Create README.md

README.md

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+---
+license: mit
+datasets:
+- issai/Central_Asian_Food_Dataset
+language:
+- en
+metrics:
+- accuracy
+- F1
+base_model:
+- microsoft/resnet-50
+pipeline_tag: image-classification
+tags:
+- classification
+- image
+- pytorch
+- safetensors
+- ResNet
+library_name: transformers
+---
+
+# ResNet-50 Model for Central Asian Image Classification
+
+## Model Description
+
+This is a pre-trained ResNet-50 model fine-tuned on the Central Asian Food Dataset. The model is used for image classification across multiple classes. The data was split into training, validation, and test sets. The model was trained using gradient descent with an SGD optimizer and CrossEntropyLoss as the loss function.
+
+## Training Parameters
+
+- **Epochs:** 25
+- **Batch Size:** 32
+- **Learning Rate:** 0.001
+- **Optimizer:** SGD with momentum of 0.9
+- **Loss Function:** CrossEntropyLoss
+
+## Results
+
+### Training and Validation, F1
+
+| Stage        | Loss (train) | Accuracy (train) | Loss (val) | Accuracy (val) |
+|--------------|--------------|------------------|------------|----------------|
+| Epoch 1      | 2.1171       | 47.00%           | 0.8727     | 75.00%         |
+| Epoch 2      | 1.0462       | 69.00%           | 0.6721     | 78.00%         |
+| ...          | ...          | ...              | ...        | ...            |
+| Epoch 25     | 0.4286       | 86.00%           | 0.4349     | 86.00%         |
+
+**Model was trained on two T4 GPUs in a Kaggle notebook trained 36m 7s**  
+**Best validation accuracy:** 86,54%
+```
+                      precision    recall  f1-score   support
+
+            achichuk       0.91      0.98      0.94        41
+         airan-katyk       0.84      0.93      0.89        46
+                asip       0.78      0.57      0.66        37
+            bauyrsak       0.90      0.90      0.90        62
+   beshbarmak-w-kazy       0.71      0.84      0.77        44
+  beshbarmak-wo-kazy       0.86      0.69      0.76        61
+           chak-chak       0.94      0.94      0.94        93
+            cheburek       0.92      0.88      0.90        94
+        doner-lavash       0.77      1.00      0.87        20
+           doner-nan       0.86      0.82      0.84        22
+             hvorost       0.98      0.86      0.91       141
+            irimshik       0.96      0.94      0.95       175
+         kattama-nan       0.84      0.88      0.86        66
+          kazy-karta       0.72      0.78      0.75        46
+                kurt       0.86      0.97      0.91        61
+             kuyrdak       0.92      0.93      0.92        58
+       kymyz-kymyran       0.93      0.82      0.87        49
+        lagman-fried       0.86      0.95      0.90        38
+       lagman-w-soup       0.90      0.80      0.85        75
+      lagman-wo-soup       0.58      0.86      0.69        22
+               manty       0.91      0.95      0.93        63
+               naryn       0.97      0.99      0.98        84
+        nauryz-kozhe       0.88      0.96      0.92        52
+               orama       0.68      0.84      0.75        38
+                plov       0.95      0.98      0.97       101
+               samsa       0.91      0.93      0.92       106
+    shashlyk-chicken       0.68      0.65      0.66        62
+  shashlyk-chicken-v       0.74      0.76      0.75        33
+   shashlyk-kuskovoi       0.75      0.75      0.75        71
+ shashlyk-kuskovoi-v       0.53      0.79      0.64        29
+shashlyk-minced-meat       0.74      0.69      0.72        42
+          sheep-head       0.75      0.94      0.83        16
+             shelpek       0.77      0.86      0.81        64
+              shorpa       0.95      0.88      0.91        80
+          soup-plain       0.96      0.94      0.95        71
+              sushki       0.83      1.00      0.91        43
+               suzbe       0.89      0.82      0.86        62
+            taba-nan       0.92      0.80      0.86       136
+        talkan-zhent       0.86      0.80      0.83        90
+      tushpara-fried       0.79      0.74      0.76        46
+     tushpara-w-soup       0.94      0.94      0.94        67
+    tushpara-wo-soup       0.92      0.87      0.89        91
+
+            accuracy                           0.87      2698
+           macro avg       0.84      0.86      0.85      2698
+        weighted avg       0.88      0.87      0.87      2698
+```
+
+![confusion matrix](matrix.png)
+
+### Testing
+
+After training, the model was tested on the test set:
+
+- **Test accuracy:** 87%
+
+## Repository Structure
+
+- `main.py` — Code for training and testing the model
+- `model/` — Saved model in SafeTensors format
+
+## Usage Instructions
+
+from transformers import AutoModelForImageClassification
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+
+repo_id = "Eraly-ml/centraasia-ResNet-50"
+filename = "model.safetensors"
+
+# Load model
+```
+model_path = hf_hub_download(repo_id=repo_id, filename=filename)
+model = AutoModelForImageClassification.from_pretrained(repo_id)
+model.load_state_dict(load_file(model_path))
+```
+My telegram @eralyf