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 'efficientvit_m5.r224_in1k', on 2025-01-20 05:31:49 UTC
Browse files- README.md +4 -3
- efficientvit_m5.r224_in1k/meta.json +3 -0
- efficientvit_m5.r224_in1k/model.onnx +3 -0
- efficientvit_m5.r224_in1k/preprocess.json +3 -0
- models.parquet +2 -2
README.md
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- timm/resnet50_clip_gap.openai
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# Models
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## Beit
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## EfficientVitMsra
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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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| [efficientvit_m4.r224_in1k](https://huggingface.co/timm/efficientvit_m4.r224_in1k) | 8.8M | 301.8M | 224 | True | 384 | 1000 | imagenet-1k | EfficientVitMsra | efficientvit_m4 | 2023-08-18 |
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| [efficientvit_m2.r224_in1k](https://huggingface.co/timm/efficientvit_m2.r224_in1k) | 4.2M | 203.3M | 224 | True | 224 | 1000 | imagenet-1k | EfficientVitMsra | efficientvit_m2 | 2023-08-18 |
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- timm/efficientvit_m4.r224_in1k
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- timm/efficientvit_m5.r224_in1k
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- timm/fastvit_ma36.apple_dist_in1k
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- timm/fastvit_s12.apple_dist_in1k
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- timm/fastvit_t12.apple_in1k
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- timm/repvit_m0_9.dist_300e_in1k
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- timm/repvit_m2_3.dist_300e_in1k
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- timm/resnet50_clip_gap.openai
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# Models
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268 models exported from TIMM in total.
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## Beit
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## EfficientVitMsra
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3 models with model class `EfficientVitMsra`.
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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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| [efficientvit_m5.r224_in1k](https://huggingface.co/timm/efficientvit_m5.r224_in1k) | 12.5M | 525.4M | 224 | True | 384 | 1000 | imagenet-1k | EfficientVitMsra | efficientvit_m5 | 2023-08-18 |
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| [efficientvit_m4.r224_in1k](https://huggingface.co/timm/efficientvit_m4.r224_in1k) | 8.8M | 301.8M | 224 | True | 384 | 1000 | imagenet-1k | EfficientVitMsra | efficientvit_m4 | 2023-08-18 |
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| [efficientvit_m2.r224_in1k](https://huggingface.co/timm/efficientvit_m2.r224_in1k) | 4.2M | 203.3M | 224 | True | 224 | 1000 | imagenet-1k | EfficientVitMsra | efficientvit_m2 | 2023-08-18 |
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efficientvit_m5.r224_in1k/meta.json
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version https://git-lfs.github.com/spec/v1
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size 169817
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efficientvit_m5.r224_in1k/model.onnx
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efficientvit_m5.r224_in1k/preprocess.json
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size 734
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models.parquet
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