Model save

README.md

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+---
+license: apache-2.0
+base_model: microsoft/swinv2-base-patch4-window12-192-22k
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: swinv2-base-patch4-window12-192-22k-ConcreteClassifier-PVT
+  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:
+        accuracy: 0.6160830090791181
+    - name: F1
+      type: f1
+      value:
+        f1: 0.6252886431691335
+    - name: Precision
+      type: precision
+      value:
+        precision: 0.6429213069076691
+    - name: Recall
+      type: recall
+      value:
+        recall: 0.6390705914040895
+---
+
+<!-- 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. -->
+
+# swinv2-base-patch4-window12-192-22k-ConcreteClassifier-PVT
+
+This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-base-patch4-window12-192-22k) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9423
+- Accuracy: {'accuracy': 0.6160830090791181}
+- F1: {'f1': 0.6252886431691335}
+- Precision: {'precision': 0.6429213069076691}
+- Recall: {'recall': 0.6390705914040895}
+
+## 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: 0.001
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 30
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy                          | F1                          | Precision                          | Recall                          |
+|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------:|:---------------------------:|:----------------------------------:|:-------------------------------:|
+| 1.2473        | 1.0   | 1927  | 0.9809          | {'accuracy': 0.5909208819714656}  | {'f1': 0.5910435702573883}  | {'precision': 0.6661654194259725}  | {'recall': 0.6016064778916844}  |
+| 1.7959        | 2.0   | 3854  | 1.8580          | {'accuracy': 0.26848249027237353} | {'f1': 0.17635435227079216} | {'precision': 0.15600300783137744} | {'recall': 0.2748312652287413}  |
+| 1.8437        | 3.0   | 5781  | 1.9243          | {'accuracy': 0.1743190661478599}  | {'f1': 0.1111482743173735}  | {'precision': 0.1516816437642854}  | {'recall': 0.1887752678966561}  |
+| 1.803         | 4.0   | 7708  | 1.9003          | {'accuracy': 0.1846952010376135}  | {'f1': 0.1316379146314846}  | {'precision': 0.1460659091286986}  | {'recall': 0.1928584239028949}  |
+| 1.8121        | 5.0   | 9635  | 1.7386          | {'accuracy': 0.33229571984435796} | {'f1': 0.24134878979912178} | {'precision': 0.2476900084001273}  | {'recall': 0.3378007007551934}  |
+| 1.7984        | 6.0   | 11562 | 1.7322          | {'accuracy': 0.3014267185473411}  | {'f1': 0.2074421859419775}  | {'precision': 0.17325128260235972} | {'recall': 0.31234162186318526} |
+| 1.7385        | 7.0   | 13489 | 1.6964          | {'accuracy': 0.3120622568093385}  | {'f1': 0.2220020856519651}  | {'precision': 0.24821744277779434} | {'recall': 0.3239827067963708}  |
+| 1.578         | 8.0   | 15416 | 1.6265          | {'accuracy': 0.2827496757457847}  | {'f1': 0.20468330816120264} | {'precision': 0.23605832742279897} | {'recall': 0.2983260879145022}  |
+| 1.7397        | 9.0   | 17343 | 1.4545          | {'accuracy': 0.41686121919584956} | {'f1': 0.38005456072209515} | {'precision': 0.38606801407175106} | {'recall': 0.43686617410103795} |
+| 1.9688        | 10.0  | 19270 | 1.5583          | {'accuracy': 0.3761348897535668}  | {'f1': 0.3427169207054117}  | {'precision': 0.3765571735514816}  | {'recall': 0.3992290557525892}  |
+| 1.5298        | 11.0  | 21197 | 1.3465          | {'accuracy': 0.46718547341115435} | {'f1': 0.4188714217488438}  | {'precision': 0.41426640127137376} | {'recall': 0.4948604123766437}  |
