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EstherSan
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car_identified_model_7

Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
mobilevit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
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  1. README.md +178 -12
  2. model.safetensors +1 -1
  3. pytorch_model.bin +1 -1
  4. runs/Nov13_14-14-06_fa108b7cccfa/events.out.tfevents.1699884892.fa108b7cccfa.5895.0 +2 -2
README.md CHANGED
@@ -5,9 +5,28 @@ tags:
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  - generated_from_trainer
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  datasets:
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  - imagefolder
 
 
 
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  model-index:
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  - name: car_identified_model_7
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- results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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
@@ -17,15 +36,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [apple/mobilevitv2-1.0-imagenet1k-256](https://huggingface.co/apple/mobilevitv2-1.0-imagenet1k-256) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - eval_loss: 0.5066
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- - eval_f1: 0.4186
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- - eval_roc_auc: 0.7233
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- - eval_accuracy: 0.1429
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- - eval_runtime: 2.3108
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- - eval_samples_per_second: 18.176
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- - eval_steps_per_second: 1.298
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- - epoch: 228.36
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- - step: 314
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  ## Model description
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@@ -52,11 +66,163 @@ The following hyperparameters were used during training:
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  - total_train_batch_size: 128
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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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- - num_epochs: 400
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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- - Transformers 4.34.1
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  - Pytorch 2.1.0+cu121
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  - Datasets 2.14.6
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  - Tokenizers 0.14.1
 
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  - generated_from_trainer
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  datasets:
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  - imagefolder
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+ metrics:
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+ - f1
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+ - accuracy
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  model-index:
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  - name: car_identified_model_7
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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: imagefolder
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+ type: imagefolder
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+ config: default
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+ split: train
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+ args: default
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+ metrics:
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+ - name: F1
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+ type: f1
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+ value: 0.3628691983122363
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.07142857142857142
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  ---
31
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
36
 
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  This model is a fine-tuned version of [apple/mobilevitv2-1.0-imagenet1k-256](https://huggingface.co/apple/mobilevitv2-1.0-imagenet1k-256) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5755
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+ - F1: 0.3629
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+ - Roc Auc: 0.6990
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+ - Accuracy: 0.0714
 
 
 
 
 
