VIT-cctv-crime-detection

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: crime_cctv_image_detection
+  results: []
+---
+
+<!-- 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. -->
+
+# crime_cctv_image_detection
+
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0332
+- Accuracy: 0.9957
+- Precision: 0.9954
+- Recall: 0.9954
+- F1: 0.9954
+
+## 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-06
+- train_batch_size: 64
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 50
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.7736        | 1.0   | 4608  | 0.1649          | 0.9874   | 0.9857    | 0.9827 | 0.9842 |
+| 0.0836        | 2.0   | 9216  | 0.0487          | 0.9951   | 0.9948    | 0.9948 | 0.9948 |
+| 0.0337        | 3.0   | 13824 | 0.0332          | 0.9957   | 0.9954    | 0.9954 | 0.9954 |
+
+
+### Framework versions
+
+- Transformers 4.53.3
+- Pytorch 2.6.0+cu124
+- Datasets 4.1.1
+- Tokenizers 0.21.2

config.json

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+{
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Abuse",
+    "1": "Arrest",
+    "2": "Arson",
+    "3": "Assault",
+    "4": "Burglary",
+    "5": "Explosion",
+    "6": "Fighting",
+    "7": "Normal",
+    "8": "RoadAccidents",
+    "9": "Robbery",
+    "10": "Shooting",
+    "11": "Shoplifting",
+    "12": "Stealing",
+    "13": "Vandalism"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Abuse": 0,
+    "Arrest": 1,
+    "Arson": 2,
+    "Assault": 3,
+    "Burglary": 4,
+    "Explosion": 5,
+    "Fighting": 6,
+    "Normal": 7,
+    "RoadAccidents": 8,
+    "Robbery": 9,
+    "Shooting": 10,
+    "Shoplifting": 11,
+    "Stealing": 12,
+    "Vandalism": 13
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "pooler_act": "tanh",
+  "pooler_output_size": 768,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.3"
+}

model.safetensors

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+size 343260888

preprocessor_config.json

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+{
+  "do_convert_rgb": null,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "ViTImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

training_args.bin

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+size 5304