+| 1.7964        | 12.0  | 23124 | 2.1154          | {'accuracy': 0.2059662775616083}  | {'f1': 0.15046942523030551} | {'precision': 0.24607297755729998} | {'recall': 0.1965173812427065}  |
+| 1.6734        | 13.0  | 25051 | 1.8294          | {'accuracy': 0.26718547341115434} | {'f1': 0.23757487469054722} | {'precision': 0.2619241634609933}  | {'recall': 0.28087243077801927} |
+| 1.4376        | 14.0  | 26978 | 1.3830          | {'accuracy': 0.4254215304798962}  | {'f1': 0.41718777684401953} | {'precision': 0.4554477836203232}  | {'recall': 0.450319904911865}   |
+| 1.3403        | 15.0  | 28905 | 1.2193          | {'accuracy': 0.4926070038910506}  | {'f1': 0.44282153705015465} | {'precision': 0.5137836037175402}  | {'recall': 0.5162930270137864}  |
+| 1.4806        | 16.0  | 30832 | 1.2119          | {'accuracy': 0.5060959792477302}  | {'f1': 0.49960050025350783} | {'precision': 0.4999152354253829}  | {'recall': 0.5224114833278971}  |
+| 1.2526        | 17.0  | 32759 | 1.1265          | {'accuracy': 0.532295719844358}   | {'f1': 0.5154889606383344}  | {'precision': 0.5564411969038197}  | {'recall': 0.5477056677098556}  |
+| 1.3673        | 18.0  | 34686 | 1.0845          | {'accuracy': 0.5553826199740597}  | {'f1': 0.5594361896393714}  | {'precision': 0.5621230845137732}  | {'recall': 0.5721227129539878}  |
+| 1.241         | 19.0  | 36613 | 1.1499          | {'accuracy': 0.5317769130998703}  | {'f1': 0.5281353810835646}  | {'precision': 0.5775200417239811}  | {'recall': 0.5464467844676986}  |
+| 1.2881        | 20.0  | 38540 | 1.0368          | {'accuracy': 0.5732814526588845}  | {'f1': 0.5822889712524011}  | {'precision': 0.5902075134144437}  | {'recall': 0.5939011675173421}  |
+| 1.1817        | 21.0  | 40467 | 1.0927          | {'accuracy': 0.562905317769131}   | {'f1': 0.5595449409010723}  | {'precision': 0.6241213136239965}  | {'recall': 0.5901142810563812}  |
+| 1.0632        | 22.0  | 42394 | 0.9835          | {'accuracy': 0.5916990920881972}  | {'f1': 0.5977863969792085}  | {'precision': 0.6133139141030883}  | {'recall': 0.6180557435733788}  |
+| 1.3852        | 23.0  | 44321 | 0.9644          | {'accuracy': 0.6108949416342413}  | {'f1': 0.6180791606479925}  | {'precision': 0.6280310097271309}  | {'recall': 0.6298244322284886}  |
+| 0.9998        | 24.0  | 46248 | 1.0270          | {'accuracy': 0.5730220492866407}  | {'f1': 0.5857906297181984}  | {'precision': 0.6037780108566702}  | {'recall': 0.5926557023955539}  |
+| 1.2504        | 25.0  | 48175 | 0.9579          | {'accuracy': 0.6041504539559014}  | {'f1': 0.6089085687671967}  | {'precision': 0.6356079019561951}  | {'recall': 0.6279729594034652}  |
+| 0.9085        | 26.0  | 50102 | 0.9158          | {'accuracy': 0.6256809338521401}  | {'f1': 0.634525318707402}   | {'precision': 0.638920861622337}   | {'recall': 0.6478580836265876}  |
+| 1.0098        | 27.0  | 52029 | 0.9567          | {'accuracy': 0.598443579766537}   | {'f1': 0.6064064760408924}  | {'precision': 0.6229095253118345}  | {'recall': 0.6214139916724217}  |
+| 1.0409        | 28.0  | 53956 | 0.9464          | {'accuracy': 0.611413748378729}   | {'f1': 0.6211699172289087}  | {'precision': 0.6387402688839067}  | {'recall': 0.634161757892515}   |
+| 1.3289        | 29.0  | 55883 | 0.9405          | {'accuracy': 0.6176394293125811}  | {'f1': 0.6271514140166757}  | {'precision': 0.6447687702321006}  | {'recall': 0.6400894012742261}  |
+| 0.8946        | 30.0  | 57810 | 0.9423          | {'accuracy': 0.6160830090791181}  | {'f1': 0.6252886431691335}  | {'precision': 0.6429213069076691}  | {'recall': 0.6390705914040895}  |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.1.0
+- Datasets 2.17.1
+- Tokenizers 0.15.2