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  ## Model description
45
 
 
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  - total_train_batch_size: 128
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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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+ - num_epochs: 200
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
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+ |:-------------:|:------:|:----:|:---------------:|:------:|:-------:|:--------:|
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+ | 0.6919 | 0.73 | 1 | 0.6887 | 0.1786 | 0.5738 | 0.0 |
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+ | 0.6919 | 1.45 | 2 | 0.6856 | 0.1818 | 0.5761 | 0.0 |
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+ | 0.6919 | 2.91 | 4 | 0.6802 | 0.2116 | 0.6066 | 0.0 |
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+ | 0.6919 | 3.64 | 5 | 0.6800 | 0.1861 | 0.5826 | 0.0 |
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+ | 0.6919 | 4.36 | 6 | 0.6858 | 0.1905 | 0.5973 | 0.0 |
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+ | 0.6919 | 5.82 | 8 | 0.6938 | 0.1549 | 0.5342 | 0.0 |
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+ | 0.6919 | 6.55 | 9 | 0.6917 | 0.1805 | 0.5802 | 0.0 |
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+ | 0.6919 | 8.0 | 11 | 0.6735 | 0.1905 | 0.5932 | 0.0 |
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+ | 0.6919 | 8.73 | 12 | 0.6727 | 0.1952 | 0.6007 | 0.0 |
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+ | 0.6919 | 9.45 | 13 | 0.6698 | 0.2061 | 0.6172 | 0.0 |
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+ | 0.6919 | 10.91 | 15 | 0.6672 | 0.2008 | 0.6092 | 0.0 |
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+ | 0.6919 | 11.64 | 16 | 0.6645 | 0.2092 | 0.6196 | 0.0 |
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+ | 0.6919 | 12.36 | 17 | 0.6646 | 0.2049 | 0.6144 | 0.0 |
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+ | 0.6919 | 13.82 | 19 | 0.6623 | 0.2081 | 0.6167 | 0.0 |
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+ | 0.6919 | 14.55 | 20 | 0.6607 | 0.2078 | 0.6149 | 0.0 |
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+ | 0.6919 | 16.0 | 22 | 0.6585 | 0.2203 | 0.6320 | 0.0 |
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+ | 0.6919 | 16.73 | 23 | 0.6562 | 0.2156 | 0.6219 | 0.0 |
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+ | 0.6919 | 17.45 | 24 | 0.6555 | 0.2182 | 0.6263 | 0.0 |
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+ | 0.6919 | 18.91 | 26 | 0.6522 | 0.2185 | 0.6232 | 0.0 |
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+ | 0.6919 | 19.64 | 27 | 0.6512 | 0.2228 | 0.6273 | 0.0 |
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+ | 0.6919 | 20.36 | 28 | 0.6501 | 0.2356 | 0.6410 | 0.0 |
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+ | 0.6919 | 21.82 | 30 | 0.6477 | 0.2280 | 0.6284 | 0.0 |
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+ | 0.6919 | 22.55 | 31 | 0.6476 | 0.2326 | 0.6343 | 0.0 |
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+ | 0.6919 | 24.0 | 33 | 0.6469 | 0.2408 | 0.6434 | 0.0 |
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+ | 0.6919 | 24.73 | 34 | 0.6432 | 0.2409 | 0.6369 | 0.0 |
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+ | 0.6919 | 25.45 | 35 | 0.6432 | 0.2431 | 0.6408 | 0.0 |
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+ | 0.6919 | 26.91 | 37 | 0.6402 | 0.2486 | 0.6449 | 0.0 |
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+ | 0.6919 | 27.64 | 38 | 0.6386 | 0.2686 | 0.6664 | 0.0 |
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+ | 0.6919 | 28.36 | 39 | 0.6376 | 0.2762 | 0.6796 | 0.0 |
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+ | 0.6919 | 29.82 | 41 | 0.6347 | 0.2692 | 0.6721 | 0.0 |
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+ | 0.6919 | 30.55 | 42 | 0.6339 | 0.2655 | 0.6643 | 0.0 |
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+ | 0.6919 | 32.0 | 44 | 0.6310 | 0.2674 | 0.6630 | 0.0 |
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+ | 0.6919 | 32.73 | 45 | 0.6307 | 0.2789 | 0.6731 | 0.0 |
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+ | 0.6919 | 33.45 | 46 | 0.6291 | 0.2714 | 0.6656 | 0.0 |
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+ | 0.6919 | 34.91 | 48 | 0.6271 | 0.2761 | 0.6659 | 0.0 |
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+ | 0.6919 | 35.64 | 49 | 0.6271 | 0.2687 | 0.6612 | 0.0 |
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+ | 0.6919 | 36.36 | 50 | 0.6277 | 0.2606 | 0.6509 | 0.0 |
