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internal.swin-base-food101-int8-structured40

Image Classification
Transformers
PyTorch
OpenVINO
swin
vision
Generated from Trainer
Eval Results
Inference Endpoints
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - image-classification
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+ - vision
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+ - generated_from_trainer
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+ datasets:
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+ - food101
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: swin-food101-jpqd
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+ results:
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+ - task:
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+ name: Image Classification
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+ type: image-classification
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+ dataset:
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+ name: food101
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+ type: food101
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+ config: default
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+ split: validation
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9055049504950495
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # swin-food101-jpqd
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+
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+ This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the food101 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3497
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+ - Accuracy: 0.9055
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+
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+ This model is quantized. Structured sparsity in transformer linear layers: 40%.
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 128
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 64
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 10.0
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 2.2676 | 0.42 | 500 | 2.1087 | 0.7947 |
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+ | 0.6823 | 0.84 | 1000 | 0.5127 | 0.8818 |
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+ | 0.816 | 1.27 | 1500 | 0.3944 | 0.8954 |
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+ | 0.5272 | 1.69 | 2000 | 0.3310 | 0.9050 |
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+ | 12.263 | 2.11 | 2500 | 12.0040 | 0.9057 |
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+ | 48.9519 | 2.54 | 3000 | 48.4500 | 0.8597 |
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+ | 75.576 | 2.96 | 3500 | 75.5765 | 0.6951 |
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+ | 93.7523 | 3.38 | 4000 | 93.3753 | 0.5992 |
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+ | 103.7155 | 3.8 | 4500 | 103.5301 | 0.5622 |
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+ | 107.7993 | 4.23 | 5000 | 108.0881 | 0.5636 |
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+ | 109.6831 | 4.65 | 5500 | 109.2205 | 0.5844 |
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+ | 1.8848 | 5.07 | 6000 | 0.9807 | 0.8315 |
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+ | 1.0668 | 5.49 | 6500 | 0.6050 | 0.8740 |
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+ | 0.7951 | 5.92 | 7000 | 0.5151 | 0.8838 |
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+ | 0.7402 | 6.34 | 7500 | 0.4843 | 0.8906 |
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+ | 0.7319 | 6.76 | 8000 | 0.4494 | 0.8933 |
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+ | 0.5683 | 7.19 | 8500 | 0.4378 | 0.8953 |
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+ | 0.496 | 7.61 | 9000 | 0.4115 | 0.8981 |
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+ | 0.6174 | 8.03 | 9500 | 0.3952 | 0.9005 |
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+ | 0.4921 | 8.45 | 10000 | 0.3765 | 0.9026 |
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+ | 0.5843 | 8.88 | 10500 | 0.3678 | 0.9035 |
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+ | 0.5485 | 9.3 | 11000 | 0.3576 | 0.9039 |
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+ | 0.4337 | 9.72 | 11500 | 0.3512 | 0.9057 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.0
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+ - Pytorch 1.13.1+cu116
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+ - Datasets 2.8.0
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+ - Tokenizers 0.13.2