SmartFarm_ResNet50 files

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: resnet-50-finetuned-eurosat
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: validation
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9641475073981334
+---
+
+<!-- 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. -->
+
+# resnet-50-finetuned-eurosat
+
+This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1382
+- Accuracy: 0.9641
+
+## 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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.2976        | 1.0   | 549  | 0.1450          | 0.9636   |
+| 0.3388        | 2.0   | 1098 | 0.1382          | 0.9641   |
+| 0.361         | 3.0   | 1647 | 0.1432          | 0.9632   |
+| 0.3163        | 4.0   | 2197 | 0.1412          | 0.9640   |
+| 0.3103        | 5.0   | 2745 | 0.1391          | 0.9639   |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 2.0.0+cu118
+- Datasets 2.12.0
+- Tokenizers 0.13.3

config.json

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+{
+  "_name_or_path": "microsoft/resnet-50",
+  "architectures": [
+    "ResNetForImageClassification"
+  ],
+  "depths": [
+    3,
+    4,
+    6,
+    3
+  ],
+  "downsample_in_first_stage": false,
+  "embedding_size": 64,
+  "hidden_act": "relu",
+  "hidden_sizes": [
+    256,
+    512,
+    1024,
+    2048
+  ],
+  "id2label": {
+    "0": "Apple___Apple_scab",
+    "1": "Apple___Black_rot",
+    "2": "Apple___Cedar_apple_rust",
+    "3": "Apple___healthy",
+    "4": "Blueberry___healthy",
+    "5": "Cherry_(including_sour)___Powdery_mildew",
+    "6": "Cherry_(including_sour)___healthy",
+    "7": "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot",
+    "8": "Corn_(maize)___Common_rust_",
+    "9": "Corn_(maize)___Northern_Leaf_Blight",
+    "10": "Corn_(maize)___healthy",
+    "11": "Grape___Black_rot",
+    "12": "Grape___Esca_(Black_Measles)",
+    "13": "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)",
+    "14": "Grape___healthy",
+    "15": "Orange___Haunglongbing_(Citrus_greening)",
+    "16": "Peach___Bacterial_spot",
+    "17": "Peach___healthy",
+    "18": "Pepper,_bell___Bacterial_spot",
+    "19": "Pepper,_bell___healthy",
+    "20": "Potato___Early_blight",
+    "21": "Potato___Late_blight",
+    "22": "Potato___healthy",
+    "23": "Raspberry___healthy",
+    "24": "Soybean___healthy",
+    "25": "Squash___Powdery_mildew",
+    "26": "Strawberry___Leaf_scorch",
+    "27": "Strawberry___healthy",
+    "28": "Tomato___Bacterial_spot",
+    "29": "Tomato___Early_blight",
+    "30": "Tomato___Late_blight",
+    "31": "Tomato___Leaf_Mold",
+    "32": "Tomato___Septoria_leaf_spot",
+    "33": "Tomato___Spider_mites Two-spotted_spider_mite",
+    "34": "Tomato___Target_Spot",
+    "35": "Tomato___Tomato_Yellow_Leaf_Curl_Virus",
+    "36": "Tomato___Tomato_mosaic_virus",
+    "37": "Tomato___healthy"
+  },
+  "label2id": {
+    "Apple___Apple_scab": 0,
+    "Apple___Black_rot": 1,
+    "Apple___Cedar_apple_rust": 2,
+    "Apple___healthy": 3,
+    "Blueberry___healthy": 4,
+    "Cherry_(including_sour)___Powdery_mildew": 5,
+    "Cherry_(including_sour)___healthy": 6,
+    "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot": 7,
+    "Corn_(maize)___Common_rust_": 8,
+    "Corn_(maize)___Northern_Leaf_Blight": 9,
+    "Corn_(maize)___healthy": 10,
+    "Grape___Black_rot": 11,
+    "Grape___Esca_(Black_Measles)": 12,
+    "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)": 13,
+    "Grape___healthy": 14,
+    "Orange___Haunglongbing_(Citrus_greening)": 15,
+    "Peach___Bacterial_spot": 16,
+    "Peach___healthy": 17,
+    "Pepper,_bell___Bacterial_spot": 18,
+    "Pepper,_bell___healthy": 19,
+    "Potato___Early_blight": 20,
+    "Potato___Late_blight": 21,
+    "Potato___healthy": 22,
+    "Raspberry___healthy": 23,
+    "Soybean___healthy": 24,
+    "Squash___Powdery_mildew": 25,
+    "Strawberry___Leaf_scorch": 26,
+    "Strawberry___healthy": 27,
+    "Tomato___Bacterial_spot": 28,
+    "Tomato___Early_blight": 29,
+    "Tomato___Late_blight": 30,
+    "Tomato___Leaf_Mold": 31,
+    "Tomato___Septoria_leaf_spot": 32,
+    "Tomato___Spider_mites Two-spotted_spider_mite": 33,
+    "Tomato___Target_Spot": 34,
+    "Tomato___Tomato_Yellow_Leaf_Curl_Virus": 35,
+    "Tomato___Tomato_mosaic_virus": 36,
+    "Tomato___healthy": 37
+  },
+  "layer_type": "bottleneck",
+  "model_type": "resnet",
+  "num_channels": 3,
+  "out_features": [
+    "stage4"
+  ],
+  "out_indices": [
+    4
+  ],
+  "problem_type": "single_label_classification",
+  "stage_names": [
+    "stem",
+    "stage1",
+    "stage2",
+    "stage3",
+    "stage4"
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.28.1"
+}

preprocessor_config.json

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

pytorch_model.bin

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