text and visual features can then be used for a variety of zero-shot learning tasks. This is based on the implementation of OpenAI-Clip found here. This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here. Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up to run these models on a hosted Qualcomm® device.
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View details ↗RegNet
qualcomm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Swin-Base
qualcomm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Swin-Small
qualcomm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Swin-Tiny
qualcomm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗swin-tiny-patch4-window7-224-finetuned-mri
raedinkhaledSwin-Tiny fine-tuned to classify cardiac MRI images as `cad` (coronary artery disease) or `healthy`. This checkpoint comes from the co-authored M.Sc. thesis *Novel Deep Learning Architecture for Predicting Heart Diseases based on Transformers and Attention Mechanism with Explainability Model* by Raedin Khaled Sakhri and Loutfi Boufeligha, supervised by Prof. Abdelouaheb Moussaoui (Ferhat Abbas University, Algeria, July 2022). The thesis compared eight models on the same task. This one is the fine-tuned Swin-Tiny, pretrained on ImageNet-1k. The Trainer logged about 6.1 hours of training for the 3 epochs; the thesis rounds this to about 7 hours. For comparison, in the thesis: ViT-Base 98.27%, DeiT 99.01% (overfitting noted), a Vision Transformer trained from scratch 75.73%, and a CNN 88%.
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View details ↗aesthetic-shadow-v2
RE-N-YThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗efficientnet-b0-finetuned-scenario16
RichardsonI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗resnet101-finetuned-scenario16
RichardsonI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗resnet152-finetuned-scenario16
RichardsonI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗resnet18-finetuned-scenario16
RichardsonI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗resnet34-finetuned-scenario16
RichardsonI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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