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--- |
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license: apache-2.0 |
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datasets: |
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- imagenet-1k |
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metrics: |
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- accuracy |
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pipeline_tag: image-classification |
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tags: |
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- pytorch |
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- torch-dag |
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--- |
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# Model Card for efficientnetv2_b0_pruned_69 |
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This is a prunned version of the [Keras EfficientNetV2B0](https://keras.io/api/applications/efficientnet_v2/#efficientnetv2b0-function) model in a [toch-dag](https://github.com/TCLResearchEurope/torch-dag) format. |
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This model has rougly 69% of the original model FLOPs with minimal metrics drop. |
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| Model | KMAPPs* | M Parameters | Accuracy (224x224) | |
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| ----------- | ----------- | ----------- | ------------------ | |
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| **Keras EfficientNetV2B (baseline)** | 29 | 7.1 | 78.69% | |
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| **efficientnetv2_b0_pruned_69 (ours)** | 20.05 **(69%)** | 6.9 **(97%)** | 78.62% **(↓ 0.07%)** | |
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\***KMAPPs** thousands of FLOPs per input pixel |
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`KMAPPs(model) = FLOPs(model) / (H * W * 1000)`, where `(H, W)` is the input resolution. |
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The accuracy was calculated on the ImageNet-1k validation dataset. For details about image pre-processing, please refer to the original repository. |
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## Model Details |
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### Model Description |
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- **Developed by:** [TCL Research Europe](https://github.com/TCLResearchEurope/) |
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- **Model type:** Classification / feature backbone |
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- **License:** Apache 2.0 |
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- **Finetuned from model:** [Keras EfficientNetV2B0](https://keras.io/api/applications/efficientnet_v2/#efficientnetv2b0-function) |
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### Model Sources |
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- **Repository:** [Keras EfficientNetV2B0](https://keras.io/api/applications/efficientnet_v2/#efficientnetv2b0-function) |
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## How to Get Started with the Model |
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To load the model, You have to install [torch-dag](https://github.com/TCLResearchEurope/torch-dag#3-installation) library, which can be done using `pip` by |
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``` |
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pip install torch-dag |
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``` |
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then, clone this repository |
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``` |
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# Make sure you have git-lfs installed (https://git-lfs.com) |
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git lfs install |
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git clone https://huggingface.co/TCLResearchEurope/efficientnetv2_b0_pruned_69 |
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``` |
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and now You are ready to load the model: |
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``` |
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import torch_dag |
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import torch |
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model = torch_dag.io.load_dag_from_path('./efficientnetv2_b0_pruned_69') |
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model.eval() |
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out = model(torch.ones(1, 3, 224, 224)) |
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print(out.shape) |
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``` |
