DazMashaly/swin-finetuned-food101

README.md

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+---
+license: apache-2.0
+base_model: microsoft/swin-large-patch4-window7-224-in22k
+tags:
+- image-classification
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: swin-finetuned-food101
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: zindi
+      type: imagefolder
+      config: default
+      split: test
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.766589207332817
+---
+
+<!-- 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. -->
+
+# swin-finetuned-food101
+
+This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-large-patch4-window7-224-in22k) on the zindi dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5697
+- Accuracy: 0.7666
+
+## 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: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.7373        | 1.0   | 173  | 0.6503          | 0.7366   |
+| 0.6106        | 2.0   | 347  | 0.5950          | 0.7503   |
+| 0.5135        | 2.99  | 519  | 0.5697          | 0.7666   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.0
+- Tokenizers 0.15.0

all_results.json

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+{
+    "epoch": 2.99,
+    "eval_accuracy": 0.766589207332817,
+    "eval_loss": 0.5696694254875183,
+    "eval_runtime": 266.3661,
+    "eval_samples_per_second": 14.54,
+    "eval_steps_per_second": 0.458
+}

eval_results.json

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+{
+    "epoch": 2.99,
+    "eval_accuracy": 0.766589207332817,
+    "eval_loss": 0.5696694254875183,
+    "eval_runtime": 266.3661,
+    "eval_samples_per_second": 14.54,
+    "eval_steps_per_second": 0.458
+}

runs/Dec23_09-07-40_bf6a584f982d/events.out.tfevents.1703331328.bf6a584f982d.11311.1

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+oid sha256:72c32488cbd5c67b42af2f8e9b7b64f391511b3a52bb7632b60cf7d3d676d507
+size 411