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+ | 0.6919 | 37.82 | 52 | 0.6257 | 0.2741 | 0.6620 | 0.0 |
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+ | 0.6919 | 38.55 | 53 | 0.6244 | 0.2892 | 0.6793 | 0.0 |
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+ | 0.6919 | 40.0 | 55 | 0.6203 | 0.2968 | 0.6806 | 0.0 |
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+ | 0.6919 | 40.73 | 56 | 0.6198 | 0.2902 | 0.6770 | 0.0 |
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+ | 0.6919 | 41.45 | 57 | 0.6184 | 0.3023 | 0.6866 | 0.0 |
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+ | 0.6919 | 43.64 | 60 | 0.6147 | 0.3322 | 0.7112 | 0.0 |
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+ | 0.6919 | 44.36 | 61 | 0.6154 | 0.3197 | 0.6954 | 0.0 |
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+ | 0.6919 | 45.82 | 63 | 0.6129 | 0.3016 | 0.6832 | 0.0 |
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+ | 0.6919 | 46.55 | 64 | 0.6112 | 0.3020 | 0.6804 | 0.0 |
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+ | 0.6919 | 48.73 | 67 | 0.6091 | 0.3133 | 0.6923 | 0.0 |
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+ | 0.6919 | 49.45 | 68 | 0.6090 | 0.3265 | 0.7019 | 0.0 |
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+ | 0.6919 | 64.0 | 88 | 0.5975 | 0.3130 | 0.6731 | 0.0238 |
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+ | 0.6919 | 64.73 | 89 | 0.5978 | 0.3271 | 0.6889 | 0.0238 |
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+ | 0.6919 | 65.45 | 90 | 0.5967 | 0.3424 | 0.6951 | 0.0238 |
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+ | 0.6919 | 66.91 | 92 | 0.5973 | 0.3125 | 0.6698 | 0.0238 |
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+ | 0.6919 | 68.36 | 94 | 0.5922 | 0.3373 | 0.6897 | 0.0238 |
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+ | 0.6919 | 69.82 | 96 | 0.5949 | 0.3320 | 0.6843 | 0.0476 |
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+ | 0.6919 | 70.55 | 97 | 0.5959 | 0.3413 | 0.6913 | 0.0476 |
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+ | 0.6919 | 72.73 | 100 | 0.5955 | 0.3333 | 0.6881 | 0.0476 |
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+ | 0.6919 | 75.64 | 104 | 0.5903 | 0.3424 | 0.6951 | 0.0238 |
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+ | 0.6919 | 76.36 | 105 | 0.5890 | 0.3411 | 0.6946 | 0.0476 |
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+ | 0.6919 | 77.82 | 107 | 0.5922 | 0.3346 | 0.6887 | 0.0476 |
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+ | 0.6919 | 78.55 | 108 | 0.5923 | 0.3243 | 0.6812 | 0.0476 |
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+ | 0.6919 | 80.0 | 110 | 0.5908 | 0.3468 | 0.6933 | 0.0476 |
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+ | 0.6919 | 80.73 | 111 | 0.5922 | 0.328 | 0.6793 | 0.0476 |
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+ | 0.6919 | 84.36 | 116 | 0.5841 | 0.3465 | 0.6967 | 0.0238 |
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+ | 0.6919 | 85.82 | 118 | 0.5841 | 0.3568 | 0.6969 | 0.0714 |
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+ | 0.6919 | 96.0 | 132 | 0.5803 | 0.3675 | 0.7006 | 0.0714 |
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+ | 0.6919 | 96.73 | 133 | 0.5825 | 0.3519 | 0.6881 | 0.0714 |
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+ | 0.6919 | 109.82 | 151 | 0.5760 | 0.3445 | 0.6856 | 0.0714 |
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+ | 0.6919 | 110.55 | 152 | 0.5757 | 0.3559 | 0.6931 | 0.0714 |
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+ | 0.6919 | 138.91 | 191 | 0.5740 | 0.3529 | 0.6920 | 0.0952 |
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+ | 0.6919 | 139.64 | 192 | 0.5740 | 0.3660 | 0.7001 | 0.0714 |
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+ | 0.6919 | 141.82 | 195 | 0.5740 | 0.3675 | 0.7006 | 0.0714 |
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+ | 0.6919 | 142.55 | 196 | 0.5721 | 0.3707 | 0.7016 | 0.0714 |
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+ | 0.6919 | 144.0 | 198 | 0.5725 | 0.3767 | 0.6998 | 0.0714 |
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+ | 0.6919 | 144.73 | 199 | 0.5734 | 0.3729 | 0.7060 | 0.0952 |
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+ | 0.6919 | 145.45 | 200 | 0.5755 | 0.3629 | 0.6990 | 0.0714 |
221
+
222
 
223
  ### Framework versions
224
 
225
+ - Transformers 4.35.0
226
  - Pytorch 2.1.0+cu121
227
  - Datasets 2.14.6
228
  - Tokenizers 0.14.1
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