Instructions to use deepghs/timms with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use deepghs/timms with timm:
import timm model = timm.create_model("hf_hub:deepghs/timms", pretrained=True) - Notebooks
- Google Colab
- Kaggle
narugo1992 commited on
Export model 'resnetv2_50x3_bit.goog_in21k_ft_in1k', on 2025-01-21 12:57:25 JST
Browse files
README.md
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# Models
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## Beit
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## ResNetV2
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| Name | Params | Flops | Input Size | Can Classify | Features | Classes | Dataset | Model | Architecture | Created At |
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|:-----------------------------------------------------------------------------------------------------------|:---------|:--------|-------------:|:---------------|-----------:|----------:|:------------------|:---------|:-------------------|:-------------|
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| [resnetv2_18.ra4_e3600_r224_in1k](https://huggingface.co/timm/resnetv2_18.ra4_e3600_r224_in1k) | 11.7M | 1.8G | 224 | True | 512 | 1000 | imagenet-1k | ResNetV2 | resnetv2_18 | 2024-11-01 |
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| [resnetv2_101x1_bit.goog_in21k](https://huggingface.co/timm/resnetv2_101x1_bit.goog_in21k) | 44.8M | 44.8M | 224 | True | 2048 | 21843 | imagenet-21k-goog | ResNetV2 | resnetv2_101x1_bit | 2023-03-23 |
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| [resnetv2_50x1_bit.goog_in21k](https://huggingface.co/timm/resnetv2_50x1_bit.goog_in21k) | 44.8M | 44.8M | 224 | True | 2048 | 21843 | imagenet-21k-goog | ResNetV2 | resnetv2_50x1_bit | 2023-03-23 |
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| [resnetv2_101x1_bit.goog_in21k_ft_in1k](https://huggingface.co/timm/resnetv2_101x1_bit.goog_in21k_ft_in1k) | 2.0M | 2.5M | 448 | True | 2048 | 1000 | imagenet-1k | ResNetV2 | resnetv2_101x1_bit | 2023-03-23 |
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| [resnetv2_50x1_bit.goog_in21k_ft_in1k](https://huggingface.co/timm/resnetv2_50x1_bit.goog_in21k_ft_in1k) | 2.0M | 2.5M | 448 | True | 2048 | 1000 | imagenet-1k | ResNetV2 | resnetv2_50x1_bit | 2023-03-23 |
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| [resnetv2_50x1_bit.goog_distilled_in1k](https://huggingface.co/timm/resnetv2_50x1_bit.goog_distilled_in1k) | 2.0M | 2.2M | 224 | True | 2048 | 1000 | imagenet-1k | ResNetV2 | resnetv2_50x1_bit | 2023-03-23 |
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# Models
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1013 models exported from TIMM in total.
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## Beit
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## ResNetV2
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15 models with model class `ResNetV2`.
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| Name | Params | Flops | Input Size | Can Classify | Features | Classes | Dataset | Model | Architecture | Created At |
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|:-----------------------------------------------------------------------------------------------------------|:---------|:--------|-------------:|:---------------|-----------:|----------:|:------------------|:---------|:-------------------|:-------------|
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| 1199 |
| [resnetv2_18.ra4_e3600_r224_in1k](https://huggingface.co/timm/resnetv2_18.ra4_e3600_r224_in1k) | 11.7M | 1.8G | 224 | True | 512 | 1000 | imagenet-1k | ResNetV2 | resnetv2_18 | 2024-11-01 |
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| 1200 |
| [resnetv2_101x1_bit.goog_in21k](https://huggingface.co/timm/resnetv2_101x1_bit.goog_in21k) | 44.8M | 44.8M | 224 | True | 2048 | 21843 | imagenet-21k-goog | ResNetV2 | resnetv2_101x1_bit | 2023-03-23 |
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| [resnetv2_50x1_bit.goog_in21k](https://huggingface.co/timm/resnetv2_50x1_bit.goog_in21k) | 44.8M | 44.8M | 224 | True | 2048 | 21843 | imagenet-21k-goog | ResNetV2 | resnetv2_50x1_bit | 2023-03-23 |
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| [resnetv2_50x3_bit.goog_in21k_ft_in1k](https://huggingface.co/timm/resnetv2_50x3_bit.goog_in21k_ft_in1k) | 6.1M | 7.4M | 448 | True | 6144 | 1000 | imagenet-1k | ResNetV2 | resnetv2_50x3_bit | 2023-03-23 |
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| 1203 |
| [resnetv2_101x1_bit.goog_in21k_ft_in1k](https://huggingface.co/timm/resnetv2_101x1_bit.goog_in21k_ft_in1k) | 2.0M | 2.5M | 448 | True | 2048 | 1000 | imagenet-1k | ResNetV2 | resnetv2_101x1_bit | 2023-03-23 |
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| [resnetv2_50x1_bit.goog_in21k_ft_in1k](https://huggingface.co/timm/resnetv2_50x1_bit.goog_in21k_ft_in1k) | 2.0M | 2.5M | 448 | True | 2048 | 1000 | imagenet-1k | ResNetV2 | resnetv2_50x1_bit | 2023-03-23 |
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| [resnetv2_50x1_bit.goog_distilled_in1k](https://huggingface.co/timm/resnetv2_50x1_bit.goog_distilled_in1k) | 2.0M | 2.2M | 224 | True | 2048 | 1000 | imagenet-1k | ResNetV2 | resnetv2_50x1_bit | 2023-03-23 |
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models.parquet
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resnetv2_50x3_bit.goog_in21k_ft_in1k/meta.json
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resnetv2_50x3_bit.goog_in21k_ft_in1k/model.onnx
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resnetv2_50x3_bit.goog_in21k_ft_in1k/preprocess.json
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