End of training

README.md

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+---
+license: apache-2.0
+base_model: facebook/convnextv2-tiny-1k-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- precision
+model-index:
+- name: convnextv2-tiny-1k-224-finetuned-crop-neck-style
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8127853881278538
+    - name: Precision
+      type: precision
+      value: 0.8388735739429154
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# convnextv2-tiny-1k-224-finetuned-crop-neck-style
+
+This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7591
+- Accuracy: 0.8128
+- Precision: 0.8389
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 10
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 100
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|
+| No log        | 1.0   | 88   | 0.7865          | 0.7717   | 0.8100    |
+| No log        | 2.0   | 176  | 0.7591          | 0.8128   | 0.8389    |
+| No log        | 3.0   | 264  | 0.8601          | 0.7854   | 0.8206    |
+| No log        | 4.0   | 352  | 0.8518          | 0.8082   | 0.8454    |
+| No log        | 5.0   | 440  | 0.7959          | 0.8128   | 0.8295    |
+
+
+### Framework versions
+
+- Transformers 4.44.0
+- Pytorch 2.4.0
+- Datasets 2.21.0
+- Tokenizers 0.19.1

all_results.json

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+{
+    "epoch": 5.0,
+    "eval_accuracy": 0.8127853881278538,
+    "eval_loss": 0.7590766549110413,
+    "eval_precision": 0.8388735739429154,
+    "eval_runtime": 2.3748,
+    "eval_samples_per_second": 92.217,
+    "eval_steps_per_second": 23.16
+}

config.json

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+{
+  "_name_or_path": "facebook/convnextv2-tiny-1k-224",
+  "architectures": [
+    "ConvNextV2ForImageClassification"
+  ],
+  "depths": [
+    3,
+    3,
+    9,
+    3
+  ],
+  "drop_path_rate": 0.0,
+  "hidden_act": "gelu",
+  "hidden_sizes": [
+    96,
+    192,
+    384,
+    768
+  ],
+  "id2label": {
+    "0": "bell sleeve",
+    "1": "half sleeve",
+    "2": "long sleeve",
+    "3": "three-quarter sleeves"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "bell sleeve": 0,
+    "half sleeve": 1,
+    "long sleeve": 2,
+    "three-quarter sleeves": 3
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "convnextv2",
+  "num_channels": 3,
+  "num_stages": 4,
+  "out_features": [
+    "stage4"
+  ],
+  "out_indices": [
+    4
+  ],
+  "patch_size": 4,
+  "problem_type": "single_label_classification",
+  "stage_names": [
+    "stem",
+    "stage1",
+    "stage2",
+    "stage3",
+    "stage4"
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.0"
+}

eval_results.json

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+{
+    "epoch": 5.0,
+    "eval_accuracy": 0.8127853881278538,
+    "eval_loss": 0.7590766549110413,
+    "eval_precision": 0.8388735739429154,
+    "eval_runtime": 2.3748,
+    "eval_samples_per_second": 92.217,
+    "eval_steps_per_second": 23.16
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c8e09aef4ae66b96f533d7035fc750dca08d1d1fcf53da5801240456436713b5
+size 111501976

preprocessor_config.json

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+{
+  "crop_pct": 0.875,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "ConvNextImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  }
+}

training_args.bin

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