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habibi26
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document-crop

Image Classification
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clip
Generated from Trainer
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  1. README.md +25 -31
  2. model.safetensors +1 -1
  3. runs/Jul24_11-08-28_gurame007/events.out.tfevents.1721794110.gurame007.76562.0 +2 -2
README.md CHANGED
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  ---
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- base_model: openai/clip-vit-base-patch32
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  tags:
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  - generated_from_trainer
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  datasets:
@@ -21,7 +20,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9120879120879121
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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
@@ -29,10 +28,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # document-crop
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- This model is a fine-tuned version of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2842
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- - Accuracy: 0.9121
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  ## Model description
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@@ -60,37 +59,32 @@ The following hyperparameters were used during training:
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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: 25
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|
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- | No log | 0.9655 | 7 | 0.7309 | 0.5495 |
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- | No log | 1.9310 | 14 | 0.6289 | 0.5934 |
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- | 0.6408 | 2.8966 | 21 | 0.5930 | 0.6923 |
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- | 0.6408 | 4.0 | 29 | 0.8962 | 0.6154 |
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- | 0.4193 | 4.9655 | 36 | 0.4717 | 0.8022 |
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- | 0.4193 | 5.9310 | 43 | 0.4143 | 0.8242 |
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- | 0.3622 | 6.8966 | 50 | 0.4415 | 0.8242 |
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- | 0.3622 | 8.0 | 58 | 0.3617 | 0.8462 |
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- | 0.2049 | 8.9655 | 65 | 0.4250 | 0.8352 |
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- | 0.2049 | 9.9310 | 72 | 0.4062 | 0.8462 |
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- | 0.1712 | 10.8966 | 79 | 0.4120 | 0.8242 |
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- | 0.1712 | 12.0 | 87 | 0.5777 | 0.8132 |
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- | 0.1291 | 12.9655 | 94 | 0.3530 | 0.8571 |
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- | 0.1291 | 13.9310 | 101 | 0.3432 | 0.8571 |
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- | 0.0836 | 14.8966 | 108 | 0.2558 | 0.8901 |
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- | 0.0836 | 16.0 | 116 | 0.4536 | 0.8352 |
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- | 0.1053 | 16.9655 | 123 | 0.4638 | 0.8462 |
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- | 0.1053 | 17.9310 | 130 | 0.5922 | 0.8352 |
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- | 0.0734 | 18.8966 | 137 | 0.3474 | 0.8681 |
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- | 0.0734 | 20.0 | 145 | 0.5511 | 0.8462 |
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- | 0.046 | 20.9655 | 152 | 0.5115 | 0.8462 |
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- | 0.046 | 21.9310 | 159 | 0.3097 | 0.9011 |
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- | 0.0139 | 22.8966 | 166 | 0.2511 | 0.9121 |
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- | 0.0139 | 24.0 | 174 | 0.2863 | 0.9121 |
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- | 0.0139 | 24.1379 | 175 | 0.2842 | 0.9121 |
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  ### Framework versions
 
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  ---
 
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  tags:
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  - generated_from_trainer
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  datasets:
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9340659340659341
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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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  # document-crop
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+ This model was trained from scratch on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2057
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+ - Accuracy: 0.9341
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  ## Model description
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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: 20
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|
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+ | No log | 0.9655 | 7 | 0.3032 | 0.9121 |
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+ | No log | 1.9310 | 14 | 0.3624 | 0.8242 |
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+ | 0.48 | 2.8966 | 21 | 0.3741 | 0.8352 |
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+ | 0.48 | 4.0 | 29 | 0.2343 | 0.8901 |
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+ | 0.3749 | 4.9655 | 36 | 0.2027 | 0.9121 |
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+ | 0.3749 | 5.9310 | 43 | 0.1904 | 0.9451 |
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+ | 0.2323 | 6.8966 | 50 | 0.1959 | 0.9121 |
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+ | 0.2323 | 8.0 | 58 | 0.2905 | 0.8901 |
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+ | 0.1447 | 8.9655 | 65 | 0.3999 | 0.9011 |
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+ | 0.1447 | 9.9310 | 72 | 0.1072 | 0.9670 |
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+ | 0.125 | 10.8966 | 79 | 0.2654 | 0.9011 |
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+ | 0.125 | 12.0 | 87 | 0.1799 | 0.9451 |
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+ | 0.1979 | 12.9655 | 94 | 0.2546 | 0.9121 |
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+ | 0.1979 | 13.9310 | 101 | 0.2576 | 0.9011 |
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+ | 0.0633 | 14.8966 | 108 | 0.1996 | 0.9341 |
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+ | 0.0633 | 16.0 | 116 | 0.1824 | 0.9560 |
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+ | 0.0311 | 16.9655 | 123 | 0.1834 | 0.9560 |
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+ | 0.0311 | 17.9310 | 130 | 0.2770 | 0.9231 |
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+ | 0.0154 | 18.8966 | 137 | 0.2113 | 0.9451 |
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+ | 0.0154 | 19.3103 | 140 | 0.2057 | 0.9341 |
 
 
 
 
 
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  ### Framework versions
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