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meat_calssify_fresh_crop_V_0_1

Image Classification
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  1. README.md +125 -0
  2. config.json +34 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +36 -0
  5. runs/Jun07_15-50-42_DESKTOP-QA5IM1O/events.out.tfevents.1717746643.DESKTOP-QA5IM1O.20116.0 +3 -0
  6. runs/Jun07_15-53-59_DESKTOP-QA5IM1O/events.out.tfevents.1717746839.DESKTOP-QA5IM1O.18436.0 +3 -0
  7. runs/Jun07_16-45-23_DESKTOP-QA5IM1O/events.out.tfevents.1717749923.DESKTOP-QA5IM1O.12832.0 +3 -0
  8. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-224-in21k
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+ tags:
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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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+ - accuracy
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+ model-index:
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+ - name: meat_calssify_fresh_crop_V_0_1
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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: Accuracy
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+ type: accuracy
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+ value: 0.7741935483870968
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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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+ # meat_calssify_fresh_crop_V_0_1
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.5238
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+ - Accuracy: 0.7742
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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: 1
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+ - eval_batch_size: 1
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+ - seed: 42
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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: 50
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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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+ | 0.9291 | 1.0 | 617 | 1.0099 | 0.5613 |
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+ | 1.0473 | 2.0 | 1234 | 1.2180 | 0.4323 |
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+ | 0.7998 | 3.0 | 1851 | 1.0517 | 0.5548 |
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+ | 1.21 | 4.0 | 2468 | 1.3208 | 0.4968 |
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+ | 1.0814 | 5.0 | 3085 | 1.3423 | 0.5290 |
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+ | 1.4674 | 6.0 | 3702 | 1.5941 | 0.5419 |
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+ | 0.0633 | 7.0 | 4319 | 1.6778 | 0.5548 |
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+ | 0.8345 | 8.0 | 4936 | 1.4949 | 0.5419 |
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+ | 1.6888 | 9.0 | 5553 | 1.6784 | 0.4581 |
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+ | 1.0997 | 10.0 | 6170 | 1.9437 | 0.5097 |
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+ | 1.6235 | 11.0 | 6787 | 1.0796 | 0.6194 |
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+ | 1.3159 | 12.0 | 7404 | 1.7363 | 0.5935 |
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+ | 0.5513 | 13.0 | 8021 | 1.6918 | 0.6387 |
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+ | 0.4463 | 14.0 | 8638 | 1.8225 | 0.5742 |
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+ | 0.0455 | 15.0 | 9255 | 1.6463 | 0.6258 |
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+ | 0.5906 | 16.0 | 9872 | 2.0199 | 0.6065 |
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+ | 0.6373 | 17.0 | 10489 | 1.7233 | 0.6516 |
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+ | 1.0226 | 18.0 | 11106 | 1.4137 | 0.6710 |
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+ | 1.0566 | 19.0 | 11723 | 1.3841 | 0.7097 |
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+ | 0.3117 | 20.0 | 12340 | 1.9925 | 0.6258 |
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+ | 0.3041 | 21.0 | 12957 | 1.6802 | 0.6710 |
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+ | 1.0978 | 22.0 | 13574 | 1.8269 | 0.6323 |
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+ | 1.0631 | 23.0 | 14191 | 1.7333 | 0.6645 |
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+ | 0.0039 | 24.0 | 14808 | 1.8621 | 0.6645 |
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+ | 0.0018 | 25.0 | 15425 | 2.1279 | 0.6258 |
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+ | 0.2373 | 26.0 | 16042 | 1.6896 | 0.7032 |
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+ | 0.5993 | 27.0 | 16659 | 1.8411 | 0.6710 |
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+ | 0.0021 | 28.0 | 17276 | 1.9047 | 0.6645 |
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+ | 0.0401 | 29.0 | 17893 | 1.7597 | 0.7226 |
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+ | 0.3687 | 30.0 | 18510 | 2.2599 | 0.6387 |
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+ | 0.2841 | 31.0 | 19127 | 2.0735 | 0.6581 |
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+ | 0.5707 | 32.0 | 19744 | 1.9316 | 0.6581 |
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+ | 0.46 | 33.0 | 20361 | 2.1935 | 0.6452 |
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+ | 0.7589 | 34.0 | 20978 | 1.5770 | 0.7355 |
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+ | 0.0008 | 35.0 | 21595 | 1.7779 | 0.7226 |
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+ | 0.0005 | 36.0 | 22212 | 2.0994 | 0.6774 |
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+ | 0.0114 | 37.0 | 22829 | 1.7210 | 0.7161 |
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+ | 0.4071 | 38.0 | 23446 | 2.2015 | 0.6839 |
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+ | 0.0007 | 39.0 | 24063 | 1.8165 | 0.7161 |
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+ | 0.0005 | 40.0 | 24680 | 1.8461 | 0.7032 |
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+ | 0.0005 | 41.0 | 25297 | 1.9554 | 0.7032 |
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+ | 0.7269 | 42.0 | 25914 | 1.6843 | 0.7355 |
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+ | 0.0008 | 43.0 | 26531 | 1.9346 | 0.7032 |
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+ | 0.5102 | 44.0 | 27148 | 1.7949 | 0.7161 |
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+ | 1.1679 | 45.0 | 27765 | 1.7431 | 0.7484 |
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+ | 0.151 | 46.0 | 28382 | 1.7899 | 0.7355 |
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+ | 0.0004 | 47.0 | 28999 | 1.5098 | 0.7548 |
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+ | 0.0003 | 48.0 | 29616 | 1.7388 | 0.7419 |
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+ | 0.0003 | 49.0 | 30233 | 1.5232 | 0.7677 |
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+ | 0.0003 | 50.0 | 30850 | 1.5238 | 0.7742 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.2
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+ - Pytorch 2.3.0
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+ - Datasets 2.19.2
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+ - Tokenizers 0.19.1
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+ "ViTForImageClassification"
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+ "encoder_stride": 16,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.0,
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+ "id2label": {
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+ "0": "fresh1",
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+ "1": "fresh2",
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+ "2": "fresh3"
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+ "fresh2": "1",
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+ "fresh3": "2"
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+ "patch_size": 16,
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+ "problem_type": "single_label_classification",
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+ "qkv_bias": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.41.2"
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+ }
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